Modeling cooperation in multi-agent communities

Abstract

In Multi-Agent Systems the main goal is providing fruitful cooperation among agents in order to enrich the support given to user activities. Cooperation can be implemented in many ways, depending on how local knowledge of agents is represented and consists, in general, in providing the user with an integrated view of individual knowledge bases. But the main difficulty is determining which agents are promising candidates for a fruitful cooperation among the (possibly large) universe of agents operating in the net. This paper gives a contribution in this context, by proposing a formal framework for representing and managing cooperation in multi-agent networks. Semantic properties are here represented by coefficients and adaptive algorithms permit the computation of a set of agents suggested for cooperation. Actual choices of the users modify internal parameters in such a way that the next suggestions are closer to users expectancy.

Introduction

Coordinating the activities of multiple agents is a basic task for the viability of any system in which such agents coexist. Each agent in an agent community does not have to learn only by its own discovery, but also through a cooperation with other agents, by sharing individual learned knowledge. Indeed, cooperation is often considered as one of the key concepts of agent communities (Arisha, Kraus, Ross, Ozcan, & Subrahmanian, 1998; Dagaeff, Chantemargue, & Hirsbrunner, 1997; Doran, Franklin, Jennings, & Norman, 1997; Fisher, Muller, Schroeder, Staniford, & Wagner, 1997; Gmytrasiewicz & Durfee, 2000; Iglesias, Garijo, Centeno-Gonzalez, & Velasco, 1997; Kraus, 1997; Wooldridge & Jennings, 1999). Moreover, the problem of integrating heterogeneous knowledge bases has to be considered in order to implement cooperation (Adali & Emery, 1995; Adali & Subrahmanian, 1996; Boella & Lesmo, 2001; Mundhe & Sen, 2000; Nagendra Prasad & Lesser, 1999; Peshkin, Kim, Meuleau, & Kaelbling, 2000; Sun, 2001; Xuan, Lesser, & Zilberstein, 2001). Researchers in Intelligent Agent Systems have recognized that learning and adaptation are essential mechanisms by which agents can evolve coordinated behaviors finalized to meet the knowledge of the interest domain and the requirements of the individual agents (Byrne & Edwards, 1995; Sen, 1995, Sen, 1997; Tan, 1993; Weiß, 1996). In order to realize such a cooperation, some techniques developed in the field of Machine Learning has been introduced in various Multi-Agent Systems (often denoted by MAS) (Choi & Yoo, 1999; Moukas & Maes, 1998; Wang, Liu, & Conradi, 1999).

In such a context, this paper describes a new multi-agent model, called SPY, able to inform the individual user's agent of a multi-agent network about which agents are the most appropriate to be contacted for possible knowledge integration. The main contributions of this paper are the following:

• We point out which properties can be considered important for driving the integration of the knowledge coming from non-local agents and give a formal model in which such properties are represented as a quantitative information by means of a number of real coefficients.

• We propose an adaptive method for determining, for a given agent a of a multi-agent net, the most appropriate agents to cooperate with a. Such a method is adaptive in the sense that it takes into account reactions of the user (by exploiting some reactive properties) and, as such, its result depends on user behavior.

• On the basis of this model we design a strategy for supporting cooperation of agents operating in a multi-agent network. The first step consists in providing the user with a number of agent lists, each containing the most appropriate agents for cooperation, from which the user can choose agents she/he wants to contact for supporting her/his activity. The multiplicity of such choice lists depends on the multiplicity of the properties that can be used as preference criteria. Users are free to use the suggested lists even partially, or to ignore them. In any case, user's behavior induces a modification of some coefficients (describing reactive properties) in such a way that lists suggested in the future are (hopefully) closer to real user needs. Therefore, the system learns from user's behavior about how to provide her/him with suggestions meeting as much as possible their expectancy.

The plan of the paper is the following. In the following section we present related work. In Section 3, we describe how we represent agent knowledge. Section 4 includes the core of our proposal: after the definition of the semantic properties we show how to extract such properties and exploit them in order to detect good candidates for cooperation. Cooperation is implemented by merging agent knowledge bases through a technique presented in Section 5. The model is validated by a number of experiments and examples reported in Section 6. Finally, we draw our conclusion and sketch a description of a system implementation in Section 7.