Making high-stakes personal decisions involves cognitive, emotional, and intuitive processes, and individuals differ in how they allocate attention across these modes. Integration of these processes has shown to benefit decision making. Yet, most current decision-support systems focus primarily on supporting cognitive aspects, rather than adapting to the individual’s thinking profile to support integration of different types of thoughts. In this study, we investigate an agent designed to encourage integration by adapting to the individual user’s thought patterns. We explore its effects on participants’ perceptions of the agent and their reflective behavior, in comparison with unaided pre-reflection and a baseline agent. In a between-subjects study (N = 128), our agent, which fostered broad and elaborated thinking, enabled more personalized reflective trajectories, elicited more integrative reflective language, and was perceived as providing stronger support for holistic reflection. In contrast, the baseline agent produced homogenized profiles dominated by cognitive language across participants.

Hybrid Intelligence (HI) is an emerging paradigm in which artificial intelligence (AI) augments human intelligence. The current literature lacks systematic models that guide the design and evaluation of HI systems. Further, discussions around HI primarily focus on technology, neglecting the holistic human-AI ensemble. In this paper, we take the initial steps toward the development of a quality model for characterizing and evaluating HI systems from a human-AI teams perspective. We first conducted a study investigating the adequacy of properties commonly associated with effective human teams to describe HI. The study features the insights of 50 HI researchers, and shows that various human team properties, including boundedness, interdependence, competency, purposefulness, initiative, normativity, and effectiveness, are important for HI systems. Based on these results, we developed a quality model for HI teams composed of seven high-level quality attributes, further refined into 16 specific ones. To evaluate the relevance and understanding of the proposed attributes, we conducted a second empirical investigation by staging competitions in which participants used the quality model to develop and analyze HI usage scenarios. Our analysis of 48 collected scenarios, which we openly release, confirms the proposed attributes' relevance and highlights insights that emerge when designers consider the quality model in HI system design.

Providing citizens with transparent, efficient, and consistent legal advice is a critical challenge to uphold governmental fairness in societies.However, this remains a tall order for already overburdened organizations.This paper proposes an interactive learning system that seeks to address these challenges through an organizational learning lens, in collaboration with the Legal Desk in the Netherlands.This system, called SHARE, serves the dual purpose of supporting legal consultations and enhancing the training of legal advisors.SHARE captures and stores strategic decision-making knowledge from experienced legal advisors and facilitates its transfer to less experienced advisors.We explain in this case study the system's approach, prototype design, and evaluation through exploratory user tests.Our findings suggest that this prototype has the potential to contribute towards improved knowledge management for conducting legal consultations within the Legal Desk.To conclude, we discuss the outlook and broader implications of this approach in knowledge-driven work environments.

Mutual trust between humans and interactive artificial agents is crucial for effective human-agent teamwork.This involves not only the human appropriately trusting the artificial teammate, but also the artificial teammate assessing the human's trustworthiness for different tasks (i.e., artificial trust in human partners).Literature indicated that transparency and explainability is generally beneficial for human-agent collaboration.However, communicating artificial trust potentially affects human trust and satisfaction, which impact team dynamics.Towards studying these effects, we developed an artificial trust model and implemented five distinct communication approaches which varied in modality (visual/graphical and/or text), level (communication and/or explanation), and timing (real-time or occasional).We evaluated the effects of the different communication styles through a user study (N=120) in a 2D grid-world Search and Rescue scenario.Our results show that all our artificial trust explanations improved human trust and satisfaction, but the mere graphical communication of it did not.These results are bound to the specific scenario and context in which this study was run and require further exploration.As such, this work presents a first step towards understanding the consequences of communicating and explaining to a human teammate their assessed trustworthiness.

In today's society, where Artificial Intelligence (AI) has gained a vital role, concerns regarding user's trust have garnered significant attention.The use of AI systems in high-risk domains have often led users to either under-trust it, potentially causing inadequate reliance or over-trust it, resulting in over-compliance.Therefore, users must maintain an appropriate level of trust.Past research has indicated that explanations provided by AI systems can enhance user understanding of when to trust or not trust the system.However, the utility of presentation of different explanations forms still remains to be explored especially in high-risk domains.Therefore, this study explores the impact of different explanation types (text, visual, and hybrid) and user expertise (retired police officers and lay users) on establishing appropriate trust in AI-based predictive policing.While we observed that the hybrid form of explanations increased the subjective trust in AI for expert users, it did not led to better decision-making.Furthermore, no form of explanations helped build appropriate trust.The findings of our study emphasize the importance of re-evaluating the use of explanations to build [appropriate] trust in AI based systems especially when the system's use is questionable.Finally, we synthesize potential challenges and policy recommendations based on our results to design for appropriate trust in high-risk based AI-based systems.

Understanding citizens’ values in participatory systems is crucial for citizen-centric policy-making. We envision a hybrid participatory system where participants make choices and provide motivations for those choices, and AI agents estimate their value preferences by interacting with them. We focus on situations where a conflict is detected between participants’ choices and motivations, and propose methods for estimating value preferences while addressing detected inconsistencies by interacting with the participants. We operationalize the philosophical stance that “valuing is deliberatively consequential.” That is, if a participant’s choice is based on a deliberation of value preferences, the value preferences can be observed in the motivation the participant provides for the choice. Thus, we propose and compare value preferences estimation methods that prioritize the values estimated from motivations over the values estimated from choices alone. Then, we introduce a disambiguation strategy that combines Natural Language Processing and Active Learning to address the detected inconsistencies between choices and motivations. We evaluate the proposed methods on a dataset of a large-scale survey on energy transition. The results show that explicitly addressing inconsistencies between choices and motivations improves the estimation of an individual’s value preferences. The disambiguation strategy does not show substantial improvements when compared to similar baselines—however, we discuss how the novelty of the approach can open new research avenues and propose improvements to address the current limitations.

Michiel van der Meer, Piek Vossen, Catholijn M. Jonker, Pradeep K. Murukannaiah. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Understanding behavior of human drivers in interactions with automated vehicles (AV) can aid the development of future AVs. Existing investigations of such behavior have predominantly focused on situations in which an AV a priori needs to take action because the human has the right of way. However, future AVs might need to proactively manage interactions even if they have the right of way over humans, e.g., a human driver taking a left turn in front of the approaching AV. Yet it remains unclear how AVs could behave in such interactions and how humans would react to them. To address this issue, here we investigated behavior of human drivers (N=19) when interacting with an oncoming AV during unprotected left turns in a driving simulator experiment. We measured the outcomes (Go or Stay) and timing of participants’ decisions when interacting with an AV which performed subtle longitudinal nudging maneuvers, e.g. briefly decelerating and then accelerating back to its original speed. We found that participants’ behavior was sensitive to deceleration nudges but not acceleration nudges. We compared the obtained data to predictions of several variants of a drift-diffusion model of human decision making. The most parsimonious model that captured the data hypothesized noisy integration of dynamic information on time-to-arrival and distance to a fixed decision boundary, with an initial accumulation bias towards the Go decision. Our model not only accounts for the observed behavior but can also flexibly generate predictions of human responses to arbitrary longitudinal AV maneuvers, and can be used for both informing future studies of human behavior and incorporating insights from such studies into computational frameworks for AV interaction planning.

Large-scale survey tools enable the collection of citizen feedback in opinion corpora. Extracting the key arguments from a large and noisy set of opinions helps in understanding the opinions quickly and accurately. Fully automated methods can extract arguments but (1) require large labeled datasets that induce large annotation costs and (2) work well for known viewpoints, but not for novel points of view. We propose HyEnA, a hybrid (human + AI) method for extracting arguments from opinionated texts, combining the speed of automated processing with the understanding and reasoning capabilities of humans. We evaluate HyEnA on three citizen feedback corpora. We find that, on the one hand, HyEnA achieves higher coverage and precision than a state-of-the-art automated method when compared to a common set of diverse opinions, justifying the need for human insight. On the other hand, HyEnA requires less human effort and does not compromise quality compared to (fully manual) expert analysis, demonstrating the benefit of combining human and artificial intelligence.

We adopt an emerging and prominent vision of human-centred Artificial Intelligence that requires building trustworthy intelligent systems. Such systems should be capable of dealing with the challenges of an interconnected, globalised world by handling plurality and by abiding by human values. Within this vision, pluralistic value alignment is a core problem for AI– that is, the challenge of creating AI systems that align with a set of diverse individual value systems. So far, most literature on value alignment has considered alignment to a single value system. To address this research gap, we propose a novel method for estimating and aggregating multiple individual value systems. We rely on recent results in the social choice literature and formalise the value system aggregation problem as an optimisation problem. We then cast this problem as an ℓp-regression problem. Doing so provides a principled and general theoretical framework to model and solve the aggregation problem. Our aggregation method allows us to consider a range of ethical principles, from utilitarian (maximum utility) to egalitarian (maximum fairness). We illustrate the aggregation of value systems by considering real-world data from two case studies: the Participatory Value Evaluation process and the European Values Study. Our experimental evaluation shows how different consensus value systems can be obtained depending on the ethical principle of choice, leading to practical insights for a decision-maker on how to perform value system aggregation.

In teams composed of humans, we use trust in others to make decisions, such as what to do next, who to help and who to ask for help. When a team member is artificial, they should also be able to assess whether a human teammate is trustworthy for a certain task. We see trustworthiness as the combination of (1) whether someone will do a task and (2) whether they can do it. With building beliefs in trustworthiness as an ultimate goal, we explore which internal factors (krypta) of the human may play a role (e.g., ability, benevolence, and integrity) in determining trustworthiness, according to existing literature. Furthermore, we investigate which observable metrics (manifesta) an agent may take into account as cues for the human teammate’s krypta in an online 2D grid-world experiment ( n = 54). Results suggest that cues of ability, benevolence and integrity influence trustworthiness. However, we observed that trustworthiness is mainly influenced by human’s playing strategy and cost-benefit analysis, which deserves further investigation. This is a first step towards building informed beliefs of human trustworthiness in human-AI teamwork.

Appropriate trust is an important component of the interaction between people and AI systems, in that “inappropriate” trust can cause disuse, misuse, or abuse of AI. To foster appropriate trust in AI, we need to understand how AI systems can elicit appropriate levels of trust from their users. Out of the aspects that influence trust, this article focuses on the effect of showing integrity. In particular, this article presents a study of how different integrity-based explanations made by an AI agent affect the appropriateness of trust of a human in that agent. To explore this, (1) we provide a formal definition to measure appropriate trust, (2) present a between-subject user study with 160 participants who collaborated with an AI agent in such a task. In the study, the AI agent assisted its human partner in estimating calories on a food plate by expressing its integrity through explanations focusing on either honesty, transparency, or fairness. Our results show that (a) an agent who displays its integrity by being explicit about potential biases in data or algorithms achieved appropriate trust more often compared to being honest about capability or transparent about the decision-making process, and (b) subjective trust builds up and recovers better with honesty-like integrity explanations. Our results contribute to the design of agent-based AI systems that guide humans to appropriately trust them, a formal method to measure appropriate trust, and how to support humans in calibrating their trust in AI.

Enrico Liscio, Oscar Araque, Lorenzo Gatti, Ionut Constantinescu, Catholijn Jonker, Kyriaki Kalimeri, Pradeep Kumar Murukannaiah. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Disagreements are common in online discussions.Disagreement may foster collaboration and improve the quality of a discussion under some conditions.Although there exist methods for recognizing disagreement, a deeper understanding of factors that influence disagreement is lacking in the literature.We investigate a hypothesis that differences in personal values are indicative of disagreement in online discussions.We show how state-of-the-art models can be used for estimating values in online discussions and how the estimated values can be aggregated into value profiles.We evaluate the estimated value profiles based on human-annotated agreement labels.We find that the dissimilarity of value profiles correlates with disagreement in specific cases.We also find that including value information in agreement prediction improves performance.

Abstract Support agents that help users in their daily lives need to take into account not only the user’s characteristics, but also the social situation of the user. Existing work on including social context uses some type of situation cue as an input to information processing techniques in order to assess the expected behavior of the user. However, research shows that it is important to also determine the meaning of a situation, a step which we refer to as social situation comprehension. We propose using psychological characteristics of situations, which have been proposed in social science for ascribing meaning to situations, as the basis for social situation comprehension. Using data from user studies, we evaluate this proposal from two perspectives. First, from a technical perspective, we show that psychological characteristics of situations can be used as input to predict the priority of social situations, and that psychological characteristics of situations can be predicted from the features of a social situation. Second, we investigate the role of the comprehension step in human–machine meaning making. We show that psychological characteristics can be successfully used as a basis for explanations given to users about the decisions of an agenda management personal assistant agent.

Mutual trust is considered a required coordinating mechanism for achieving effective teamwork in human teams. However, it is still a challenge to implement such mechanisms in teams composed by both humans and AI (human-AI teams), even though those are becoming increasingly prevalent. Agents in such teams should not only be trustworthy and promote appropriate trust from the humans, but also know when to trust a human teammate to perform a certain task. In this project, we study trust as a tool for artificial agents to achieve better team work. In particular, we want to build mental models of humans so that agents can understand human trustworthiness in the context of human-AI teamwork, taking into account factors such as human teammates', task's and environment's characteristics.

This interdisciplinary workshop aims to break boundaries between the researchers who develop human models (e.g., from the fields of human factors, cognitive psychology, and computational neuroscience) and roboticists who use human models in different human-robot interaction (HRI) contexts. The keynote talks, contributed submissions, and interactive discussions will focus on the questions such as: How can modeling humans help us understand and design human-robot interactions? What kinds of models are useful for which HRI contexts (physical/cognitive interactions) and purposes (behavior prediction/personalization/theory -of- mind/etc.)? What common lessons can be learned from human behavior modeling in HRI across different application domains? How can modeling humans in HRI tasks help us to better understand human cognition/behavior? By stimulating an interdisciplinary conver-sation around these questions, we aim to raise awareness of the benefits of modeling and expose the wider HRI community to a variety of different modeling approaches, and facilitate the HRI researchers who already engage in modeling to exchange views on methodology of modeling and best nractices from diverse fields.

When working in a group, it is essential to understand each other’s viewpoints to increase group cohesion and meeting productivity. This can be challenging in teams: participants might be left misunderstood and the discussion could be going around in circles. To tackle this problem, previous research on group interactions has addressed topics such as dominance detection, group engagement, and group creativity. Conversational memory, however, remains a widely unexplored area in the field of multimodal analysis of group interaction. The ability to track what each participant or a group as a whole find memorable from each meeting would allow a system or agent to continuously optimise its strategy to help a team meet its goals. In the present paper, we therefore investigate what participants take away from each meeting and how it is reflected in group dynamics.As a first step toward such a system, we recorded a multimodal longitudinal meeting corpus (MEMO), which comprises a first-party annotation of what participants remember from a discussion and why they remember it. We investigated whether participants of group interactions encode what they remember non-verbally and whether we can use such non-verbal multimodal features to predict what groups are likely to remember automatically. We devise a coding scheme to cluster participants’ memorisation reasons into higher-level constructs. We find that low-level multimodal cues, such as gaze and speaker activity, can predict conversational memorability. We also find that non-verbal signals can indicate when a memorable moment starts and ends. We could predict four levels of conversational memorability with an average accuracy of 44 %. We also showed that reasons related to participants’ personal feelings and experiences are the most frequently mentioned grounds for remembering meeting segments.

This study investigates whether an agent-based Negotiation Training System (NTS) can teach women Strategic Empathy - a recently introduced negotiation strategy based on perspective taking - and whether this can improve their negotiation performance. Developed and tested through an interaction-based real-time experiment was a NTS that integrated instructions on how to utilize Strategic Empathy. Women in the experimental group showed significantly higher levels of perspective-taking compared to the control group, and their understanding and use of Strategic Empathy increased over time. Also, a significant positive effect was found of Strategic Empathy on women's self-efficacy. No significant positive effect was found of Strategic Empathy on persistence. The high cognitive load of the experiment and a lack of intrinsic motivation may have caused this finding. Overall, this work demonstrates the applicability of using NTS to teach Strategic Empathy, and its effectiveness for enhancing women's self-efficacy in salary negotiations.

As intelligent agents are becoming human's teammates, not only do humans need to trust intelligent agents, but an intelligent agent should also be able to form artificial trust, i.e. a belief regarding human's trustworthiness. We see artificial trust as the beliefs of competence and willingness, and we study which internal factors (krypta) of the human may play a role when assessing artificial trust. Furthermore, we investigate which observable measures (manifesta) an agent may take into account as cues for the human teammate's krypta. This paper proposes a conceptual model of artificial trust for a specific task during human-agent teamwork. Our model proposes observable measures related to human trustworthiness (ability, benevolence, integrity) and strategy (perceived cost and benefit) as predictors for willingness and competence, based on literature and a preliminary user study.

The pursuit of values drives human behavior and promotes cooperation. Existing research is focused on general values (e.g., Schwartz) that transcend contexts. However, context-specific values are necessary to (1) understand human decisions, and (2) engineer intelligent agents that can elicit and align with human values. We propose Axies, a hybrid (human and AI) methodology to identify context-specific values. Axies simplifies the abstract task of value identification as a guided value annotation process involving human annotators. Axies exploits the growing availability of value-laden text corpora and Natural Language Processing to assist the annotators in systematically identifying context-specific values. We evaluate Axies in a user study involving 80 human subjects. In our study, six annotators generate value lists for two timely and important contexts: Covid-19 measures and sustainable Energy. We employ two policy experts and 72 crowd workers to evaluate Axies value lists and compare them to a list of general (Schwartz) values. We find that Axies yields values that are (1) more context-specific than general values, (2) more suitable for value annotation than general values, and (3) independent of the people applying the methodology. Supplementary Information: The online version contains supplementary material available at 10.1007/s10458-022-09550-0.

Disabled people can benefit greatly from assistive digital technologies. However, this increased human-machine symbiosis makes it important that systems are personalized and transparent to users. Existing work often uses data-oriented approaches. However, these approaches lack transparency and make it hard to influence the system’s behavior. In this paper, we use knowledge-based techniques for personalization, introducing the concept of Semantic User Models for representing the behavior, values and capabilities of users. To allow the system to construct such a user model, we investigate the use of a conversational agent which can elicit the relevant information from users through dialogue. A conversational interface is essential for our case study of navigation support for visually impaired people, but in general, has the potential to enhance transparency as users know what the system represents about them. For such a dialogue to be effective, it is crucial that the user understands what the conversational agent is asking, i.e., that misalignments that decrease the transparency are avoided or resolved. In this paper, we investigate whether we can use a conversational agent for Semantic User Model elicitation, which types of misalignments can occur in this process and how they are related, and how misalignments can be reduced. We investigate this in two (iterative) qualitative studies (n = 7 & n = 8) with visually impaired people in which a personalized user model for navigation support is elicited via a dialogue with a conversational agent. Our results show four hierarchically structured levels of human-agent misalignment. We identify several design solutions for reducing misalignments, which point to the need for restricting the generic user model to what is needed in the domain under consideration. With this research, we lay a foundation for conversational agents capable of eliciting Semantic User Models.

As AI systems are increasingly involved in decision making, it also becomes important that they elicit appropriate levels of trust from their users. To achieve this, it is first important to understand which factors influence trust in AI. We identify that a research gap exists regarding the role of personal values in trust in AI. Therefore, this paper studies how human and agent Value Similarity (VS) influences a human's trust in that agent. To explore this, 89 participants teamed up with five different agents, which were designed with varying levels of value similarity to that of the participants. In a within-subjects, scenario-based experiment, agents gave suggestions on what to do when entering the building to save a hostage. We analyzed the agent's scores on subjective value similarity, trust and qualitative data from open-ended questions. Our results show that agents rated as having more similar values also scored higher on trust, indicating a positive effect between the two. With this result, we add to the existing understanding of human-agent trust by providing insight into the role of value-similarity.

The bargaining power of prosumers in a market can vary significantly. Participants can range from industrial participants to powerful and less powerful citizens. Existing trade mechanisms in such markets, e.g., in rural India’s energy trade market, show occurrences of discrimination, exclusion, and unfairness. We study how discrimination affects market access, efficiency, and demand satisfaction for the discriminating and discriminated groups via an agent-based simulation, incorporating the available real data. We introduce a mechanism for such markets that is designed for the values of inclusion and equal opportunities. The crux of our mechanism is that goods are divided into smaller units, as determined by the market participants’ surplus and demands, and traded anonymously via agents representing the prosumers. We evaluate six hypotheses in a case study about energy trade in rural India, where members of a caste known as Dalits are discriminated by Others. We show that anonymization contributes to the value of inclusion, and the combination of anonymization and inclusion contributes to equal opportunities with respect to market access for both Dalits and Others.

Specifying a normative multiagent system (nMAS) is challenging, because different agents often have conflicting requirements. Whereas existing approaches can resolve clear-cut conflicts, tradeoffs might occur in practice among alternative nMAS specifications with no apparent resolution. To produce an nMAS specification that is acceptable to each agent, we model the specification process as a negotiation over a set of norms. We propose an agent-based negotiation framework, where agents’ requirements are represented as values (e.g., patient safety, privacy, and national security), and an agent revises the nMAS specification to promote its values by executing a set of norm revision rules that incorporate ontology-based reasoning. To demonstrate that our framework supports creating a transparent and accountable nMAS specification, we conduct an experiment with human participants who negotiate against our agent. Our findings show that our negotiation agent reaches better agreements (with small p -value and large effect size) faster than a baseline strategy. Moreover, participants perceive that our agent enables more collaborative and transparent negotiations than the baseline (with small p -value and large effect size in particular settings) toward reaching an agreement.

In the context of human-agent interaction, we see the emergence of computational artefacts that display hybridity because they can be experienced as tools and agents. In this paper we propose a tool-agent spectrum as an analytical lens that uses 'intention' as a central concept. This spectrum aims to clarify how a computational object can change from being conducive to the intentions of others ('tool') to appearing to have intentions of its own ('agent'), or vice versa. We have applied this analytical lens to unravel people's experiences in two hybrid cases; guide dogs as a living mobility aid for the visually impaired and an experimental wearable object named 'BagSight' as a rudimentary artificial counterpart. We compared both cases through the lens of a tool-agent spectrum and elaborate on these results by discussing some of the principles by which computational artefacts can shift across the spectrum. We conclude by discussing the limitations of this study and provide suggestions for future work.

Evidence-driven agent-based modeling plays a useful part in understanding social phenomena. By integrating social-cognitive theories in our agent models, we bear evidence from social and psychological studies on our models for human decision-making. Social practice theory (SPT) provides a socio-cognitive theory that emphasizes three empirically and theoretically grounded aspects of behavior: habituality, sociality, and interconnectivity. Previous work has emphasized the importance of SPT for agents, has made abstract models of SPT, or used SPT to study energy systems. This article provides a set of requirements for integrating SPT in agent models and an evaluation of 11 current agent models with respect to these requirements. We find that current agent models do not fully capture habituality, sociality, or interconnectivity, nor is there a model that aims to integrate all three aspects. For example, current models do not support context-dependent habits, use a comprehensive set of collective concepts, and support hierarchies of activities. Our evaluation allows researchers to pick one of the current agent models depending on their needs regarding habituality, sociality, and interconnectivity. Furthermore, this article shows the usefulness of an agent model that integrates SPT and provides requirements that help modelers to achieve this model.

We present the results of the 2ndAnnual Human-Agent League of the Automated Negotiating Agent Competition. Building on the success of the previous year's results, a new challenge was issued that focused exploring the likeability-success tradeoff in negotiations. By examining a series of repeated negotiations, actions may affect the relationship between automated negotiating agents and their human competitors over time. The results presented herein support a more complex view of human-agent negotiation and capture of integrative potential (win-win solutions). We show that, although likeability is generally seen as a tradeoff to winning, agents are able to remain well-liked while winning if integrative potential is not discovered in a given negotiation. The results indicate that the top-performing agent in this competition took advantage of this loophole by engaging in favor exchange across negotiations (cross-game logrolling). These exploratory results provide information about the effects of different submitted “black-box” agents in human-agent negotiation and provide a state-of-the-art benchmark for human-agent design.

We present the results of the first annual Human-Agent League of ANAC. By introducing a new human-agent negotiating platform to the research community at large, we facilitated new advancements in human-aware agents. This has succeeded in pushing the envelope in agent design, and creating a corpus of useful human-agent interaction data. Our results indicate a variety of agents were submitted, and that their varying strategies had distinct outcomes on many measures of the negotiation. These agents approach the problems endemic to human negotiation, including user modeling, bidding strategy, rapport techniques, and strategic bargaining. Some agents employed advanced tactics in information gathering or emotional displays and gained more points than their opponents, while others were considered more "likeable" by their partners.

Behaviour support technology is aimed at assisting people in organizing their Activities of Daily Living (ADLs). Numerous frameworks have been developed for activity recognition and for generating specific types of support actions, such as reminders. The main goal of our research is to develop a generic formal framework for representing and reasoning about ADLs and their temporal relations. This framework should facilitate modelling and reasoning about 1) durative activities, 2) relations between higher-level activities and subactivities, 3) activity instances, and 4) activity duration. In this paper we present a temporal logic as an extension of the logic TPTL for specification of real-time systems. Our logic TPTL_{bih} is defined over Behaviour Identification Hierarchies (BIHs) for representing ADL structure and typical activity duration. To model execution of ADLs, states of the temporal traces in TPTL_{bih} comprise information about the start, stop and current execution of activities. We provide a number of constraints on these traces that we stipulate are desired for the accurate representation of ADL execution, and investigate corresponding validities in the logic. To evaluate the expressivity of the logic, we give a formal definition for the notion of Coherence for (complex) activities, by which we mean that an activity is done without interruption and in a timely fashion. We show that the definition is satisfiable in our framework. In this way the logic forms the basis for a generic monitoring and reasoning framework for ADLs.

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent’s decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for three research fields. For machine learning (ML) researchers, emotion models may improve learning efficiency. For the interactive ML and human–robot interaction community, emotions can communicate state and enhance user investment. Lastly, it allows affective modelling researchers to investigate their emotion theories in a successful AI agent class. This survey provides background on emotion theory and RL. It systematically addresses (1) from what underlying dimensions (e.g. homeostasis, appraisal) emotions can be derived and how these can be modelled in RL-agents, (2) what types of emotions have been derived from these dimensions, and (3) how these emotions may either influence the learning efficiency of the agent or be useful as social signals. We also systematically compare evaluation criteria, and draw connections to important RL sub-domains like (intrinsic) motivation and model-based RL. In short, this survey provides both a practical overview for engineers wanting to implement emotions in their RL agents, and identifies challenges and directions for future emotion-RL research.

The annual International Automated Negotiating Agents Competition (ANAC) is used by the automated negotiation research community to benchmark and evaluate its work andto challenge itself. The benchmark problems and evaluation results and the protocols and strategies developed are available to the wider research community.

Automatic recognition of negative affect and aggression is key in many safety critical domains such as surveillance and health care. In this paper we explore the potential of overlapping speech for predicting aggression levels. As a first step we consider 3 categories of overlapping speech based on literature. Having an annotation of these overlap categories, we examine whether overlapping speech is a good feature for predicting aggression by using it in classification together with a set of acoustic features typically used for this purpose. Next, we explore if this fine categorization of overlap is necessary in predicting aggression levels or a more coarse representation is sufficient. Finally, we check the additive values of automatically predicted overlapping speech for aggression recognition. The experiments are performed on a dataset of dyadic interactions between professional aggression training actors (actors) and naive participants (students) interacting freely based on short role descriptions. Our findings show that overlapping speech is a key feature for predicting aggression levels, that discriminating only severe cases of overlap is a sufficient feature and that automatically predicted overlap is improving aggression recognition as well.

We present the collection and annotation of a multi-modal database with negative human-human interactions. The work is part of supporting behavior recognition in the context of a virtual reality aggression prevention training system. The data consist of dyadic interactions between professional aggression training actors (actors) and naive participants (students). In addition to audio and video, we have recorded motion capture data with kinect, head tracking, and physiological data: heart rate (ECG), galvanic skin response (GSR) and electromyography (EMG) of biceps, triceps and trapezius muscles. Aggression levels, fear, valence, arousal and dominance have been rated separately for actors and students. We observe higher inter-rater agreement for rating the actors than for rating the students, consistently for each annotated dimension, and a higher inter-rater agreement for speaking behavior than for listening behavior. The data can be used among others for research on affect recognition, multimodal fusion and the relation between different bodily manifestation.

Computers that negotiate on our behalf hold great promise for the future and will even become indispensable in emerging application domains such as the smart grid and the Internet of Things. Much research has thus been expended to create agents that are able to negotiate in an abundance of circumstances. However, up until now, truly autonomous negotiators have rarely been deployed in real-world applications. This paper sizes up current negotiating agents and explores a number of technological, societal and ethical challenges that autonomous negotiation systems have brought about. The questions we address are: in what sense are these systems autonomous, what has been holding back their further proliferation, and is their spread something we should encourage? We relate the automated negotiation research agenda to dimensions of autonomy and distill three major themes that we believe will propel autonomous negotiation forward: accurate representation, long-term perspective, and user trust. We argue these orthogonal research directions need to be aligned and advanced in unison to sustain tangible progress in the field.

For real-time programs reproducing a bug by rerunning the system is likely to fail, making fault localization a time-consuming process. Omniscient debugging is a technique that stores each run in such a way that it supports going backwards in time. However, the overhead of existing omniscient debugging implementations for languages like Java is so large that it cannot be effectively used in practice. In this paper, we show that for agent-oriented programming practical omniscient debugging is possible. We design a tracing mechanism for efficiently storing and exploring agent program runs. We are the first to demonstrate that this mechanism does not affect program runs by empirically establishing that the same tests succeed or fail. Usability is supported by a trace visualization method aimed at more effectively locating faults in agent programs.

The main goal of our demonstration is to show how omniscient debugging can be applied in practice to cognitive agents. A concrete implementation of the mechanisms proposed in Koeman et. al [2017] has been created for the GOAL agent programming language in the Eclipse environment, integrated with the source-level debugger of Koeman et. al [2016], thus fully implementing the proposal within a state-of-the-art setting. The implementation will be used together with typical agent programs to demonstrate its practical use.

When an agent program exhibits unexpected behaviour, a developer needs to locate the fault by debugging the agent’s source code. The process of fault localisation requires an understanding of how code relates to the observed agent behaviour. The main aim of this paper is to design a source-level debugger that supports single-step execution of a cognitive agent program. Cognitive agents execute a decision cycle in which they process events and derive a choice of action from their beliefs and goals. Current state-of-the-art debuggers for agent programs provide insight in how agent behaviour originates from this cycle but less so in how it relates to the program code. As relating source code to generated behaviour is an important part of the debugging task, arguably, a developer also needs to be able to suspend an agent program on code locations. We propose a design approach for single-step execution of agent programs that supports both code-based as well as cycle-based suspension of an agent program. This approach results in a concrete stepping diagram ready for implementation and is illustrated by a diagram for both the Goal and Jason agent programming languages, and a corresponding full implementation of a source-level debugger for Goal in the Eclipse development environment. The evaluation that was performed based on this implementation shows that agent programmers prefer a source-level debugger over a purely cycle-based debugger.

Institutions regulate societies. Comprising Searle's constitutive counts-as rules, "A counts-as B in context C", an institution ascribes from brute and institutional facts (As), a social reality comprising institutional facts (Bs) conditional on the social reality (contexts Cs). When brute facts change an institution evolves from one social reality to the next. Rule changes are also regulated by rule-modifying counts-as rules ascribing rule change in the past/present/future (e.g. a majority rule change vote counts-as a rule change). Determining rule change legality is difficult, since changing counts-as rules both alters and is conditional on the social reality, and in some cases hypothetical rule-change effects (e.g. not retroactively criminalising people). However, without a rigorous account of rule change ascriptions, AI agents cannot support humans in understanding the laws imposed on them. Moreover, advances in automated governance design for socio-technical systems, are limited by agents' ability to understand how and when to enact institutional changes. Consequently, we answer "when do rule changes count-as legal rule changes?" in a temporal setting with a novel formal framework.

People often have reciprocal habits, almost automatically responding to others' actions. A robot who interacts with humans may also reciprocate, in order to come across natural and be predictable. We aim to facilitate decision support that advises on utility-efficient habits in these interactions. To this end, given a model for reciprocation behavior with parameters that represent habits, we define a game that describes what habit one should adopt to increase the utility of the process. This paper concentrates on two agents. The used model defines that an agent's action is a weighted combination of the other's previous actions (reacting) and either i) her innate kindness, or ii) her own previous action (inertia). In order to analyze what happens when everyone reciprocates rationally, we define a game where an agent may choose her habit, which is either her reciprocation attitude (i or ii), or both her reciprocation attitude and weight. We characterize the Nash equilibria of these games and consider their efficiency. We find that the less kind agents should adjust to the kinder agents to improve both their own utility as well as the social welfare. This constitutes advice on improving cooperation and explains real life phenomena in human interaction, such as the societal benefits from adopting the behavior of the kindest person, or becoming more polite as one grows up.

A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent’s preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other’s wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy.

Recent years have witnessed a growing interest in recognizing emotions and events based on speech. One of the applications of such systems is automatically detecting when a situations gets out of hand and human intervention is needed. Most studies have focused on increasing recognition accuracies using parts of the same dataset for training and testing. However, this says little about how such a trained system is expected to perform `in the wild'. In this paper we present a cross-corpus study using the audio part of three multimodal datasets containing negative human-human interactions. We present intra- and cross-corpus accuracies whilst manipulating the acoustic features, normalization schemes, and oversampling of the least represented class to alleviate the negative effects of data unbalance. We observe a decrease in performance when disjunct corpora are used for training and testing. Merging two datasets for training results in a slightly lower performance than the best one obtained by using only one corpus for training. A hand crafted low dimensional feature set shows competitive behavior when compared to a brute force high dimensional features vector. Corpus normalization and artificially creating samples of the sparsest class have a positive effect.

In this paper we computationally study the relation between adaptive behaviour and emotion. Using the reinforcement learning framework, we propose that learned state utility, V(s), models fear (negative) and hope (positive) based on the fact that both signals are about anticipation of loss or gain. Further, we propose that joy/distress is a signal similar to the error signal. We present agent-based simulation experiments that show that this model replicates psychological and behavioural dynamics of emotion. This work distinguishes itself by assessing the dynamics of emotion in an adaptive agent framework – coupling it to the literature on habituation, development, extinction and hope theory. Our results support the idea that the function of emotion is to provide a complex feedback signal for an organism to adapt its behaviour. Our work is relevant for understanding the relation between emotion and adaptation in animals, as well as for human–robot interaction, in particular how emotional signals can be used to communicate between adaptive agents and humans.

Given the growing interest in automated negotiation, the search for effective strategies has produced a variety of different negotiation agents. Despite their diversity, there is a common structure to their design. A negotiation agent comprises three key components: the bidding strategy, the opponent model and the acceptance criteria. We show that this three-component view of a negotiating architecture not only provides a useful basis for developing such agents but also provides a useful analytical tool. By combining these components in varying ways, we are able to demonstrate the contribution of each component to the overall negotiation result, and thus determine the key contributing components. Moreover, we study the interaction between components and present detailed interaction effects. Furthermore, we find that the bidding strategy in particular is of critical importance to the negotiator's success and far exceeds the importance of opponent preference modeling techniques. Our results contribute to the shaping of a research agenda for negotiating agent design by providing guidelines on how agent developers can spend their time most effectively.

Every driver behaves differently in traffic. However, when it comes to micro-simulation of drivers with a high level of detail no framework manages to model the complexities of various driving styles as well as scale up to larger simulations. We propose a framework of micro-simulation combined with cognitive agents to facilitate such simulation tasks. Our goal is to (i) model individual drivers, and (ii) use this framework for the purpose of simulating realistic highway traffic with heterogeneous driving styles. The challenge is therefore to create a framework that facilitates such complex modeling and supports large scale simulations. We evaluate the framework from two perspectives. First, the ability to represent, model and simulate dissimilar drivers in addition to study and compare emerging behavior. Second, the scalability of the framework. We report on our experiences with the framework, outline several challenges and identify future areas for development.

We investigate the role of communication in the coordination of cooperative robot teams and its impact on performance in search and retrieval tasks. We first discuss a baseline without communication and analyse various kinds of coordination strategies for exploration and exploitation. We then discuss how the robots construct a shared mental model by communicating beliefs and/or goals with one another, as well as the coordination protocols with regard to subtask allocation and destination selection. Moreover, we also study the influence of various factors on performance including the size of robot teams, the size of the environment that needs to be explored and ordering constraints on the team goal. We use the Blocks World for Teams as an abstract testbed for simulating such tasks, where the team goal of the robots is to search and retrieve a number of target blocks in an initially unknown environment. In our experiments we have studied two main variations: a variant where all blocks to be retrieved have the same color (no ordering constraints on the team goal) and a variant where blocks of various colors need to be retrieved in a particular order (with ordering constraints). Our findings show that communication increases performance but significantly more so for the second variant and that exchanging more messages does not always yield a better team performance.

Conflict is an omnipresent phenomenon in human society. It spans from individual decision-making trade-offs such as deciding what to do next (sleep, eat, work, play), to complex scenarios including politics and business. The social sciences, psychology, economy, and biology study the nature of conflict, its consequences, and strategies to successfully deal with it. Over the last decades computer science has joined those disciplines and studies conflict from a computational perspective. This special issue presents a selection of the best papers presented at the First Workshop of Conflict Resolution in Decision Making (COREDEMA). The workshop focused on computational approaches that tackle conflict in order to provide new insights and explore potential applications. The workshop was jointly hosted with the 12th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS) in Salamanca, Spain, from June 4 to 6, 2013.

The design of automated negotiators has been the focus of abundant research in recent years. However, due to difficulties involved in creating generalized agents that can negotiate in several domains and against human counterparts, many automated negotiators are domain specific and their behavior cannot be generalized for other domains. Some of these difficulties arise from the differences inherent within the domains, the need to understand and learn negotiators’ diverse preferences concerning issues of the domain, and the different strategies negotiators can undertake. In this paper we present a system that enables alleviation of the difficulties in the design process of general automated negotiators termed Genius, a General Environment for Negotiation with Intelligent multi‐purpose Usage Simulation. With the constant introduction of new domains, e‐commerce and other applications, which require automated negotiations, generic automated negotiators encompass many benefits and advantages over agents that are designed for a specific domain. Based on experiments conducted with automated agents designed by human subjects using Genius we provide both quantitative and qualitative results to illustrate its efficacy. Finally, we also analyze a recent automated bilateral negotiators competition that was based on Genius. Our results show the advantages and underlying benefits of using Genius and how it can facilitate the design of general automated negotiators.

Taking important life decisions is a complex task leading to long-lasting consequences. It requires balancing one's own needs and those of other stakeholders. Current digital decision support focuses little on the human decision-making capabilities. Systems are designed as analytic tools to find optimal outcomes assuming stable and known preferences. However, insights from psychology and behavioral decision research show that people construct preferences during an adaptive decision-making process and are less rational than assumed by current tools. It has been suggested that a stronger focus on personal values could lead to improved decision making, but reflection on values is difficult for people. This paper presents a first exploration of how to aid people in reflecting on their values. It serves as a starting point to develop digital value-focused decision support tools. We describe the design of a probe for value reflection and several studies with experts and end-users that led to a first set of considerations for such tools.

Coactive Design is a new approach to address the increasingly sophisticated roles that people and robots play as the use of robots expands into new, complex domains. The approach is motivated by the desire for robots to perform less like teleoperated tools or independent automatons and more like interdependent teammates. In this article, we describe what it means to be interdependent, why this is important, and the design implications that follow from this perspective. We argue for a human-robot system model that supports interdependence through careful attention to requirements for observability, predictability, and directability. We present a Coactive Design method and show how it can be a useful approach for developers trying to understand how to translate high-level teamwork concepts into reusable control algorithms, interface elements, and behaviors that enable robots to fulfill their envisioned role as teammates. As an example of the coactive design approach, we present our results from the DARPA Virtual Robotics Challenge, a competition designed to spur development of advanced robots that can assist humans in recovering from natural and man-made disasters. Twenty-six teams from eight countries competed in three different tasks providing an excellent evaluation of the relative effectiveness of different approaches to human-machine system design.

Conventional n-gram language models are known for their limited ability to capture long-distance dependencies and their brittleness with respect to within-domain variations. In this paper, we propose a k-component recurrent neural network language model using curriculum learning (CL-KRNNLM) to address within-domain variations. Based on a Dutch-language corpus, we investigate three methods of curriculum learning that exploit dedicated component models for specific sub-domains. Under an oracle situation in which context information is known during testing, we experimentally test three hypotheses. The first is that domain-dedicated models perform better than general models on their specific domains. The second is that curriculum learning can be used to train recurrent neural network language models (RNNLMs) from general patterns to specific patterns. The third is that curriculum learning, used as an implicit weighting method to adjust the relative contributions of general and specific patterns, outperforms conventional linear interpolation. Under the condition that context information is unknown during testing, the CL-KRNNLM also achieves improvement over conventional RNNLM by 13% relative in terms of word prediction accuracy. Finally, the CL-KRNNLM is tested in an additional experiment involving N-best rescoring on a standard data set. Here, the context domains are created by clustering the training data using Latent Dirichlet Allocation and k-means clustering.

Teamwork has become a widely accepted metaphor for describing the nature of multi-robot and multi-agent cooperation. By virtue of teamwork models, team members attempt to manage general responsibilities and commitments to each other in a coherent fashion that both enhances performance and facilitates recovery when unanticipated problems arise. Whereas early research on teamwork focused mainly on interaction within groups of autonomous agents or robots, there is a growing interest in leveraging human participation effectively. Unlike autonomous systems designed primarily to take humans out of the loop, many important applications require people, agents, and robots to work together in close and relatively continuous interaction. For software agents and robots to participate in teamwork alongside people in carrying out complex real-world tasks, they must have some of the capabilities that enable natural and effective teamwork among groups of people. Just as important, developers of such systems need tools and methodologies to assure that such systems will work together reliably and safely, even when they have been designed independently.

We present a modified version of Extended Classifier System (XCS) on a humanoid NAO robot. The robot is capable of learning a complete, accurate, and maximally general map of an environment through evolutionary search and reinforcement learning. The standard alternation between explore and exploit trials is revised so that the robot relearns only when necessary. This modification makes the learning more effective and provides the XCS with external memory to evaluate the environmental change. Furthermore, it overcomes the drawbacks of learning rate settings in traditional XCS. A simple object seeking task is presented which demonstrates the desirable adaptivity of LCS for a sequential task on a real robot in dynamic environments.

We propose a dynamic Bayesian classifier for the socio-situational setting of a conversation. Knowledge of the socio-situational setting can be used to search for content recorded in a particular setting or to select context-dependent models in speech recognition. The dynamic Bayesian classifier has the advantage - compared to static classifiers such a naive Bayes and support vector machines - that it can continuously update the classification during a conversation. We experimented with several models that use lexical and part-of-speech information. Our results show that the prediction accuracy of the dynamic Bayesian classifier using the first 25% of a conversation is almost 98% of the final prediction accuracy, which is calculated on the entire conversation. The best final prediction accuracy, 88.85%, is obtained by bigram dynamic Bayesian classification using words and part-of-speech tags.

Two problems may arise when an intelligent (recommender) system elicits users’ preferences. First, there may be a mismatch between the quantitative preference representations in most preference models and the users’ mental preference models. Giving exact numbers, e.g., such as “I like 30 days of vacation 2.5 times better than 28 days” is difficult for people. Second, the elicitation process can greatly influence the acquired model (e.g., people may prefer different options based on whether a choice is represented as a loss or gain). We explored these issues in three studies. In the first experiment we presented users with different preference elicitation methods and found that cognitively less demanding methods were perceived low in effort and high in liking. However, for methods enabling users to be more expressive, the perceived effort was not an indicator of how much the methods were liked. We thus hypothesized that users are willing to spend more effort if the feedback mechanism enables them to be more expressive. We examined this hypothesis in two follow-up studies. In the second experiment, we explored the trade-off between giving detailed preference feedback and effort. We found that familiarity with and opinion about an item are important factors mediating this trade-off. Additionally, affective feedback was preferred over a finer grained one-dimensional rating scale for giving additional detail. In the third study, we explored the influence of the interface on the elicitation process in a participatory set-up. People considered it helpful to be able to explore the link between their interests, preferences and the desirability of outcomes. We also confirmed that people do not want to spend additional effort in cases where it seemed unnecessary. Based on the findings, we propose four design guidelines to foster interface design of preference elicitation from a user view.

We present a method for automatic classification of the socio-situational setting of a conversation based on the language used. The socio-situational setting depicts the social background of a conversation which involves the communicative goals, number of speakers, number of listeners and the relationship among the speakers and the listeners. Knowledge of the socio-situational setting can be used to search for content recorded in a particular setting or to select context-dependent models for example for speech recognition. We investigated the performance of different feature sets of conversation level features and word level features and their combinations on this task. Our final system, that classifies the conversations in the Spoken Dutch Corpus in one of 14 socio-situational settings, achieves an accuracy of 89.55%.

For the study of complex social situations, both gaming simulation and agent-based simulation have been proposed as research methods. The combination of gaming and agent-based simulation has proved useful for the formulation of theories underlying trade network processes. However, validation remains a problematic issue in that type of research. Two important sources of difficulties are the sensitivity of gaming simulations to the participants’ cultural background and the complexity of the agent model. The sensitivity to culture may be managed by incorporating it in the agent model. The complexity of the agent model may be managed by compositional process modeling. However, both solutions require additional validation. This presentation proposes a validation approach for a culturally adaptive, composed, process model.

The “negotiation dance”, as Raiffa calls the dynamic pattern of the bidding, has an important influence on the outcome of the negotiation. The current practice of evaluating a negotiation strategy is to focus on fairness and quality aspects of the ag

This paper introduces the concept of Coactivity as a new focal point for Human-Robot Interaction to address the more sophisticated roles of partner or teammate envisioned for future human-robot systems. We propose that most approaches to date have focused on autonomy and suggest that autonomy is the wrong focal point. The envisioned roles, if properly performed, have a high level of interdependence that cannot be addressed solely by autonomy and necessitate a focus on the coactivity.

Within many domains, among which biological, cognitive, and social areas, multiple interacting processes occur among agents with dynamics that are hard to handle. This paper presents the predicate logical Temporal Trace Language (TTL) for the formal specification and analysis of dynamic properties of agents and multi-agent systems. This language supports the specification of both qualitative and quantitative aspects, and therefore subsumes specification languages based on differential equations and qualitative, logical approaches. A software environment has been developed for TTL, which supports editing TTL properties and enables the formal verification of properties against a set of traces. The TTL environment proved its value in a number of projects within different biological, cognitive and social domains.

This paper presents a decentralized task allocation method that can handle allocation of tasks with time and precedence constraints in a multi-agent setting where not all information needed for a centralized approach is shared.

A component-based generic agent architecture for multi-attribute (integrative) negotiation is introduced and its application is described in a prototype system for negotiation about cars, developed in cooperation with, among others, Dutch Telecom KPN. The approach can be characterized as cooperative one-to-one multi-criteria negotiation in which the privacy of both parties is protected as much as desired. We model a mechanism in which agents are able to use any amount of incomplete preference information revealed by the negotiation partner in order to improve the efficiency of the reached agreements. Moreover, we show that the outcome of such a negotiation can be further improved by incorporating a “guessing” heuristic, by which an agent uses the history of the opponent’s bids to predict his preferences. Experimental evaluation shows that the combination of these two strategies leads to agreement points close to or on the Pareto-efficient frontier. The main original contribution of this paper is that it shows that it is possible for parties in a cooperative negotiation to reveal only a limited amount of preference information to each other, but still obtain significant joint gains in the outcome.

A new, formal, role-based, framework for modeling and analyzing both real world and artificial organizations is introduced. It exploits static and dynamic properties of the organizational model and includes the (frequently ignored) environment. The transition is described from a generic framework of an organization to its deployed model and to the actual agent allocation. For verification and validation of the proposed model, a set of dedicated techniques is introduced. Moreover, where most computational models can handle only two or three layered organizational structures, our framework can handle any arbitrary number of organizational layers. Henceforth, real-world organizations can be modeled and analyzed, as illustrated by a case study, within the DEAL project line

In this paper a modelling approach to the dynamics within a multi-agent organisation is presented. A declarative, executable specification language for dynamics within an organisation is proposed as a basis for simulation. Moreover, to be able to specify and analyse dynamic properties within an organisation, another declarative specification language is put forward, which is much more expressive than the executable language for simulations. Supporting tools have been implemented that consist of a software environment for simulation of organisation models and a software environment for analysis of dynamic properties against traces of dynamics within an organisation.

To analyze emergent behavior, a formal framework is needed to characterize the structure and dynamics of complex interaction-based multi-agent systems. We introduce an extension of an existing agent testbed for artificial societies making it possible to formally analyze the dynamics of the simulated agent system. The extension generates temporally annotated logical terms that describe parts of the dynamics of the simulated system. Based on these terms, it is possible to validate hypotheses about the system on different levels of aggregation, i.e. agent, group and system level. We present first results from a set of simple experiments in a class of artificial societies.

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In this paper the difference between two reproduction strategies of deliberate evolution is studied: partner selection and environment evaluation. The experiments conducted indicate that environment evaluation outperforms partner selection when timing of reproduction is important. When timing is not important, partner selection performs better than environment evaluation.

A compositional method is presented for the verification of multi-agent systems. The advantages of the method are the well-structuredness of the proofs and the reusability of parts of these proofs in relation to reuse of components. The method is illustrated for an example multi-agent system, consisting of co-operative information gathering agents. This application of the verification method results in a formal analysis of pro-activeness and reactiveness of agents, and shows which combinations of pro-activeness and reactiveness in a specific type of information agents lead to a successful cooperation.

Dynamics are an important aspect of agent models. Control of dynamics requires specific methods of specification that have their own specific semantics. This paper addresses specification and semantics of dynamics and control in component-based agent models. Specification is based on a dedicated formal design specification language for agent models. Semantics of the dynamics are defined using temporal traces with composite states. It is shown in what manner control aspects can be specified, and what their semantics is in terms of the temporal traces. An agent model for controlled diagnostic reasoning processes is used to illustrate the approach. © 2002 Wiley Periodicals, Inc.

In current literature few detailed process models for Requirements Engineering are presented: usually high-level activities are distinguished, without a more precise specification of each activity. In this paper the process of Requirements Engineering has been analyzed using knowledge-level modelling techniques, resulting in a well-specified compositional process model for the Requirements Engineering task. This process model is considered to be a generic process model: it can be refined (by instantiation or specialisation) into a process model for a specific kind of Requirements Engineering process.

Evolution of automated systems, in particular evolution of automated agents based on agent deliberation, is the topic of this paper. Evolution is not a merely material process, it requires interaction within and between individuals, their environments and societies of agents. An architecture for an individual agent capable of (1) deliberation about the creation of new agents, and (2) (run-time) creation of a new agent on the basis of this, is presented. The agent architecture is based on an existing generic agent model, and includes explicit formal conceptual representations of both design structures of agents and (behavioural) properties of agents. The process of deliberation is based on an existing generic reasoning model of design. The architecture has been designed using the compositional development method DESIRE, and has been tested in a prototype implementation.

In this paper, one of the informally described models of agent cooperation (Jennings, 1995) has been used to develop and formally specify a generic model of a cooperative agent (GCAM). The compositional development method for multi-agent systems DESIRE supported the principled design of this model of cooperation. To illustrate reusability of the generic model, two application domains have been addressed: collaborative engineering design, and Call Center support.

Emerging technologies allowing two-way communication between utility companies and their customers, as well as with smart equipment, are changing the rules of the energy market. Deregulation makes it even more demanding for utility companies to create new business processes for mutual benefit. Dynamic load management of the power grid is essential to make better and more cost-effective use of electricity production capabilities, and to increase customer satisfaction. The compositional development method DESIRE has been used to analyse, design, implement and verify a multi-agent system capable of negotiation for load management.