Planning as search: a quantitative approach
Artificial Intelligence
Theory and algorithms for plan merging
Artificial Intelligence
Partial-order planning: evaluating possible efficiency gains
Artificial Intelligence
Learning search control knowledge to improve plan quality
Learning search control knowledge to improve plan quality
Accelerating Partial Order Planners by Improving Plan and Goal Choices
Accelerating Partial Order Planners by Improving Plan and Goal Choices
A heuristic technique for multi-agent planning
Annals of Mathematics and Artificial Intelligence
Beyond the Plan-Length Criterion
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
Answer Set Planning under Action Costs
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Answer set planning under action costs
Journal of Artificial Intelligence Research
A linear programming heuristic for optimal planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
An agent-oriented approach to change propagation in software maintenance
Autonomous Agents and Multi-Agent Systems
| Hi-index | 0.00 |
We present a cost-directed heuristic planning algorithm, which uses an A* strategy for node selection. The heuristic evaluation function is computed by a deep lookahead that calculates the cost of complete plans for a set of pre-defined top-level subgoals, under the (generally false) assumption that they do not interact. This approach leads to finding low-cost plans, and in many circumstances it also leads to a significant decrease in total planning time. This is due in part to the fact that generating plans for subgoals individually is often much less costly than generating a complete plan taking interactions into account, and in part to the fact that the heuristic can effectively focus the search. We provide both analytic and experimental results.