Planning and Acting in Incomplete Domains

Engineering complete planning domain descriptions is often very costly because of human error or lack of domain knowledge. Learning complete domain descriptions is also very challenging because many features are irrelevant to achieving the goals and data may be scarce. We present a planner and agent that respectively plan and act in incomplete domains by i) synthesizing plans to avoid execution failure due to ignorance of the domain model, and ii) passively learning about the domain model during execution to improve later re-planning attempts. Our planner DeFault is the first to reason about a domain’s incompleteness to avoid potential plan failure. DeFault computes failure explanations for each action and state in the plan and counts the number of interpretations of the incomplete domain where failure will occur. We show that DeFault performs best by counting prime implicants (failure diagnoses) rather than propositional models. Our agent Goalie learns about the preconditions and ...

• Type: Journal
• Year: 2011
• Where: AIPS
• Authors: Christopher Weber, Daniel Bryce
• Keywords: AIPS 2011, Artificial Intelligence, Domain Descriptions, Domain Model, Incomplete Domain