Response fusion in multi-agent environment

American Journal of Applied Sciences, Jan, 2009 by Fereshteh-Azadi Parand, Nasrollah Moghaddam Charkari, Sara Afrasyabian

INTRODUCTION

One of the main purpose in agents communication is to achieve the goals better (10). Suppose that in response to a query, each agent can produce a response set which has a level of uncertainty. Uncertainty in agent response may be attributed to two main reasons, namely, deficiency in agent capability and the ambiguity of application information. The first aspect is generally due to agent design/implementation deficiency. The second aspect is due to information overload. For example, in an environment such as the Internet, properties like inaccessibility, non-deterministic and dynamic nature of the information space are sources of agent imprecise decisions. Our goal is the utilization of the other agents in order to reduce the uncertainty and consequently to obtain response with higher quality. For this purpose, the query should propagate in multi-agent environment and agents' response to be aggregate.

For aggregation, these issues should be considered. Agent responses have different degree of accuracy, so final response should be a function of each agent response with considering the degree of credibility. In other words, the opinion of agent with higher credibility has more effect on final answer and vice versa.

For each agent response, the degree of its credibility should be determined. In order to fuse agent responses, we need an operator with considering the problem of credibility degree of resources.

Credibility assignment can be user generated or sanctioned knowledge base (2). In sanction based system a central agent decides about the credibility of the other agents. As there is no centralized element in multi-agent environment, sanctioned knowledge base methods might not be used. In our suggested method, the opinion of agents' community is used to assign the credibility to each agent. There are currently some works based on weighting and probability theory for credibility assignment (4), (12), (13), (14), (15). Also bayesian (5), (6) and fuzzy approach (7), (8) are used for credibility assessment. Also in our work the possibility of credibility is assigned to each agent, not the probability of credibility. As the credibility, value assigned by each agent to the other agents is an approximate value and with some uncertainty, assigning credibility possibility is more appropriate than credibility probability.

In the next step, we assume that when an agent receives a query, it propagates the query to the other agents to attain their abilities and opinions. After receiving a query, each agent generates a fuzzy answer set. We need a fusion operator, which considers agent credibility to fuse these answers sets with considering agent credibility. For information fusion by considering source credibility, an operator is suggested by Yager (13) and Prade (9). A problem with this operator applying to fuse agent answers is that the operator on the final decisions does not reflect the low credibility of all agents within the multi-agent environment. Therefore, an environment with low creditable decision makers cannot be distinguished from the ones with highly creditable agents. Consequently, a comparison of agent community response is not possible. To resolve this problem, with considering the credibility of the agents, an improved version of this decision fusion operator based upon the assumption that each agent generates a fuzzy decision set is presented.

MATERIALS AND METHODS

Multi-agent environment is an infrastructure that enables collaborative decision-making. Decision makers may have different degrees of credibility. I A = {[A.sub.1], [A.sub.2], [A.sub.3],.., [A.sub.N]}. Each of these agents collects information from its accessible knowledge resources and has special capability of decision-making. In this regard, each agent, such as [A.sub.i], defines an assigned credibility possibility distribution for each subject.

[AsscrePoss.sub.i] = [[union]crePosss.sub.ij]

where, [crePosss.sub.ij] is the degree of credibility possibility assigned by the ith agent to the jth one, while:

0[less than or equal to][crePoss.sub.ij][union][crePoss.sub.i2][union]...[union][crePoss.sub.in][less than or equal to]1[for all]D[M.sub.i][euro]DM

[DP.sub.N] is the Nth decision problem, Dk is the decision which is made for [DP.sub.N] and [R.sub.N] is the membership degree of [D.sub.K] To decision set which is made against [DP.sub.N]

The [crePoss[s.sub.ij]] values for each subject are kept in a matrix called the credibility matrix for each subject, as shown in Fig. 1.

[FIGURE 1 OMITTED]

CREDIBILITY ASSIGNMENT TO AGENTS

After the matrix CrePoss is established, the credibility possibility of each agent crePosij, should be influenced by the opinions of the other agents.

Credibility assignment to each agent is defined by a fuzzy relation implemented as a max-min composition. At each instance of time, t+1, the max-min composition influences the opinion of each agent, [crePoss.sub.ij.sup.t+1]" by the others opinions as follows: Suppose there is a group of N agents in multi-agent environment, indexed by the set