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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Logical models of argument formalize commonsense reasoning while taking process and computation seriously. This survey discusses the main ideas which characterize different logical models of argument. It presents the formal features of a few main approaches to the modeling of argumentation. We trace the

This article is a revised and extended version of our earlier work which appeared in Proceedings of the 3rd International Symposium on Requirements Engineering (1997), pages 78 -- 86; Authors' addresses: A. Hunter, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK; email: A.Hunter@cs.ucl.ac.uk; B. Nuseibeh, Department of Computing, Imperial College, 180 Queen's Gate, London, SW7 2BZ, UK; email: ban@doc.ic.ac.uk.

We explore a framework for argumentation (based on classical logic) in which an argument is a pair where the first item in the pair is a minimal consistent set of formulae that proves the second item (which is a formula). We provide some basic definitions for arguments, and various kinds of counter-arguments (defeaters). This leads us to the definition of canonical undercuts which we argue are the only defeaters that we need to take into account. We then motivate and formalise the notion of argument trees and argument structures which provide a way of exhaustively collating arguments and counter-arguments. We use argument structures as the basis of our general proposal for argument aggregation.

This paper presents and discusses several methods for reasoning from inconsistent knowledge bases. A so-called argued consequence relation, taking into account the existence of consistent arguments in favour of a conclusion and the absence of consistent arguments in favour of its contrary, is particularly investigated. Flat knowledge bases, i.e., without any priority between their elements, are studied under different inconsistency-tolerant consequence relations, namely the so-called argumentative, free, universal, existential, cardinality-based, and paraconsistent consequence relations. The syntax-sensitivity of these consequence relations is studied. A companion paper is devoted to the case where priorities exist between the pieces of information in the knowledge base. Key words: inconsistency, argumentation, nonmonotonic reasoning, syntaxsensitivity. * Some of the results contained in this paper were presented at the Ninth Conference on Uncertainty in Artificial Intelligence (UAI'...

The language for describing inconsistency is underdeveloped. If a knowledgebase (a set of formulae) is inconsistent, we need more illuminating ways to say how inconsistent it is, or to say whether one knowledgebase is “more inconsistent” than another. To address this, we provide a general characterization of inconsistency, based on quasi-classical logic (a form of paraconsistent logic with a more expressive semantics than Belnap’s four-valued logic, and unlike other paraconsistent logics, allows the connectives to appear to behave as classical connectives). We analyse inconsistent knowledge by considering the conflicts arising in the minimal quasi-classical models for that knowledge. This is used for a measure of coherence for each knowledgebase, and for a preference ordering, called the compromise relation, over knowledgebases. In this paper, we formalize this framework, and consider applications in managing heterogeneous sources of knowledge.

. Information fusion is the process of deriving a single consistent knowledgebase from multiple knowledgebases. This process is important in many cognitive tasks such as decision-making, planning, design, and specification, that can involve collecting information from a number of potentially conflicting perspectives or sources, or participants. In this brief overview, we focus on the problem of inconsistencies arising in information fusion. In the following, we consider reasoning with inconsistencies, acting on inconsistencies, and resolving inconsistencies. 1 Introduction Many tasks that an intelligent agent performs such as decision-making, planning, design, and specification, often involve collecting information from a number of potentially conflicting perspectives or sources, or participants with different views, and forming a single combined view or perspective --- a synthesis, or consensus. Consider requirements engineering. The development of most large and complex systems nece...

Classical logic has many appealing features for knowledge representation and reasoning. But unfortunately it is flawed when reasoning about inconsistent information, since anything follows from a classical inconsistency. This problem is addressed by introducing the notions of "argument" and of "acceptability" of an argument. These notions are used to introduce the concept of "argumentative structures". Each definition of acceptability selects a subset of the set of arguments, and an argumentative structure is a subset of the power set of arguments. In this paper, we consider, in detail, a particular argumentative structure, where each argument is defined as a classical inference together with the applied premisses. For such arguments, a variety of definitions of acceptability are provided, the properties of these definitions are explored, and their inter-relationship described. The definitions of acceptability induce a family of logics called argumentative logics which we explore. The ...

Inconsistencies frequently occur in knowledge about the real-world. Some of these inconsistencies may be more significant than others, and some knowledgebases (sets of formulae) may contain more inconsistencies than others. This creates problems of deciding whether to act on these inconsistencies, and if so how. To address this, we provide a general characterization of inconsistency, based on quasi-classical logic (a form of paraconsistent logic with a more expressive semantics than Belnap’s four-valued logic, and unlike other paraconsistent logics, allows the connectives to appear to behave as classical connectives). We analyse inconsistent knowledge by considering the conflicts arising in the minimal quasi-classical models for that knowledge. This is used for a measure of coherence for each knowledgebase, and for a measure of significance of inconsistencies in each knowledgebase. In this paper, we formalize this framework, and consider applications in managing heterogeneous sources of knowledge. 1