The need for negotiation in multi-agent systems stems from the requirement for agents to solve the problems posed by their interdependence upon one another. Negotiation provides a solution to these problems by giving the agents the means to resolve their conflicting objectives, correct inconsistencies in their knowledge of other agents' world views, and coordinate a joint approach to domain tasks which benefits all the agents concerned. We propose a framework, based upon a system of argumentation, which permits agents to negotiate in order to establish acceptable ways of solving problems. The framework provides a formal model of argumentation-based reasoning and negotiation, details a design philosophy which ensures a clear link between the formal model and its practical instantiation, and describes a case study of this relationship for a particular class of architectures (namely those for belief-desire-intention agents). 1 Introduction An increasing number of software app...

Abstract. Many autonomous agents operate in domains in which the cooperation of their fellow agents cannot be guaranteed. In such domains negotiation is essential to persuade others of the value of co-operation. This paper describes a general framework for negotiation in which agents exchange proposals backed by arguments which summarise the reasons why the proposals should be accepted. The argumentation is persuasive because the exchanges are able to alter the mental state of the agents involved. The framework is inspired by our work in the domain of business process management and is explained using examples from that domain. Keywords: Automated negotiation, Argumentation, Persuasion. 1

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In this paper we present a new semantics, called Local Models Semantics, and use it to provide a foundation to reasoning with contexts. This semantics captures and makes precise the two main intuitions underlying contextual reasoning: (i) reasoning is mainly local and uses only part of what is potentially available (e.g., what is known, the available inference procedures), this part is what we call context (of reasoning); however (ii) there is compatibility among the reasoning performed in different contexts. We validate our semantics by formalizing two important forms of contextual reasoning: reasoning with viewpoints and reasoning about belief.

Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a party's subjective view of a domain. In this paper we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.

The goal of this paper is to propose a new technique for developing decision procedures for propositional modal logics. The basic idea is that propositional modal decision procedures should be developed on top of propositional decision procedures. As a case study, we consider satisfiability in modal K(m), that is modal K with m modalities, and develop an algorithm, called Ksat, on top of an implementation of the Davis-Putnam-Longemann-Loveland procedure. Ksat is thoroughly tested and compared with various procedures and in particular with the state-of-the-art tableau-based system Kris. The experimental results show that Ksat outperforms Kris and the other systems of orders of magnitude, highlight an intrinsic weakness of tableau-based decision procedures, and provide partial evidence of a phase transition phenomenon for K(m).

We present a logic-based formalism for modeling of dialogues between intelligent and autonomous software agents, building on a theory of abstract dialogue games which we present. The formalism enables representation of complex dialogues as sequences of moves in a combination of dialogue games, and allows dialogues to be embedded inside one another. The formalism is computational and its modular nature enables dierent types of dialogues to be represented.

Possible world semantics underlies many of the applications of modal logic in computer science and philosophy. The standard theory arises from interpreting the semantic definitions in the ordinary meta-theory of informal classical mathematics. If, however, the same semantic definitions are interpreted in an intuitionistic metatheory then the induced modal logics no longer satisfy certain intuitionistically invalid principles. This thesis investigates the intuitionistic modal logics that arise in this way. Natural deduction systems for various intuitionistic modal logics are presented. From one point of view, these systems are self-justifying in that a possible world interpretation of the modalities can be read off directly from the inference rules. A technical justification is given by the faithfulness of translations into intuitionistic first-order logic. It is also established that, in many cases, the natural deduction systems induce well-known intuitionistic modal logics, previously given by Hilbertstyle axiomatizations. The main benefit of the natural deduction systems over axiomatizations is their

Multi-Agent Systems are increasingly complex, and the problem of their verification and validation is acquiring increasing importance. In this paper we show how a well known and effective verification technique, symbolic model checking, can be generalized to deal with Multi-Agent Systems. Our approach is fully amenable to the reuse of data structures used in symbolic model checking, Binary Decision Diagrams in particular, to deal with extremely large state spaces. A preliminary implementation of the approach in the NuMAS system shows promising results.

It is widely agreed on that most cognitive processes are contextual in the sense that they depend on the environment, or context, inside which they are carried on. Even concentrating on the issue of contextuality in reasoning, many different notions of context can be found in the Artificial Intelligence literature. Our intuition is that reasoning is usually performed on a subset of the global knowledge base. The notion of context is used as a means of formalizing this idea of localization. Roughly speaking, we take a context to be the set of facts used locally to prove a given goal plus the inference routines used to reason about them (which in general are different for different sets of facts). Our perspective is similar to that proposed in [McC87, McC91]. The goal of this paper is to propose an epistemologically adequate theory of reasoning with contexts. The emphasis is on motivations and intuitions, rather than on technicalities. The two basic definitions are reported i...