Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
O-Plan: the open planning architecture
Artificial Intelligence
Omnipotence without omniscience: efficient sensor management for planning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Heterogeneous active agents, I: semantics
Artificial Intelligence
State-space planning by integer optimization
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Heterogeneous active agents, III: polynomially implementable agents
Artificial Intelligence
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
IMPACTing SHOP: Putting an AI Planner Into a Multi-Agent Environment
Annals of Mathematics and Artificial Intelligence
The LPSAT Engine & Its Application to Resource Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
SHOP: simple hierarchical ordered planner
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
SiN: integrating case-based reasoning with task decomposition
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Monitoring agents using declarative planning
Fundamenta Informaticae
Monitoring Agents using Declarative Planning
Fundamenta Informaticae - The 1st International Workshop on Knowledge Representation and Approximate Reasoning (KR&AR)
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We give the theoretical foundations and empirical evaluation of a planning agent, ashop, performing HTN planning in a multi-agent environment. ashop is based on ASHOP, an agentised version of the original SHOP HTN planning algorithm, and is integrated in the IMPACT multi-agent environment. We ran several experiments involving accessing various distributed, heterogeneous information sources, based on simplified versions of noncombatant evacuation operations, NEO's. As a result, we noticed that in such realistic settings the time spent on communication (including network time) is orders of magnitude higher than the actual inference process. This has important consequences for optimisations of such planners. Our main results are: (1) using NEO's as new, more realistic benchmarks for planners acting in an agent environment, and (2) a memoization mechanism implemented on top of shop, which improves the overall performance considerably.