Domain-independent planning: representation and plan generation
Artificial Intelligence
O-Plan: control in the open planning architecture
Proc. of the fifth technical conference of the British Computer Society Specialist Group on Expert Systems on Expert systems 85
Reasoning about partially ordered events
Artificial Intelligence
A Computational Model of Skill Acquisition
A Computational Model of Skill Acquisition
Admissible criteria for loop control in planning
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Applying clustering techniques to reduce complexity in automated planning domains
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
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Partially ordered plans have not solved the goal ordering problem. Consider: a goal in a partially ordered plan is an operator precondition that is not yet achieved; operators, orderings and variable bindings are introduced to achieve such goals. While the planning community has known how to achieve individual goals for some time, there has been little work on the problem of which one of the many possible goals the planner should achieve next. This paper argues that partially ordered plans do not usefully address the goal-ordering problem and then presents a heuristic called temporal coherence which does. Temporal coherence is an admissible heuristic which provides goal-ordering guidance. Temporal coherence is admissible in the sense that if a solution exists in the planner's search space, then there will be a series of goal achievements permitted by the heuristic which can produce this solution.