Oracle Learning for Agent Negotiation Based on Rationality in Task Reallocation Problems

• Main Authors: Chen, Yi-chun, 陳怡君
• Other Authors: Soo, Von-Wun Soo
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/78264207742641099689

碩士 === 國立清華大學 === 資訊工程學系 === 101 === Task allocation with a contract net protocol is an important issue in multi-agent system. The OCSM contracts protocol has been proposed and it has a good property on that its guarantee of global optimality has already been proved. However, without a proper an oracle to provide guideline of selection of the strategies at proper problem solving situation, the reachability of the optimal allocation solution still has some difficulty. A method to find the oracle, the guide, to agents who can help to reduce the needed number of steps of negotiation that can lead to the optimal allocation solution from any random initial assignment of task allocation is proposed in this thesis. The Oracle Learning method we proposed in this thesis is a method that is divided into several sub-mechanisms, each of which is designed to solve every detailed sub-problem in modeling the task (re)-allocation problem. And we show how each sub-problem can be solved and how the complexity of the optimal solution finding in this problem can be reduced. Then, through experiments, the performance of problem solving, the needed numbers of negotiation steps and the applicability of the method on different scale of problems were evaluated. We conclude the method can really help to get a good result in reducing the needed number of steps of negotiation and can really give a proper negotiation guide in each assignment of task allocation since its sub-mechanisms answers questions that an Oracle needs to answer. Thus, the computational complexity of OCSM negotiation mechanism in task re-allocation problem has a great reduction.