Toward Incremental Knowledge Correction for Agents in Complex Environments

In complex, dynamic environments, an agent's domain knowledge will rarely be complete and correct. Existing deliberate approaches to domain theory correction are signi cantly restricted in the environments where they can be used. These systems are typically not used in agent-based tasks and rely on declarative representations to support non-incremental learning. This research investigates the use of procedural knowledge to support deliberate incremental error correction in complex environments. We describe a series of domain properties that constrain the error correction process and that are violated by existing approaches. We then present a procedural representation for domain knowledge which is su ciently expressive, yet tractable. We develop a general framework for error detection and correction and then describe an error correction system, IMPROV, that uses our procedural representation to meet the constraints imposed by complex environments. Finally, we test the system in two sample domains and empirically demonstrate that it satis es many of the constraints faced by agents in complex and challenging environments.