论文标题
多代理团队对抗自然的知识策略
Knowledge-Based Strategies for Multi-Agent Teams Playing Against Nature
论文作者
论文摘要
我们研究代理团队，这些代理商反对大自然，以实现共同的目标。由于部分可观察性，假定代理商具有不完美的信息，并且在游戏游戏期间没有通信。我们提出了对代理高级知识的自然概念。基于这个概念，我们定义了一类基于知识的策略，并考虑了该类别策略的综合问题。我们介绍了应用于此类游戏的众所周知的基于知识的子集结构的多代理扩展名MKBSC。它的迭代应用结果是计算代理的高阶知识。我们展示了如何将MKBSC用于设计基于知识的策略概况的设计，并根据某些自然假设调查了原始游戏和MKBSC的迭代应用之间存在此类策略的存在。我们还将基于明确的知识表示和更新的基于知识的策略的“强度”视图与基于有限传感器的有限内存策略的“扩展”视图进行了比较，并表明从某种意义上说，这些观点是等效的。
We study teams of agents that play against Nature towards achieving a common objective. The agents are assumed to have imperfect information due to partial observability, and have no communication during the play of the game. We propose a natural notion of higher-order knowledge of agents. Based on this notion, we define a class of knowledge-based strategies, and consider the problem of synthesis of strategies of this class. We introduce a multi-agent extension, MKBSC, of the well-known Knowledge-Based Subset Construction applied to such games. Its iterative applications turn out to compute higher-order knowledge of the agents. We show how the MKBSC can be used for the design of knowledge-based strategy profiles and investigate the transfer of existence of such strategies between the original game and in the iterated applications of the MKBSC, under some natural assumptions. We also relate and compare the "intensional" view on knowledge-based strategies based on explicit knowledge representation and update, with the "extensional" view on finite memory strategies based on finite transducers and show that, in a certain sense, these are equivalent.
