Domain-independent planning: representation and plan generation
Artificial Intelligence
A Computational Model of Skill Acquisition
A Computational Model of Skill Acquisition
Planning for Conjunctive Goals
Planning for Conjunctive Goals
Subjective understanding: computer models of belief systems.
Subjective understanding: computer models of belief systems.
GARI: a problem solver that plans how to machine mechanical parts
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
IJCAI'75 Proceedings of the 4th international joint conference on Artificial intelligence - Volume 1
Robotics and Autonomous Systems
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The Machinist program extends domain dependent planning technology. It is modeled after the behavior of human machinists, and makes plan for fabricating metal parts using machine tools. Many existing planning programs rely on a problem solving strategy that involves fixing problems in plans only after they occur. The result is that planning time may be wasted when a bad plan is unnecessarily generated and must be thrown out or modified. The machinist program improves on these methods by looking for cues in the problem specification that may indicate potential difficulties or conflicting goal interactions, before generating any plans. It plans around those difficulties, greatly increasing the probability of producing a good plan on the first try. Planning efficiency is greatly increased when Jalse starts can be eliminated. The machinist program contains about 180 OPS5 rules, and has been judged by experienced machinists to make plans that, are on the average, better than those of a 5 year journeyman. The knowledge that makes the technique effective is domain dependent, but the technique itself can be used in other domains.