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μ-satplan: Multi-agent planning as satisfiability

机译:μ-satplan:将多主体计划视为可满足性

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Planning is a fundamental issue in multi-agent systems. In this work we focus on the coordination of multiple agents in two different settings. In the first, agents are able to attain individual goals that are necessary for the achievement of a global common goal. As the agents share the same environment, they need to find a coordinated course of action that avoids harmful (or negative) interactions, and benefit from positive interactions, whenever this is possible. In the second setting some of the agents may need the assistance of other agents to achieve their individual goals. This is the case where some of the actions of the plan of an agent may be executed by another agent who will play the role of the assistant. We formalize these two problems in a more general way than in previous works, and present a coordination algorithm which generates optimal solutions in the case of two agents. In this algorithm, agents use μ-SATPLAN as the underlying planner for generating individual and joint consistent plans. This planner is an extension of the classical SATPLAN planner, that tackles negative and positive interactions and. therefore, multi-agent planning. We also present an algorithm that solves the assistance problem. The underlying algorithm is again μ-SATPLAN, and is used for the generation of individual {based on assistance} and joint consistent plans. Finally experimental results on multi-agent versions of problems taken from International Planning Competitions demonstrate the effectiveness of μ-SATPLAN and the coordination algorithm.
机译:计划是多代理系统中的一个基本问题。在这项工作中,我们专注于在两个不同环境中协调多个业务代表。首先,代理商能够实现实现全球共同目标所必需的个人目标。由于代理共享相同的环境,因此,他们需要找到协调的行动方案,避免有害(或负面)互动,并在可能的情况下从积极互动中受益。在第二种设置中,某些代理商可能需要其他代理商的帮助才能实现其个人目标。在这种情况下,座席计划中的某些动作可能会由另一个扮演助手角色的座席执行。我们以比以前的工作更一般的方式对这两个问题进行形式化,并提出了一种协调算法,该算法可以在两个代理的情况下生成最佳解决方案。在此算法中,代理将μ-SATPLAN用作基础计划器,以生成单个和联合的一致计划。该计划程序是经典SATPLAN计划程序的扩展,它解决了正面和负面的相互作用。因此,多代理计划。我们还提出了一种解决协助问题的算法。底层算法还是μ-SATPLAN,用于生成{基于协助}的个人和联合一致的计划。最后,来自国际计划竞赛的多智能体版本问题的实验结果证明了μ-SATPLAN和协调算法的有效性。

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