Argumentation and the Dynamics of Warranted Beliefs in Changing Environments

Abstract

One of the most difficult problems in Multi-Agent Systems (MAS) involves representing the knowledge and beliefs of an agent which performs its tasks in a dynamic environment. New perceptions modify this agent's current knowledge about the world, and consequently its beliefs about it also change. Such a revision and update process should be performed efficiently by the agent, particularly in the context of real-time constraints. In the last decade argumentation has evolved as a successful approach to formalize defeasible, commonsense reasoning, gaining wide acceptance in the MAS community by providing tools for designing and implementing features, which characterize reasoning capabilities in rational agents. In this paper we present a new argument-based formalism specifically designed for representing knowledge and beliefs of agents in dynamic environments, called Observation-based Defeasible Logic Programming (ODeLP). A simple but effective perception mechanism allows an ODeLP-based agent to model new incoming perceptions, and modify the agent's knowledge about the world accordingly. In addition, in order to improve the reactive capabilities of ODeLP-based agents, the process of computing beliefs in a changing environment is made computationally attractive by integrating a database with the agent's program, providing pre-compiled information about previous inferences. We present algorithms for managing dialectical databases as well as examples of their use in the context of real-world problems.