Self-organization is a process where a stable pattern is formed by the cooperative behaviour between parts of an initially disordered system without external control or influence. It has been introduced to multi-agent systems as an internal control process or mechanism to solve difficult problems spontaneously. However, the complex link between local agent behaviour and system level behaviour in a self-organizing multi-agent system makes it difficult to predict the global behaviour of the system from the behaviour of the agents we design deductively, and thus implementation becomes the usual way of correctness evaluation for such a type of systems. Moreover, the complex link also makes it difficult to reconfigure the system when it is needed to change the results and predict the change to the global system behaviour under interventions. Therefore, it is important to have a logic-based framework that allows us to verify a self-organizing multi-agent system offline and reason about agents' independence in terms of the properties they bring about. This paper proposes a logic-based framework of self-organizing multi-agent systems, where agents communicate with each other and behave by following their prescribed local rules. The independence between coalitions of agents regarding the properties they will bring about is reasoned about from the dimensions of agents'collective actions and communication. We then illustrate the applicability of our framework using a real-world case, where a self-organization-based approach is used to form user communities. Finally, we show that the computational complexity of verifying whether a coalition of agents is fully independent with respect to a system property in a self-organizing multi-agent system is in exponential time.

Opportunism is an intentional behavior that takes advantage of knowledge asymmetry and results in promoting agents’ own value and demoting others’ value. It is important to eliminate such selfish behavior in multi-agent systems, as it has undesirable results for the participating agents. In order for monitoring and eliminating mechanisms to be put in the right place, it is needed to know in which context agents are likely to perform opportunistic behavior. In this paper, we develop a formal framework to reason about agents’ opportunistic propensity. Opportunistic propensity refers to the potential for an agent to perform opportunistic behavior. Agents in the system are assumed to have their own value systems and knowledge. With value systems, we define agents’ state preferences. Based on their value systems and incomplete knowledge about the state, they choose one of their rational alternatives to perform, which might be opportunistic behavior. We then characterize the situation where agents are likely to perform opportunistic behavior and the contexts where opportunism is impossible to occur, and prove the computational complexity of predicting opportunism.

Many philosophers are convinced that rationality dictates that one's overall set of intentions be consistent. The starting point and inspiration for our study is Bratman's planning theory of intentions. According to this theory, one needs to appeal to the fulfilment of characteristic planning roles to justify norms that apply to our intentions. Our main objective is to demonstrate that one can be rational despite having mutually inconsistent intentions. Conversely, it is also shown that one can be irrational despite having a consistent overall set of intentions. To overcome this paradox, we argue that it is essential for a successful planning system that one's intentions are practically consistent rather than being consistent or applying an aggregation procedure. Our arguments suggest that a new type of norm is needed: whereas the consistency requirement focuses on rendering the contents of one's intentions consistent, our new practical consistency requirement demands that one's intentions be able to simultaneously and unconditionally guide one's action. We observe that for intentions that conform to the 'own-action condition', the practical consistency requirement is equivalent to the traditional consistency requirement. This implies that the consistency requirement only needs to be amended in scenarios of choice under uncertainty.

This paper presents a so-called maramafication of an essential part of functional programming languages such as Haskell or Clean: the construction of fully polymorphic well-typed algebraic data structures based on type definitions with at most one type parameter. As such, this work extends our previous work, in which only a very limited form of polymorphism was present. Maramafication means the design of visual 'twins' of existing programming constructs using spatial metaphors rooted in common sense or inborn spatial intuition, to achieve self-explanatoriness. This is, among others, useful to considerably reduce the gap between programmers and non-programmers in the creation of programs, for educational purposes, for inclusion of non-typical programmers and for invoking enthusiasm among non-programmers.

Opportunism is a behavior that takes advantage of knowledge asymmetry and results in promoting agents' own value and demoting other agents' value. It is important to eliminate such a selfish behavior in multi-agent systems, as it has undesirable results for the participating agents. However, as the context we study here is multi-agent systems, system designers actually might not be aware of the value system for each agent thus they have no idea whether an agent will perform opportunistic behavior. Given this fact, this paper designs an epistemic mechanism to eliminate opportunism given a set of possible value systems for the participating agents: an agent's knowledge gets updated so that the other agent is not able to perform opportunistic behavior, and there exists a balance between eliminating opportunism and respecting agents' privacy.

This paper presents a maramafication of an essential part of FPLs: the construction of well-typed algebraic data structures based on type definitions with at most one type parameter. Maramafication means the design of visual ‘twins’ of existing programming constructs using spatial metaphors rooted in common sense or inborn spatial intuition, to achieve self-explanatoriness. This is, among others, useful to considerably reduce the gap between programmers and non-programmers in the creation of programs, for educational purposes or for invoking enthusiasm among non-programmers.

Opportunism is a behavior that takes advantage of knowledge asymmetry and results in promoting agents’ own value and demoting others’ value. We want to eliminate such selfish behavior in multi-agent systems, as it has undesirable results for the participating agents. In order for monitoring and eliminating mechanisms to be put in place, it is needed to know in which context agents will or are likely to perform opportunistic behavior. In this paper, we develop a framework to reason about agents’ opportunistic propensity. Opportunistic propensity refers to the potential for an agent to perform opportunistic behavior. In particular, agents in the system are assumed to have their own value systems and knowledge. With value systems, we define agents’ state preferences. Based on their value systems and incomplete knowledge about the state, they choose one of their rational alternatives, which might be opportunistic behavior. We then characterize the situations where agents will or will not perform opportunistic behavior and prove the computational complexity of predicting opportunism.

Errors in reasoning about probabilistic evidence can have severe consequences. In the legal domain a number of recent miscarriages of justice emphasises how severe these consequences can be. These cases, in which forensic evidence was misinterpreted, have ignited a scientific debate on how and when probabilistic reasoning can be incorporated in (legal) argumentation. One promising approach is to use Bayesian networks (BNs), which are well-known scientific models for probabilistic reasoning. For non-statistical experts, however, Bayesian networks may be hard to interpret. Especially since the inner workings of Bayesian networks are complicated, they may appear as black box models. Argumentation models, on the contrary, can be used to show how certain results are derived in a way that naturally corresponds to everyday reasoning. In this paper we propose to explain the inner workings of a BN in terms of arguments. We formalise a two-phase method for extracting probabilistically supported arguments from a Bayesian network. First, from a Bayesian network we construct a support graph, and, second, given a set of observations we build arguments from that support graph. Such arguments can facilitate the correct interpretation and explanation of the relation between hypotheses and evidence that is modelled in the Bayesian network.

Complex situations and systems can be studied by using adequate models in simulation. An important aspect of models and the simulation software is the ability to use a wide range of possible input parameters. The simulation described in this paper is based on agile manufacturing by using transport robots and cheap reconfigurable production platforms, called equiplets. This setup makes agile manufacturing of different products in parallel possible.The simulation showed the maximum load of such a production environment as well as a proof of concept for the distributed approach for transport used.

In social interactions, it is common for individuals to possess different amounts of knowledge about a specific transaction, and those who are more knowledgeable might perform opportunistic behavior to others in their interest, which promotes their value but demotes others' value. Such a typical social behavior is called opportunistic behavior (opportunism). In this paper, we propose a formal account of opportunism based on the situation calculus. We first propose a model of opportunism that only considers a single action between two agents, and then extend it to multiple actions and incorporate social context in the model. A simple example of selling a broken cup is used to illustrate our models. Through our models, we can have a thorough understanding of opportunism.

This chapter focuses on the communicative aspects of humor—more specifically, how it persuades. It explores specific persuasive tactics meant to persuade the audience through humor. Scholars discuss humor's various communicative, physiological, psychological, and sociological aspects. Message creators use humor in a variety of different situations and in very specific ways. Humor is communicated, interpreted, and understood through the lenses of three primary theories: superiority theory, relief theory and incongruity theory. Relief theory argues that people experience humor based on some form of relief in stressful situations or stressful events. Incongruity theory attributes humor to laughing at an occurrence resulting from an unexpected, perhaps out of the ordinary, nonthreatening surprise. The widespread use of humor suggests that it does have some desirable persuasive effects. Research has shown humor's capacity to promote objectivity, audience interest, and speaker credibility, which are its three major benefits in the realm of persuasion.

From the article: "Most technical studies require and assume from the students a certain knowledge of mathematics. In this paper an experiment is described where students, starting with a study of ICT at a bachelor level, are performing a very short test in mathematics to measure their knowledge. The results of this test are compared with the results students are having with the real exams at the end of the rst quarter of the rst year."

This paper provides a formalization of the other-condemning anger emotion which is a social type of anger triggered by the behaviour of other agents. Other-condemning anger responds to frustration of committed goals by others, and motivates goal-congruent behavior towards the blameworthy agents. Understanding this type of anger is crucial for modelling human behavior in social settings as well as designing socially aware artificial systems. We utilize existing psychological theories on other-condemning anger and propose a logical framework to formally specify this emotion. The logical framework is based on dynamic multi-agent logic with graded cognitive attitudes.

This paper focuses on the other-condemning anger emotion which is a social type of anger triggered by the behaviour of other agents. Other-condemning anger responds to frustration of committed goals by others, and motivates goal-congruent behavior towards the blameworthy agents. Understanding this type of anger is crucial for modelling human behavior in social settings as well as designing socially aware artificial systems. We summarize some existing psychological theories on other-condemning anger and advocate building logical frameworks to formally specify this emotion. We believe that a formalization should provide a precise conceptualization and characterization of other-condemning anger in terms of social and cognitive concepts such as beliefs, goals, intentions, controllability, accountability, and blameworthiness.

In abstract argumentation, the directionality principle conveys the intuition that, for an unattacked set, the choice of arguments that are part of an extension should only depend on the restriction of the framework to that set. Furthermore, having made such a choice, one should be able to select arguments from the rest of the framework so as to get an extension. In this paper we show how this idea can be generalized and used for formulating SCC-recursiveness as a stronger version of directionality. We argue that such properties characterize the information that is needed for computing the extensions of an argumentation semantics. We provide a formal approach for describing and comparing directionality-like properties. Our model provides a clear distinction between SCC-recursive semantics that use defense information and those that do not use it.

Ontologies are considered a necessary ingredient for communication among heterogeneous agents in the Web. With the multiplication of ontologies for the same domains, semantic interoperability has become a challenge. In this work, we study the use of ontology negotiation in a agent communication mechanism for agents with ontological reasoning. The resulting communication mechanism allows agents to exchange not only factual but also terminological knowledge about an individual domain and is closely related to available mechanisms in the literature such as KQML and FIPA-ACL.

Personalisation can increase the learning efficacy of educational games by tailoring their content to the needs of the individual learner. This paper presents the Personalised Educational Game Architecture (PEGA). It uses a multi-agent organisation and an ontology to offer learners personalised training in a game environment. The multi-agent organisation's flexibility enables adaptive automation; the instructor can decide to control only parts of the training, while leaving the rest to the intelligent agents.

Due to the uses of DNA profiling in criminal investigation and decision-making, it is ever more common that probabilistic information is discussed in courts. The people involved have varied backgrounds, as factfinders and lawyers are more trained in the use of non-probabilistic information, while forensic experts handle probabilistic information on a routine basis. Hence, it is important to have a good understanding of the sort of reasoning that happens in criminal cases, both probabilistic and non-probabilistic. In the present article, we report results on combining three normative reasoning frameworks from the literature: arguments, scenarios and probabilities. We discuss a hybrid model that connects arguments and scenarios, a method to probabilistically model possible scenarios in a Bayesian network, a method to extract arguments from a Bayesian network and a proposal to model arguments for and against different scenarios in standard probability theory. These results have been produced as parts of research projects on the formal and computational modelling of evidence. The present article reviews these results, shows how they are connected and where they differ, and discusses strengths and limitations.

Opportunism is a behavior that takes advantage of knowledge asymmetry and results in promoting agents' own value and demoting others' value. We propose a framework to reason about agents' opportunistic propensity and characterize the situation where agents will perform opportunistic behavior.