Motivational attitudes and norms in a unified agent communication language for open multi-agent systems: A pragmatic approach

In order to perform some tasks, agents need to interact with each other. Thus, a Multi-Agent System (MAS) is a system composed by several agents, capable of mutual interaction. Communication is a kind of interaction that allows agents to work more effectively by sharing knowledge and exchanging information. Thus, communication allow agents to make queries, transmit information, perform declarations and to commit themselves to execute an action. For agents to communicate, a method of sequencing messages is needed (conversations). For conversations to be successful, pragmatic principles to guide the linguistic interchange should be make available. These principles should not violate crucial properties of agency such as autonomy, heterogeneity and proactiveness. Various classes of agent communication languages (ACLs) have been proposed to handle these issues, but standardization is still a holy grail. We claim that a rethinking of the general principles on the foundations of ACLs is needed. More specifically, a redistribution of the role played by the semantics (speech acts) and pragmatics (protocols and policies) of ACLs will dissolve some of the most important problems currently affecting agent communication. Agent communication has traditionally focused on the semantics of speech acts, and many important advances have been done on that respect. But for some exceptions discussed later on the pragmatic component has often been the poor relative, consisting usually on low-level contextual free protocols that merely established the order in which speech acts may be used. This thesis aims to show how a high-level ACL pragmatics is crucial to facilitate the use of the semantic component in a variety of scenarios and a necessary step towards standardization. In the pragmatic turn for agent communication that we are proposing, ACL pragmatics will take the form of conversation norms. These principles can be specifically formulated by means of conversation protocols and policies that govern agents’ message interchange taking into account contextual factors that affect agents’ decisions. Once the theoretical issues are established, we ground the pragmatic principles in a computational model and study its applicability using a declarative programming language

Weaving a fabric of socially aware agents

The expansion of web-enabled social interaction has shed light on social aspects of intelligence that have not been typically studied within the AI paradigm so far. In this context, our aim is to understand what constitutes intelligent social behaviour and to build computational systems that support it. We argue that social intelligence involves socially aware, autonomous individuals that agree on how to accomplish a common endeavour, and then enact such agreements. In particular, we provide a framework with the essential elements for such agreements to be achieved and executed by individuals that meet in an open environment. Such framework sets the foundations to build a computational infrastructure that enables socially aware autonomy.This work has been supported by the projects EVE(TIN2009-14702-C02-01) and AT (CSD2007-0022)Peer Reviewe

Detection and resolution of normative conflicts in multi-agent systems : a literature survey

Peer reviewedPostprin

I Don't Want to Think About it Now:Decision Theory With Costly Computation

Computation plays a major role in decision making. Even if an agent is willing to ascribe a probability to all states and a utility to all outcomes, and maximize expected utility, doing so might present serious computational problems. Moreover, computing the outcome of a given act might be difficult. In a companion paper we develop a framework for game theory with costly computation, where the objects of choice are Turing machines. Here we apply that framework to decision theory. We show how well-known phenomena like first-impression-matters biases (i.e., people tend to put more weight on evidence they hear early on), belief polarization (two people with different prior beliefs, hearing the same evidence, can end up with diametrically opposed conclusions), and the status quo bias (people are much more likely to stick with what they already have) can be easily captured in that framework. Finally, we use the framework to define some new notions: value of computational information (a computational variant of value of information) and and computational value of conversation.Comment: In Conference on Knowledge Representation and Reasoning (KR '10