Reasoning about complex agent knowledge - Ontologies, Uncertainty, rules and beyond

Abstract

Agent-based technology is one of the most vibrant and important areas of research and development that have emerged in information technology in recent years. An intelligent agent is an autonomous entity which observes and acts upon an environment and directs its activity towards achieving goals.

The distinguishing characteristics of intelligent agents are that they are autonomous, responsive, proactive and social. The key features of intelligent agents that has made them so is that intelligent agents have their knowledge of the world and themselves and that they have the capability to make deductions. Hence it is our belief that knowledge representation and reasoning is one of the most important research areas in agent-based technologies.

In the current stage, we have identified four challenges related to the field of agent knowledge representation and reasoning. (1) The interoperability and heterogeneity problem is how agents with different domains of discourse, employing different problem solving paradigms, and with different assumptions about their world and each other, can be made to interact in an effective and scalable manner. (2) As agents have a necessarily partial perspective of their world, and because their problem domain is open, complex and distributed, they require sophisticated mechanisms for reasoning with uncertain, incomplete and contradictory information. (3) Rules are natural means to specify reactive and possibly proactive behavior. It is a challenge for agents to perform reasoning on and with such rules. (4) The knowledge of an intelligent agent typically deals with what agents consider possible given their current information. This includes knowledge about facts as well as higher-order information about information that other agents have. It is a challenging task to enable systematic design of such intelligent agents as the reasoning process of interacting agents can be extremely complex.

This thesis presents our contribution to the solutions to the challenges. More specifically we employ a formal modeling approach to verifying ontology-based agent knowledge. We also extend the current state-of-the-art ontology language with the ability to model certainty factors about facts and proposed the corresponding reasoning algorithms. We define a set of notion for the quality of agent rule base and provide an automated checking mechanism. Lastly we present a formal hierarchical framework for specifying and reasoning about higher-order agent knowledge.