This research addresses the interplay of changing social and organizational context factors with the big five personality traits and the three main characterizing elements of burnout. A computational analysis is contributed based on an integrative biological-mental-social network modeling approach. The simulation results show how two people who are high in personality traits such as agreeableness, openness, extraversion, conscientiousness, and highly sensitive to neuroticism, are vulnerable to reaching a burnout level in all dimensions whenever the organizational context is changing in a less favorable direction. By a What-If analysis, it is analyzed how important characteristics affect the outcomes and indicate how, in a qualitative sense, that is in line with empirical literature. Several differentiations are made. In particular, the connection between the three dimensions of burnout shows that it is possible that one employee reaches a burnout state while the other does not. It is also shown how therapy alone may not be sufficient as a long-term treatment, but therapy of one employee does affect the other. As numerical data are not (yet) available, further numerical validation has been proposed for future work.

When people interact, their behavior tends to become synchronized, a mutual coordination process that fosters short-term adaptations, like increased affiliation, and long-term adaptations, like increased bonding. This paper addresses for the first time how such short-term and long-term adaptivity induced by synchronization can be modeled computationally by a second-order multi-adaptive neural agent model. It addresses movement, affect and verbal modalities and both intrapersonal synchrony and interpersonal synchrony. The behavior of the introduced neural agent model was evaluated in a simulation paradigm with different stimuli and communication-enabling conditions. Moreover, in this paper, mathematical analysis is also addressed for adaptive network models and their positioning within the landscape of adaptive dynamical systems. The first type of analysis addressed shows that any smooth adaptive dynamical system has a canonical representation by a self-modeling network. This implies theoretically that the self-modeling network format is widely applicable, which also has been found in many practical applications using this approach. Furthermore, stationary point and equilibrium analysis was addressed and applied to the introduced self-modeling network model. It was used to obtain verification of the model providing evidence that the implemented model is correct with respect to its design specifications.

This study describes the use of adaptive temporal-causal networks to model and simulate the development of mutually interacting opinion states and connections between individuals in social networks. The focus is on adaptive networks combining the homophily principle with the more becomes more principle. The model has been used to analyse a data set concerning opinions about the use of alcohol and tobacco, and friendship relations. The achieved results provide insights in the potential of the approach.

In this study, an adaptive temporal-causal network model is presented for learning of basic skills for social interaction. It focuses on greeting a known person and how that relates to learning how to recognize a person from seeing his or her face. The model involves a Hebbian learning process. The model also addresses avoidance behavior related to enhanced sensory processing sensitivity. In scenarios persons without and with enhanced sensory processing sensitivity are compared. Mathematical analysis was performed to verify correctness of the model.

This paper introduces an integrated adaptive temporal-causal network model for dynamics in networks of social interactions addressing contagion between states, and changing connections within these social networks by two principles: the homophily principle and the more-becomes-more principle. The model has been evaluated in three different manners: by simulation experiments, by verification based on mathematical analysis, and by validation against an empirical data set.

In this paper, based on literature from Cognitive and Affective Neuroscience, a computational agent model is introduced incorporating the role of emotional awareness states in the dynamics of action generation. More specifically, it covers both automatic, unconscious (bottom-up) and more cognitive and conscious (top-down) emotion generation processes, and their mutual interaction. The model was formalised in a dynamical system format. In different scenarios the model shows simulation results that are in line with patterns reported in literature.

Collective decision making involves on the one hand individual mental states such as beliefs, emotions and intentions, and on the other hand interaction with others with possibly different mental states. Achieving a satisfactory common group decision on which all agree requires that such mental states are adapted to each other by social interaction. Recent developments in social neuroscience have revealed neural mechanisms by which such mutual adaptation can be realised. These mechanisms not only enable intentions to converge to an emerging common decision, but at the same time enable to achieve shared underlying individual beliefs and emotions. This paper presents a computational model for such processes. As an application of the model, an agent-based analysis was made of patterns in crowd behaviour, in particular to simulate a real-life incident that took place on May 4, 2010 in Amsterdam. From available video material and witness reports, useful empirical data were extracted. Similar patterns were achieved in simulations, whereby some of the parameters of the model were tuned to the case addressed, and most parameters were assigned default values. The results show the inclusion of contagion of belief, emotion, and intention states of agents results in better reproduction of the incident than non-inclusion.

Within human trust related behaviour, according to the literature from the domains of Psychology and Social Sciences often non-rational behaviour can be observed. Current trust models that have been developed typically do not incorporate non-rational

A human's performance in a complex task is highly dependent on the demands of the task, in the sense that highly demanding situations will often cause a degradation of performance. To maintain performance quality usually extra effort has to be contri

To support human functioning, ambient intelligent agents require knowledge about the tasks executed by the human. This knowledge includes design-time information like: (i) the goal of a task and (ii) the alternative ways for a human to achieve that goal, as well as run-time information such as the choices made by a human during task execution. In order to provide effective support, the agent must know exactly what steps the human is following. However, if not all steps along the path can be observed, it is possible that the agent cannot uniquely derive which path the human is following. Furthermore, in order to provide timely support, the agent must observe, reason, conclude and support within a limited period of time. To deal with these problems, this paper presents a generic focused reasoning mechanism to enable a guided selection of the path which is most likely followed by the human. This mechanism is based upon knowledge about the human and the workflow to perform the task. In order to come to such an approach, a reasoning mechanism is adopted in combination with the introduction of a new workflow representation, which is utilized to focus the reasoning process in an appropriate manner. The approach is evaluated by means of an extensive case study.

This paper discusses and evaluates an agent model that is able to manipulate the visual attention of a human, in order to support naval crew. The agent model consists of four sub-models, including a model to reason about a subject's attention. The mo

For agent-based modelling of collective phenomena, more and more agent models are employed that go beyond simple reactive behaviour. Such less trivial individual behaviours can be modelled either from an agent-internal perspective, in the form of dir

In order for agents to be able to act intelligently in an environment, a first necessary step is to become aware of the current situation in the environment. Forming such awareness is not a trivial matter. Appropriate observations should be selected by the agent, and the observation results should be interpreted and combined into one coherent picture. Humans use dedicated mental models which represent the relationships between various observations and the formation of beliefs about the environment, which then again direct the further observations to be performed. In this paper, a generic agent model for situation awareness is proposed that is able to take a mental model as input, and utilize this model to create a picture of the current situation. In order to show the suitability of the approach, it has been applied within the domain of F-16 fighter pilot training for which a dedicated mental model has been specified, and simulations experiments have been conducted. 1

Differences in social responses of individuals can often be related to differences in functioning of neurological mechanisms. This paper presents a cognitive agent model capable of showing different types of social response patterns based on such mechanisms, adopted from theories on mirror neuron systems, emotion regulation, empathy, and autism spectrum disorders. The presented agent model provides a basis for human-like social response patterns of virtual agents in the context of simulation-based training (e.g., for training of therapists), gaming, or for agent-based generation of virtual stories.

The cognitive agent model presented in this paper generates prior and retrospective ownership states for an action based on principles from recent neurological theories. A prior ownership state is affected by prediction of the effects of a prepared action, and exerts control by strengthening or suppressing actual execution of the action. A retrospective ownership state depends on whether the sensed consequences co-occur with the predicted consequences, and is the basis for acknowledging authorship of actions, for example, in social context. It is shown how poor action effect prediction capabilities can lead to reduced retrospective ownership states, as in persons suffering from schizophrenia.

To enhance believability of virtual agents, this paper presents an agent-based modelling approach for decision making, which integrates rational reasoning based on means-end analysis with personal psychological and biological aspects. The agent model developed is a combination of a BDI-model and a utility-based decision model in the context of specific desires and beliefs. The approach is illustrated by addressing the behaviour of violent criminals, thereby creating a model for virtual criminals. Within a number of simulation experiments, the model has been tested in the context of a street robbery scenario. In addition, a user study has been performed, which confirms the fact that the model enhances believability of virtual agents.

A computational agent model for monitoring and control of a virtual human agent’s resources and exhaustion is presented. It models a physically grounded intelligent decision making process within the agent model for physical effort to be spent. Simulation results are discussed, and a formal analysis is presented on conditions under which the agent model functions properly, for example, such that it can be used to avoid running out of resources. The model is related to a model for monitoring or simulating a person’s heart rate. Finally some validation experiments are briefly discussed.

Central research questions addressed within Criminology are how the geographical displacement of crime can be understood, explained, and predicted. The process of crime displacement is usually explained by referring to the interaction of three types

This paper focuses on relationships between agent models and their physical realisations. Approaches on reduction from philosophical literature are analysed in a formalised manner, and extended by incorporating context-dependency w.r.t. specific make

In recent times researchers have initiated investigating emotion as a collective property of groups, emphasizing the influence of combined emotions among group members on group processes. Within groups humans recognize and react emotionally to expressions of emotions of other group members. This paper uses a multi-agent-based approach to formalize and simulate such emotion contagion within groups. 1.

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.

In this paper a simulation model for visual attention is discussed and formally analysed. The model is part of the design of an agent-based system that supports a naval officer in its task to compile a tactical picture of the situation in the field.

Within Criminology, the process of crime displacement is usually explained by referring to the interaction of three types of agents: criminals, passers-by, and guardians. Most existing simulation models of this process are agent-based. However, when the number of agents considered becomes large, population-based simulation has computational advantages over agent-based simulation. This paper presents both an agent-based and a population-based simulation model of crime displacement, and reports a comparative evaluation of the two models. In addition, an approach is put forward to analyse the behaviour of both models by means of formal techniques.

Both for developing human-like virtual agents and for developing intelligent systems that make use of knowledge about the emotional state of the user, it is important to model the mood of a person. In this paper, a model for simulating the dynamics of mood is presented, based on psychological theories about a uni-polar clinical depression. The model was analyzed mathematically and by means of simulations, and it was shown that the model exhibits the most important characteristics of the theories. It shows how stress factors under some conditions can lead to a depression, while it will not lead to a depression under other conditions.

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.

A specification language for performance indicators and their relations and requirements is presented and illustrated for a case study in logistics. The language can be used in different forms, varying from informal, semiformal, graphical to formal. A software environment has been developed that supports the specification process and can be used to automatically check whether performance indicators or relations between them or certain requirements over them are satisfied in a given organisational process.

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In this paper the framework DESIRE for the design of compositional reasoning systems and multi-agent systems was applied to build a generic nonmonotonic reasoning system. The outcome is a general reasoning system that can be used to model different nonmonotonic reasoning formalisms and that can be executed by a generic execution mechanism. The main advantages of using DESIRE (for example, compared to a direct implementation in a programming language such as PROLOG) are that the design is generic and has a transparent compositional structure, and the explicit declarative specification of both the static and dynamic aspects of the nonmonotonic reasoning processes, including their control. © 2003 Wiley Periodicals, Inc.

In this article we show how formal semantics can be given to reasoning processes in meta-level architectures that reason about (object level) knowledge states and effects changes may have on them. Especially, attention is focused on the upward and downward reflections in these architectures. Temporalized epistemic logic is used to specify meta-level reasoning processes and the outcomes of these. © 2003 Wiley Periodicals, Inc.

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.

Meta-level architectures for dynamic control of reasoning processes are quite powerful. In the literature, many applications in reasoning systems modeling complex tasks are described, usually in a procedural manner. In this article we present a semantic framework based on temporal partial logic to describe the dynamics of reasoning behavior. Using these models, the semantics of the behavior of the whole (meta-level) reasoning system can be described by a set of (intended) temporal models. © 2002 Wiley Periodicals, Inc.

In this article a semantic framework is developed that provides a logical description of the functionality of an interactive reasoning process. The concept of functionality description defines the functionality of a reasoning process abstracted from specific inference relations or knowledge bases. Moreover, a domain description is formalized. A number of properties of a functionality description are identified and related to properties of the domain. It is established under which conditions a functionality can be implemented by an inference relation and a knowledge base. © 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 constructions leading to the formation of belief sets by agents are studied. The focus is on the situation when possible belief sets are built incrementally in stages. An infinite sequence of theories that represents such a process is called a reasoning trace. A set of reasoning traces describing all possible reasoning scenarios for the agent is called a reasoning frame. Default logic by Reiter is not powerful enough to represent reasoning frames. In the paper a generalization of default logic of Reiter is introduced by allowing infinite sets of justifications. This formalism is called infinitary default logic. In the main result of the paper it is shown that every reasoning frame can be represented by an infinitary default theory. A similar representability result for antichains of theories (belief frames) is also presented.

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.

This paper discusses an example of the application of a high-level modelling framework which supports both the specification and implementation of a system's conceptual design. This framework, DESIRE (framework for DEsign and Specification of Interacting REasoning components), explicitly models the knowledge, interaction, and coordination of complex tasks and reasoning capabilities in agent systems. For the application domain addressed in this paper, an operational multi-agent system which manages an electricity transportation network for a Spanish electricity utility, a comprehensible specification is presented.

Complex patterns of reasoning as involved in diagnosis, process and robot control, and design can be formally described by interacting basic inference processes. Reflection principles are used to give a formal description of some types of interactions between basic inference processes. Thus, complex reasoning is modelled in a modular form.