Until recently the game industry heavily relied on manual playtesting to test the games it produces. Even if the benefits of introducing automated testing are acknowledged, it is rarely done in practice. Some of the main hurdles include the lack of automated testing tools that can target computer games as well as the complexity of automated game plays which are much more difficult to program than typical simple test sequences. This article presents an agent-based testing framework called aplib that comes with a Domain Specific Language (DSL) that allows complex playtests to be programmed more abstractly. A so-called goal structure is used to abstractly formulate a playtest scenario in terms of main goals and their decomposition into subgoals. Scenarios that are not too complicated can be formulated using static goal structures. More complex scenarios may need a test agent that can dynamically adapt its play according to the situation that evolves during the play. To handle such cases, aplib allows dynamic goals to be expressed as well. Invariants and pre-/post-conditions are used to assert the properties that a play is expected to satisfy. They include differential properties that allow constraints on the current state to be related to that of past states. Three case studies are included in the article. The first one aims to evaluate the performance of playtests programmed with aplib . The second shows that the approach can also be combined with other automated testing approaches, in this case reinforcement learning. The third shows the applicability of such playtests in a 3D setup and for non-functional testing.

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.

Theory of mind refers to the human ability to reason about the mental content of other people, such as their beliefs, desires, and goals. People use their theory of mind to understand, reason about, and explain the behaviour of others. Having a theory of mind is especially useful when people collaborate, since individuals can then reason on what the other individual knows as well as what reasoning they might do. Similarly, hybrid intelligence systems, where AI agents collaborate with humans, necessitate that the agents reason about the humans using computational theory of mind. However, to try to keep track of all individual mental attitudes of all other individuals becomes (computationally) very difficult. Accordingly, this paper provides a mechanism for computational theory of mind based on abstractions of single beliefs into higher-level concepts. These abstractions can be triggered by social norms and roles. Their use in decision making serves as a heuristic to choose among interactions, thus facilitating collaboration. We provide a formalization based on epistemic logic to explain how various inferences enable such a computational theory of mind. Using examples from the medical domain, we demonstrate how having such a theory of mind enables an agent to interact with humans effectively and can increase the quality of the decisions humans make.

As the game industry continues to evolve in pop-ularity, testing the experience of players becomes crucial for attracting and retaining players in the highly competitive market. However, the absence of automated methods for articulating and verifying player experience (PX) specifications led us to introduce EmoSTL, a specialized language that extends Linear Temporal Logic with spatial and time-interval expressions, enabling the capture of complex temporal and spatial aspects of players' emotions and their experiences within games. We conducted a user study to collect suggestive PX requirements for a game under test to assess the capabilities of EmoSTL. Findings reveal that the language formalizes 92 percent of the set PX requirements, and with runtime verification, several PX design issues are iden-tified in the game. Moreoever, EmoSTL performance evaluation demonstrates its linear execution time, showcasing the language potential usage in automated PX testing of games.

As video games become more complex and widespread, player experience (PX) testing becomes crucial in the game industry. Attracting and retaining players are key elements to guarantee the success of a game in the highly competitive market. Although a number of techniques have been introduced to measure the emotional aspect of the experience, automated testing of player experience still needs to be explored. This paper presents PX-MBT, a framework for automated player experience testing with emotion pattern verification. PX-MBT (1) utilizes a model-based testing approach for test suite generation, (2) employs a computational model of emotions developed based on a psychological theory of emotions to model players' emotions during game-plays with an intelligent agent, and (3) verifies emotion patterns given by game designers on executed test suites to identify PX-issues. We explain PX-MBT architecture and provide an example along with its result in emotion pattern verification, which asserts the evolution of emotions over time, and heat-maps to showcase the spatial distribution of emotions on the game map.

Abstract Player eXperience (PX) testing has attracted attention in the game industry as video games become more complex and widespread. Understanding players’ desires and their experience are key elements to guarantee the success of a game in the highly competitive market. Although a number of techniques have been introduced to measure the emotional aspect of the experience, automated testing of player experience still needs to be explored. This paper presents a framework for automated player experience testing by formulating emotion patterns’ requirements and utilizing a computational model of players’ emotions developed based on a psychological theory of emotions along with a model-based testing approach for test suite generation. We evaluate the strength of our framework by performing mutation test. The paper also evaluates the performance of a search-based generated test suite and LTL model checking-based test suite in revealing various variations of temporal and spatial emotion patterns. Results show the contribution of both algorithms in generating complementary test cases for revealing various emotions in different locations of a game level.

An important issue in deploying an autonomous system is how to enable human users and stakeholders to develop an appropriate level of trust in the system. It has been argued that a crucial mechanism to enable appropriate trust is the ability of a system to explain its behaviour. Obviously, such explanations need to be comprehensible to humans. Due to the perceived similarity in functioning between humans and autonomous systems, we argue that it makes sense to build on the results of extensive research in social sciences that explores how humans explain their behaviour. Using similar concepts for explanation is argued to help with comprehensibility, since the concepts are familiar. Following work in the social sciences, we propose the use of a folk-psychological model that utilises beliefs, desires, and ``valuings''. We propose a formal framework for constructing explanations of the behaviour of an autonomous system, present an (implemented) algorithm for giving explanations, and present evaluation results.

Testing for quality assurance (QA) is a crucial step in the development of Extended Reality (XR) systems that typically follow iterative design and development cycles. Bringing automation to these testing procedures will increase the productivity of XR developers. However, given the complexity of the XR environments and the User Experience (UX) demands, achieving this is highly challenging. We propose to address this issue through the creation of autonomous cognitive test agents that will have the ability to cope with the complexity of the interaction space by intelligently explore the most prominent interactions given a test goal and support the assessment of affective properties of the UX by playing the role of users.

Artificial Intelligence (AI) applications are being used to predict and assess behaviour in multiple domains which directly affect human well-being. However, if AI is to improve people’s lives, then people must be able to trust it, by being able to understand what the system is doing and why. Although transparency is often seen as the requirement in this case, realistically it might not always be possible, whereas the need to ensure that the system operates within set moral bounds remains. In this paper, we present an approach to evaluate the moral bounds of an AI system based on the monitoring of its inputs and outputs. We place a ‘Glass-Box’ around the system by mapping moral values into explicit verifiable norms that constrain inputs and outputs, in such a way that if these remain within the box we can guarantee that the system adheres to the value. The focus on inputs and outputs allows for the verification and comparison of vastly different intelligent systems; from deep neural networks to agent-based systems. The explicit transformation of abstract moral values into concrete norms brings great benefits in terms of explainability; stakeholders know exactly how the system is interpreting and employing relevant abstract moral human values and calibrate their trust accordingly. Moreover, by operating at a higher level we can check the compliance of the system with different interpretations of the same value.

Existing approaches and frameworks for modeling virtual dialogue tend to be designed with dyadic interactions in mind, and are often built to serve solely in task-oriented domains. However, modeling realistic action and turn-taking in more general scenarios remains a challenge. In this paper we propose a generic framework to aid in development of multi-modal, multi-party dialogue. It contains mechanisms inspired by social practice theory for both action selection and timing --- including handling of interruption. As a proof-of-concept, we employ these ideas in a virtual couples-therapy session, demonstrating their potential in modeling complex real-life situations.

Understanding the social contexts in which actions and interactions take place is of utmost importance for planning one’s goals and activities. People use social practices as means to make sense of their environment, assessing how that context relates to past, common experiences, culture and capabilities. Social practices can therefore simplify deliberation and planning in complex contexts. In the context of patient-centered planning, hospitals seek means to ensure that patients and their families are at the center of decisions and planning of the healthcare processes. This requires on one hand that patients are aware of the practices being in place at the hospital and on the other hand that hospitals have the means to evaluate and adapt current practices to the needs of the patients. In this paper we apply a framework for formalizing social practices of an organization to an emergency department that carries out patient-centered planning. We indicate how such a formalization can be used to answer operational queries about the expected outcome of operational actions.

Autonomous agents are increasingly required to be able to make moral decisions. In these situations, the agent should be able to reason about the ethical bases of the decision and explain its decision in terms of the moral values involved. This is of special importance when the agent is interacting with a user and should understand the value priorities of the user in order to provide adequate support. This paper presents a model of agent behavior that takes into account user preferences and moral values.

The paper describes an architecture for a social conversational agent. The aim is to use the agent in a serious game to improve the social and communicative skills of the players, showing the social effects of conversational choices on the emotions and behavioural changes of the interlocutors.

Agent organizations can be seen as a set of entities regulated by mechanisms of social order and created by more or less autonomous actors to achieve common goals. Just like agents, organizations should also be able to adapt themselves to changing environments. In order to develop a theory of how this reorganization should be performed we need a formal framework in which organizations, organizational performance and the reorganization itself can be described. In this article, we present a formal description of reorganization actions in Logic forAgent Organization. We show how this formalization can support the preservation of some nice properties of organizations while it can also be used to reason about which reorganization is needed to achieve some basic organizational properties.

Emotional synthetic characters are able to evaluate (appraise) events as positive or negative with their emotional states being triggered by several factors. Currently, the vast majority of models for appraisal in synthetic characters consider factors related to the goals and preferences of the characters. We argue that appraisals that only take into consideration these "personal" factors are incomplete as other more social factors, such as the normative and the social context, including in-group and out-group relations, should be considered as well. Without them, moral emotions such as shame cannot be appraised, limiting the believability of the characters in certain situations. We present a model for the appraisal of characters' actions that evaluates whether actions by in-group and out-group members which conform, or not, to social norms generate different emotions depending on the social relations between the characters. The model was then implemented in an architecture for virtual agents and evaluated with humans. Results suggest that the emotions generated by our model are perceived by the participants, taking into account the social context and that participants experienced very similar emotions, both in type and intensity, to the emotions appraised and generated by the characters.

Increasingly, software engineering involvesopensystems consisting of autonomous and heterogeneous participants oragentswho carry out loosely coupled interactions. Accordingly, understanding and specifying communications among agents is a key concern. A focus on ways to formalizemeaningdistinguishes agent communication from traditional distributed computing: meaning provides a basis for flexible interactions and compliance checking. Over the years, a number of approaches have emerged with some essential and some irrelevant distinctions drawn among them. As agent abstractions gain increasing traction in the software engineering of open systems, it is important to resolve the irrelevant and highlight the essential distinctions, so that future research can be focused in the most productive directions. This article is an outcome of extensive discussions among agent communication researchers, aimed at taking stock of the field and at developing, criticizing, and refining their positions on specific approaches and future challenges. This article serves some important purposes, including identifying (1) points of broad consensus; (2) points where substantive differences remain; and (3) interesting directions of future work.

No abstract available.

Serious computer games have become increasingly popular; they also require more elaborate and natural behavior on the part of Non-Playing Characters. The more elaborate the interactions among characters are during a game, the more difficult it is to design these characters without the use of specialized tools geared towards implementing intelligent agents in a modular way. This thus seems to be an excellent area for the application of intelligent agent technology, which for the past two decades has been developed based on design concepts such as Goals, Intentions, Plans and Beliefs. A first attempt at connecting game engines with these types of agents has been made with Gamebots [1]. Gamebots provides an infrastructure that allows the interfacing of any agent platform to the computer game Unreal Tournament. Gamebots manages the provision of relevant information regarding the game state, while delivering commands for actions from the agents to Unreal. More recently, this package was used as the basis for more extensive middleware called Pogamut [2]. Although the aforementionedmiddleware does allow the interfacing of agents to the game engine, that in itself does not guarantee proper behavior of the agents in the game. In the workshop series on Agents for Games and Simulations [3,4], started in 2009, issues regarding the connection of agent technology to game engines has been discussed. Most of these issues derive from the fact that game engines are typically designed to be in total control of the game’s progress. On the other hand one, of the major attributes of agents developed on MAS platforms is that they are autonomous (to some extent) and interact asynchronously. We want to exploit the benefits of having agents deciding intelligently and autonomously about their next actions, while not losing control of the game. This balancing act leads to three broad categories of issues. The first category is that of technical issues; an important issue in this category is that of coping with real-time environments. Unfortunately, agent technology has hardly bothered with real-time issues up until now. A major exception is the use of agent technology in robotics, where one obviously also has to deal with real-time environments. Maybe this is one of the reasons that, in robotics, people do not use standard (BDI) agent platforms as basis for

Service-oriented computing is the "new wave" emerging from the growing up of web services and its adoption of elements of semantic web technology. More sophistication, in response to business requirements, does of course not make it easier to use or to control. In particular business processes demand resilience and (real-time) adaptation in the face of changing business requirements, incorporation of alternative services and finding suitable substitutes when those needed are not available. The EU-funded ALIVE project is prototyping ideas, driven by commercial and industrial uses cases, that utilize research in organizational modelling, software agents, model-driven engineering, artificial intelligence, semantic web and web services to construct tools and demonstrators to address these needs. This tutorial will focus on a use case from the ALIVE project (in one of the domains of crisis management, communications, or information services), discuss the requirements that arise from it and then explore it from the three perspectives that characterize the ALIVE approach: organizations, coordination and services, all illustrated by the innovative tools that have been developed during the project.

Web services are increasingly behaving as nodes in a digital, dynamic ecosystem. On the one hand, this new situation requires flexible, spontaneous and opportunistic collaboration activities to be identified and established among (business) parties. On the other hand, it also requires engineering approaches able to integrate new functionalities and behaviors into running systems and active, distributed, interdependent processes. In this paper we present a multi-level architecture, combining organizational and coordination theories with model driven development, for the implementation, deployment and management of dynamic, flexible and robust service-oriented business applications.

Autonomous agents are believed to have control over their internal state and over their behaviour. For that reason, an agent should control how and by whom it is being influenced. We introduce a reasoning component for BDI-agents that deals with the control over external influences, and we propose heuristics using local knowledge to process incoming stimuli. One of those heuristics is based on information relevance with respect to the agent's current plans and goals. We have developed a way to determine the relevance of information in BDI-agents using magic sets from database research as basis. The method presented shows a new application of magic sets by applying the theory in agent systems.

This paper discusses semantic interoperability issues in agent-based E-commerce systems. The literature reports various techniques to enable agents to understand the meanings of the messages exchanged. We will argue how these different techniques can be combined in one agent communication protocol to obtain the best of each world. The resulting communication protocol enables agents to sufficiently understand each other to participate in successful collaboration.

While argumentation-based negotiation has been accepted as a promising alternative to game-theoretic or heuristic based negotiation, no evidence has been provided to confirm this theoretical advantage. We propose a model of bilateral negotiation extending a simple monotonic concession protocol by allowing the agents to exchange information about their underlying interests and possible alternatives to achieve them during the negotiation. We present an empirical study that demonstrates (through simulation) the advantages of this interest-based negotiation approach over the more classic monotonic concession approach to negotiation.

The article investigates the logic underlying statements of the form ‘X counts as Y in context C’ which are commonly considered to represent the paradigmatic syntax of constitutive rules, i.e. the non-regulative component of normative systems. The analytical thesis backing the whole work consists in interpreting such statements as contextual classifications. This reading of counts-as is thoroughly investigated in two variants which we call the contextual classificatory reading and the proper contextual classificatory reading. The formal analysis of these readings, which we carry out making use of modal logic, disentangles two possible senses in which counts-as statements can be interpreted within a classificatory perspective, and clarifies the logical relations holding between them. The proposal is then compared in detail with previous work on the topic, in order to shed light on similarities, differences and their grounds.

The work investigates the logic underlying the representation of the non-regulative component of normative systems, the so-called counts-as. The analytic thesis we hold here is to view counts-as statements as statements which yield classifications and which hold only with respect to a context. These two aspects of the semantics of counts-as-the classificatory flavor, and the contextual character-are then investigated by means of modal logic techniques from a semantics-driven perspective, and a formalization of counts-as statements is thus proposed. The result is then compared in detail with previous work on the topic, and related with work which, despite developed in different areas of applied and philosophical logic, shares interesting technical and theoretical similarities with our proposal.

Agent technology is an exciting and important new way to create complex software systems. Agents blend many of the traditional properties of AI programs—knowledge–level reasoning, flexibility, proactiveness, goal–directedness, and so forth—with insights gained from distributed software engineering, machine learning, negotiation and teamwork theory, and the social sciences. An important part of the agent approach is the principle that agents (like humans) can function more effectively in groups that are characterized by cooperation and division of labor. Agent programs are designed to autonomously collaborate with each other in order to satisfy both their internal goals and the shared external demands generated by virtue of their participation in agent societies. This type of collaboration depends on a sophisticated system of inter–agent communication. The assumption that inter–agent communication is best handled through the explicit use of an agent communication language (ACL) underlies each of the articles in this special issue. In this introductory article, we will supply a brief background and introduction to the main topics in agent communication.

No abstract available.