Abstract. The ability to communicate is one of the salient properties of agents. Although a number of agent communication languages (ACLs) have been developed, obtaining a suitable formal semantics for ACLs remains one of the greatest challenges of multiagent systems theory. Previous semantics have largely been mentalistic in their orientation and are based solely on the beliefs and intentions of the participating agents. Such semantics are not suitable for most multiagent applications, which involve autonomous and heterogeneous agents, whose beliefs and intentions cannot be uniformly determined. Accordingly, we present a social semantics for ACLs that gives primacy to the interactions among the agents. Our semantics is based on social commitments and is developed in temporal logic. This semantics, because of its public orientation, is essential to providing a rigorous basis for multiagent protocols. 1

In this paper we present some concepts and their relations that are necessary for modeling autonomous agents in an environment that is governed by some (social) norms. We divide the norms over three levels: the private level the contract level and the convention level. We show how deontic logic can be used to model the concepts and how the theory of speech acts can be used to model the generation of (some of) the norms. Finally we give some idea about an agent architecture incorporating the social norms based on a BDI framework.

Introduction Agents operate and exist in some environment, which typically is both computational and physical. The environment might be open or closed, and it might or might not contain other agents. Although there are situations where an agent can operate usefully by itself, the increasing interconnection and networking of computers is making such situations rare, and in the usual state of affairs the agent interacts with other agents. Whereas the previous chapter defined the structure and characteristics of an individual agent, the focus of this chapter is on systems with multiple agents. At times, the number of agents may be too numerous to deal with them individually, and it is then more convenient to deal with them collectively, as a society of agents. In this chapter, we will learn how to analyze, describe, and design environments in which agents can operate effectively and interact with each other productively. The environments will provide a computational infrastructu

In this paper norms are assumed to be useful in agent societies. It is claimed that not only following norms, but also the possibility of `intelligent' norm violation can be useful. Principles for agents that are able to behave deliberatively on the basis of explicitly represented norms are identified and an architecture is introduced. Using this agent architecture, norms can be communicated, adopted and used as meta-goals on the agent's own processes. As such they have impact on deliberation about goal generation, goal selection, plan generation and plan selection.

. In this paper norms are assumed to be useful in agent societies. It is claimed that not only following norms, but also the possibility of `intelligent' norm violation can be useful. Principles for agents that are able to behave deliberately on the basis of explicitly represented norms are identified and an architecture is introduced. Using this agent architecture, norms can be communicated, adopted and used as meta-goals on the agent's own processes. As such they have impact on deliberation about goal generation, goal selection, plan generation and plan selection. 1 Introduction Besides autonomy, an important characteristic of agents is that they can react to a changing environment. However, if the protocols that they use to react to (at least some part of) the environment are fixed, they have no ways to respond to impredictable changes. For instance, if an agent notices that another agent is cheating it cannot switch to another protocol to protect itself. (At least this is ...

We present an approach to social reasoning that integrates prior work on norms and obligations with the BDI approach to agent architectures. Norms and obligations can be used to increase the eficiency of agent reasoning, and their explicit representation supports reasoning about a wide range of behaviour types in a single framework. We propose a modified BDI interpreter loop that takes norms and obligations into account in an agent's deliberation.

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Abstract. In this paper we use recursive modelling to formalize sanction-based obligations in a qualitative game theory. In particular, we formalize an agent who attributes mental attitudes such as goals and desires to the normative system which creates and enforces its obligations. The wishes (goals, desires) of the normative system are the commands (obligations) of the agent. Since the agent is able to reason about the normative system’s behavior, our model accounts for many ways in which an agent can violate a norm believing that it will not be sanctioned. The theory can be used in theories or applications that need a model of rational decision making in normative multiagent systems, such as for example theories of fraud and deception, trust dynamics and reputation, electronic commerce, and virtual communities. 1

Cooperation is often presented as one of the key concepts which differentiates multi-agent systems from other related disciplines such as distributed computing, object-oriented systems, and expert systems. However it is a concept whose precise usage in agent-based systems is at best unclear and at worst highly inconsistent. Given the centrality of the issue, and the different ideological viewpoints on the subject, this was a lively panel which dealt with the following main issues: 1) What is cooperation? What are its limits? How does it relate to concepts like communication, coordination and negotiation? 2) Can a generic typology of cooperation be identified? If so, what might such a typology look like? What are the key characteristics? 3) What sorts of cooperation are you likely to see in real multi-agent systems? How does the degree of autonomy, self interest, benevolence, affect cooperation strategies? What are implications of cooperation? Is it always beneficial, or are there costs associated with it that lead to adverse effects? 4) Must cooperation be motivated a priori or can it emerge or evolve through complex social

In face-to-face interaction, multimodal signals are at work. We communicate not only through words, but also by intonation, body posture, hand gestures, gaze patterns, facial expressions, and so on. All these signals, verbal and nonverbal, do have a role in the communicative process. They add/modify/substitute information in discourse and are highly linked with one another. This is why facial and bodily animation is