From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.

Introduction to Multiagent Systems

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).

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Agents that Reason and Negotiate by Arguing

Negotiation is essential in settings where autonomous agents have conflicting interests and a desire to cooperate. For this reason, mechanisms in which agents exchange potential agreements according to various rules of interaction have become very popular in recent years as evident, for example, in the auction and mechanism design community. However, a growing body of research is now emerging which points out limitations in such mechanisms and advocates the idea that agents can increase the likelihood and quality of an agreement by exchanging arguments which influence each others' states. This community further argues that argument exchange is sometimes essential when various assumptions about agent rationality cannot be satisfied. To this end, in this article, we identify the main research motivations and ambitions behind work in the field. We then provide a conceptual framework through which we outline the core elements and features required by agents engaged in argumentation-based negotiation, as well as the environment that hosts these agents. For each of these elements, we survey and evaluate existing proposed techniques in the literature and highlight the major challenges that need to be addressed if argument-based negotiation research is to reach its full potential.

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Argumentation-based negotiation

Reaching agreements through argumentation: a logical model and implementation

Logical models of arguement formalize commonsense reasoning while taking process and computation seriously. This survey discusses the main ideas that characterize different logical models of argument. It presents the formal features of a few features of a few main approaches to the modeling of argumentation. We trace the evolution of argumentation from the mid-1980s, when argument systems emerged as an alternative to nonmonotonic formalisms based on classical logic, to the present, as argument in embedded in different complex systems for real-world applications, and allow more formal work to be done in different areas, such as AI and Law, case-based reasoning and negotiation among intelligent agents.

Logical models of argument

We present the syntax and proof theory of a logic of argumentation, LA. We also outline the development of a category theoretic semantics for LA. LA is the core of a proof theoretic model for reasoning under uncertainty. In this logic, propositions are labelled with a representation of the arguments which support their validity. Arguments may then be aggregated to collect more information about the potential validity of the propositions of interest. We make the notion of aggregation primitive to the logic, and then define strength mappings from sets of arguments to one of a number of possible dictionaries. This provides a uniform framework which incorporates a number of numerical and symbolic techniques for assigning subjective confidences to propositions on the basis of their supporting arguments. These aggregation techniques are also described, with examples

A logic of argumentation for reasoning under uncertainty

Arguments, contradictions and practical reasoning

A recurring problem with the development of decision support systems in medicine is the difficulty of eliciting precise numerical uncertainty values for large fragments of the domain. A prototype information system for general practitioners, the Oxford System of Medicine (OSM), addressed this problem by using an informal mechanism of argumentation. Arguments supporting and opposing the propositions of interest were identified when numerical uncertainty values where unavailable. We propose, in this paper, a proof theoretic model for reasoning under uncertainty which is motivated by the need to provide a formal underpinning for the OSM inference engine. As well as giving a presentation of a “Logic of Argumentation” (LA) as a labelled deduction system, we also discuss the development of a category theoretic semantics for LA.

Simon Ambler

Papers

A logic of argumentation for reasoning under uncertainty

Arguments, contradictions and practical reasoning

The Development of a Logic of Argumentation

Arguments, Contradicitions and Practical Reasoning.