Agents adapt to majority behaviours

Agents within a group can have different percep- tions of their working environment and autonomously fulfil their goals. However, they can be aware of beliefs and goals of the group as well as other members so that they can adjust their behaviours accordingly. To model these agents, we explicitly include knowledge commonly shared by the group and that obtained from other agents. By avoiding actions which violate "mental attitudes" shared by the majority of the group, agents demonstrate their social commitment to the group. Defeasible logic is chosen as our representation formalism for its computa- tional efficiency, and for its ability to handle incomplete and conflicting information. Hence, our agents can enjoy the low computational cost while performing "reasoning about others". Finally, we present the implementation of our multi-agent system. As can be observed from a society, every individual member can take any action driven by his/her desires. However, the individuals are often required to comply with the society "conventions". Essentially "conventions" could be norms, con- straints or desires which are popularly recognised by the soci- ety. Being aware of those conventions, an individual member can strengthen his/her social relationships and coordinates better with other members. Similarly, in a multi-agent system, an agent maintains its social commitments by discovering the "common attitudes" and fulfils its own demands whilst obligating to these attitudes. Hence agents reason not only about their own beliefs and goals but also about those of other agents. Besides, agents autonomously observe and judge their surroundings by their own view resulting in partial and sometimes conflicting descriptions of the world. Consequently, modelling those agents requires representing and reasoning with incomplete and conflicting information, which is beyond the classical logics and monotonic reasoning. Recently, defeasible logic (DL)(1), (2) has attracted consid- erable interest from the research community (3)-(5), especially in application to modelling rational agents (6)-(9). DL is an elegant and computationally efficient tool (10), (11) to deal with partial and conflicting knowledge. The key advantage of DL is being able to draw a plausible conclusion from a reasonable amount of information. In addition, DL provides a compact representation and an effective way to accommodate new information. In this paper, we propose a new modelling technique based on DL to explicitly describe the knowledge that is commonly shared by agents, and that obtained from other agents. The new model enables an agent to reason about the environment and the intentions of other agents. Social actions are achieved by balancing between the desires of an individual and the beliefs of the majority. To tackle multi-source reasoning, we extend the reasoning mechanism of DL with the notion of superior knowledge. This new mechanism allows an agent to integrate its mental attitudes with a more trustworthy source of information such as the knowledge shared by the majority of other agents. In the implementation of our multi-agent system, we add modal notions including Belief, Intention, and Obligation in order to have a fine grained model of "mental attitudes" and social actions. In the rest of this paper, we firstly describe the basic concepts of DL in Section II. We then introduce our modelling technique and discuss discuss how to represent the knowledge base of agents including the meta-knowledge about agents' importance in Section III. Also, we outline the strategies to allow agents to discover approximate "common attitudes" among the group and the mechanism for reasoning with a priority source of knowledge. In Section IV, we present the architecture of our system including the extension of Rule Markup Language as a knowledge representation tool and the algorithm of the reasoning engine. Finally, we provides an overview of research works related to our system in Section V.