DMCA
PLANNING BY EXAMINING DECOMPOSITIONAL PLAN TRACES (2006)
BibTeX
@MISC{Saraskanroud06planningby,
author = {Okhtay Ilghami Saraskanroud and Doctor Of and Okhtay Ilghami Saraskanroud},
title = {PLANNING BY EXAMINING DECOMPOSITIONAL PLAN TRACES},
year = {2006}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Knowledge-based, hand-tailorable planning systems are the most promising planning systems to solve real-wold planning problems. A great challenge in using any such knowledge-based planning system in real world is the difficulty of acquiring the domain knowledge needed to guide such a system. The objective of this disser-tation is to investigate ways to acquire parts or all of this information automatically in the context of Hierarchical Task Network (HTN) planning. Knowledge acquisi-tion is done by examining the decisions made by an expert problem solver while solving planning problems in a given HTN domain. This dissertation first describes a general framework to define what an HTN domain learner is, what its inputs and outputs are, how to evaluate such a learner, and what soundness, completeness and convergence mean in this context. Afterwards, two different HTN domain learning algorithms CaMeL and HDL are discussed. These two algorithms are then extended to handle noise in training samples, and to help HTN planners to start planning before full convergence is achieved by CaMeL or HDL. These extensions result in a family of different HTN domain learning algorithms, each of which can be useful un-
Keyphrases
planning examining decompositional plan trace different htn domain htn domain general framework expert problem solver hand-tailorable planning system domain knowledge knowledge acquisi-tion real-wold planning problem convergence mean great challenge hierarchical task network algorithm camel full convergence training sample htn planner htn domain learner real world knowledge-based planning system
