Heterogeneous active agents, I: semantics
Artificial Intelligence
IMPACTing SHOP: Putting an AI Planner Into a Multi-Agent Environment
Annals of Mathematics and Artificial Intelligence
SiN: integrating case-based reasoning with task decomposition
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
IMPACTing SHOP: Putting an AI Planner Into a Multi-Agent Environment
Annals of Mathematics and Artificial Intelligence
Hi-index | 0.00 |
We give the theoretical foundations and empirical evaluation of a planning agent, shop, performing HTN planning in a multi-agent environment. shop is based on A-SHOP, an agentized 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 optimizations 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 in a significant way.