An integrated shell and methodology for rapid development of knowledge-based agents

Abstract

This paper introduces the concept of learning agent shell as a new class of tools for rapid development of practical end-to-end knowledge-based agents, by domain experts, with limited assistance from knowledge engineers. A learning agent shell consists of a learning and knowledge acquisition engine as well as an inference engine and supports building an agent with a knowledge base consisting of an ontology and a set of problem solving rules. The paper describes a specific learning agent shell and its associated agent build-ing methodology. The process of developing an agent relies on importing ontologies from existing repositories of knowledge, and on teaching the agent how to perform various tasks, in a way that resembles how an expert would teach a human apprentice when solving problems in cooperation. The shell and methodology represent a practical integration of knowledge representation, knowledge acquisition, learning and problem solving. This work is illustrated with an example of developing a hierarchical non-linear planning agent.