Acquiring Consensus Solutions by Multi-human-agent-based Evolutionary Computation

Hironao Sakamoto, Koutaro Nakamoto, K. Ohnishi
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Abstract

The paper proposes a system to acquire consensus solutions among people to a multiobjective problem which does not require direct communication among the people. It relies on multi-human-agent-based evolutionary computation (Mhab-EC) with a mechanism for making solutions of agents converged. In the system, people prepare their own agents who can be tuned by themselves, and then submit their agents to a simulation of Mhab-EC and acquire a convergence solution from the simulation. Then, examining the simulation result, they tune their agents again to be closer to their desired solutions and submit the agents to the simulation and obtain a convergence solution. The mechanism repeats this procedure pre-determined times and outputs a convergence solution of the final simulation as the consensus solution. In basic evaluation of the system, we focus on if the system can make solution converged and show that it can indeed do it.
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基于多人类智能体进化计算的共识解获取
本文提出了一种不需要直接沟通就能获得多目标问题的共识解决方案的系统。它依赖于基于多人类智能体的进化计算(Mhab-EC),具有使智能体解收敛的机制。在系统中,人们准备自己的可自我调整的智能体,然后将他们的智能体提交给Mhab-EC仿真,并从仿真中获得收敛解。然后,检查仿真结果,再次调整代理,使其更接近期望的解,并将代理提交给仿真并获得收敛解。该机制重复该过程的预定次数,并输出最终仿真的收敛解作为共识解。在系统的基本评价中,我们关注系统是否能使解收敛,并证明它确实能做到。
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