Stabilization of Cooperative Information Agents in Unpredictable Environment: A Logic Programming Approach

TL;DR

This paper models cooperative information agents as deductive databases and investigates their ability to stabilize and converge to correct information in unpredictable environments, highlighting conditions for guaranteed stabilization.

Contribution

It introduces a logic programming framework for analyzing stabilization of cooperative agents and identifies conditions under which convergence is assured.

Findings

Stabilization is not guaranteed in general.

Sufficient conditions for stabilization are provided.

Convergence to a 'superagent' behavior is analyzed.

Abstract

An information agent is viewed as a deductive database consisting of 3 parts: an observation database containing the facts the agent has observed or sensed from its surrounding environment, an input database containing the information the agent has obtained from other agents, and an intensional database which is a set of rules for computing derived information from the information stored in the observation and input databases. Stabilization of a system of information agents represents a capability of the agents to eventually get correct information about their surrounding despite unpredictable environment changes and the incapability of many agents to sense such changes causing them to have temporary incorrect information. We argue that the stabilization of a system of cooperative information agents could be understood as the convergence of the behavior of the whole system toward the behavior of a "superagent", who has the sensing and computing capabilities of all agents combined. We show that unfortunately, stabilization is not guaranteed in general, even if the agents are fully cooperative and do not hide any information from each other. We give sufficient conditions for stabilization and discuss the consequences of our results.