Cross-Element Validation in Multiagent-based Simulation: Switching Learning Mechanisms in Agents
Cross-Element Validation in Multiagent-based Simulation: Switching Learning Mechanisms in Agents
复制标题
基于多智能体的仿真中的跨元素验证：智能体中的切换学习机制
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
K. Shimohara
中科院分区：
文献类型：
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作者：
K. Takadama;Yutaka I. Leon;Norikazu Sugimoto;N. Nawa;K. Shimohara
模拟结果的有效性仍然是基于多主体的模拟（MABS）中的一个悬而未决的问题。由于这种有效性基于计算模型的验证，因此我们提出了一种跨元素验证方法，通过研究在更改代理架构中的元素后多个模型是否可以产生相同的结果来验证计算模型。具体来说，本文重点关注应用于代理的学习机制作为重要元素之一，并比较了采用进化策略（ES）、学习分类器系统（LCS）或强化学习（RL）的三种不同的 MABS。这种类型的验证不是基于传统研究中解决的模型间，而是基于模型内。对讨价还价游戏（博弈论的基本例子之一）中的模拟结果进行比较表明，（1）计算模型在基于 ES 和 RL 的代理的情况下得到了最低程度的验证； （2）使主体能够获得理性行为的学习机制根据主体的知识表示（即讨价还价游戏中的策略）而有所不同。具体来说，我们发现（2-a）基于ES的智能体在博弈论中得出了相同的趋势，但在采用连续知识表示的情况下基于LCS的智能体却不能； (2-b)相同的基于 ES 的智能体不能在博弈论中得出相同的趋势，但基于 RL 的智能体在采用离散知识表示的情况下得出它。
The validity of simulation results remains an open problem in multiagent-based simulation (MABS). Since such validity is based on the validation of computational models, we propose a cross-element validation method that validates computational models by investigating whether several models can produce the same results after changing an element in the agent architecture. Specifically, this paper focuses on learning mechanisms applied to agents as one of the important elements and compares three different MABSs employing either an evolutionary strategy (ES), a learning classifier system (LCS), or a reinforcement learning (RL). This type of validation is not based on the between -models addressed in conventional research but on a within -model. A comparison of the simulation results in a bargaining game, one of the fundamental examples in game theory, reveals that (1) computational models are minimally validated in the case of ES- and RL-based agents; and (2) learning mechanisms that enable agents to acquire their rational behaviors differ according to the knowledge representation ( i.e. , the strategies in the bargaining game) of the agents. Concretely, we found that (2-a) the ES-based agents derive the same tendency in game theory but the LCS-based agents cannot in the case of employing continuous knowledge representation; and (2-b) the same ES-based agents cannot derive the same tendency in game theory but the RL-based agents derive it in the case of employing discrete knowledge representation.