TY - GEN
T1 - Using action benefits and plan certainties in multiagent problem solving
AU - Sugawara, Toshiharu
PY - 1993
Y1 - 1993
N2 - Choosing socially coherent and rational actions is essential in multiagent problem solving. In some domains, exchanging agents' plans are helpful for understanding what are rational actions. If they has little shared knowledge or environment, however, it is hard to understand other agents' plans. This paper discusses the utility-based cooperation for this situation. A utility matrix are created based on the local plans and through communications with other agents instead of exchanging plans. Utility numbers are calculated according to action benefits and plan certainties. Intuitively, an action benefit expresses the importance of performing or verifying the current plan, and a plan certainty expresses how strongly the agent making the plan believes that it is correct or effective for the current problem solving. Actions based on a plan supported by many proofs have high utility-numbers and so are priority over other actions. Finally, we will show how the performance can be improved by our method through experiments.
AB - Choosing socially coherent and rational actions is essential in multiagent problem solving. In some domains, exchanging agents' plans are helpful for understanding what are rational actions. If they has little shared knowledge or environment, however, it is hard to understand other agents' plans. This paper discusses the utility-based cooperation for this situation. A utility matrix are created based on the local plans and through communications with other agents instead of exchanging plans. Utility numbers are calculated according to action benefits and plan certainties. Intuitively, an action benefit expresses the importance of performing or verifying the current plan, and a plan certainty expresses how strongly the agent making the plan believes that it is correct or effective for the current problem solving. Actions based on a plan supported by many proofs have high utility-numbers and so are priority over other actions. Finally, we will show how the performance can be improved by our method through experiments.
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M3 - Conference contribution
AN - SCOPUS:0027150559
SN - 0818638400
T3 - Proceedings of the Conference on Artificial Intelligence Applications
SP - 407
EP - 413
BT - Proceedings of the Conference on Artificial Intelligence Applications
PB - Publ by IEEE
T2 - Proceedings of the 9th Conference on Artificial Intelligence for Applications
Y2 - 1 March 1993 through 5 March 1993
ER -