Agent群体中的个体规划：利用关联性和框架动作超图
Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs
摘要：
交互式部分可观察马尔可夫决策过程（I-POMDP）提供了一个正式的框架规划的多智能体设置中的一个自我利益的代理。在多智能体环境中运行的智能体必须考虑其他智能体可能采取的行动以及这些行动对环境的影响和它所获得的奖励。传统的I-POMDP模型依赖于其他代理的行动，使用联合行动和模型空间。因此，解决方案的复杂性随着代理的数量呈指数级增长，从而使可扩展性变得复杂。在本文中，我们的模型和扩展的匿名性和上下文特定的独立性-问题结构往往存在于代理人口-计算增益。我们实证证明了利用这些问题结构的效率，通过解决一个新的多智能体问题，涉及1,000多个代理。
英文摘要：
Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent operating in a multiagent environment must deliberate about the actions that other agents may take and the effect these actions have on the environment and the rewards it receives. Traditional I-POMDPs model this dependence on the actions of other agents using joint action and model spaces. Therefore, the solution complexity grows exponentially with the number of agents thereby complicating scalability. In this paper, we model and extend anonymity and context-specific independence -- problem structures often present in agent populations -- for computational gain. We empirically demonstrate the efficiency from exploiting these problem structures by solving a new multiagent problem involving more than 1,000 agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
