Date of Award:

2012

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Advisor/Chair:

Dr. Daniel L. Bryce

Abstract

The engineering of 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. Given incomplete knowledge of their actions, agents can ignore the incompleteness, plan around it, ask questions of a domain expert, or learn through trial and error.

Our agent Goalie learns about the preconditions and effects of its incompletely-specified actions by monitoring the environment state. In conjunction with the plan failure explanations generated by its planner DeFault, Goalie diagnoses past and future action failures. DeFault computes failure explanations for each action and state in the plan and counts the number of incomplete domain interpretations wherein failure will occur. The questionasking strategies employed by our extended Goalie agent using these conjunctive normal form-based plan failure explanations are goal-directed and attempt to approach always successful execution while asking the fewest questions possible. In sum, Goalie:
i) interleaves acting, planning, and question-asking;
ii) synthesizes plans that avoid execution failure due to ignorance of the domain model;
iii) uses these plans to identify relevant (goal-directed) questions;
iv) passively learns about the domain model during execution to improve later replanning attempts;
v) and employs various targeted (goal-directed) strategies to ask questions (actively learn).

Our planner DeFault is the first reason about a domain's incompleteness to avoid potential plan failure. We show that DeFault performs best by counting prime implicants (failure diagnoses) rather than propositional models. Further, we show that by reasoning about incompleteness in planning (as opposed to ignoring it), Goalie fails and replans less often, and executes fewer actions. Finally, we show that goal-directed knowledge acquisition - prioritizing questions based on plan failure diagnoses - leads to fewer questions, lower overall planning and replanning time, and higher success rates than approaches that naively ask many questions or learn by trial and error.

Comments

This work made publicly available electronically on April 12, 2012.

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