摘要翻译：
尽管在多Agent团队协作方面取得了重大进展，但现有的研究并没有解决其处方的最优性，也没有解决团队协作问题的复杂性。如果没有最优性-复杂性折衷的表征，就不可能确定某一特定理论所做的假设和近似是否获得足够的效率来证明总体性能的损失是合理的。为了给多Agent研究人员提供一个评估这种权衡的工具，我们提出了一个统一的框架--通信多Agent团队决策问题（COM-MTDP）。COM-MTDP模型结合和扩展了现有的多Agent理论，如分散的部分可观测马尔可夫决策过程和经济团队理论。COM-MTDPs除了具有一般性外，还支持对团队绩效最优性和Agent决策问题计算复杂度的分析。在复杂性分析中，我们给出了在各种问题域下构造最优团队的计算复杂性的分解，沿着可观测性和通信代价维度。在分析最优性时，我们利用COM-MTDP对现有的团队合作理论和模型进行编码的能力，对文献中的联合意图理论的两个实例进行编码。此外，COM-MTDP模型为开发新的团队协调算法提供了基础。我们导出了一个与域无关的最优通信准则，并给出了关于该最优策略的两个联合意图实例的比较分析。我们基于COM-MTDPs实现了一个可重用的、与领域无关的软件包来分析团队协作策略，并在一个示例域中通过对两种联合意图策略的编码和评估来演示该软件包的使用。
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英文标题：
《The Communicative Multiagent Team Decision Problem: Analyzing Teamwork
  Theories and Models》
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作者：
D. V. Pynadath, M. Tambe
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Without a characterization of the optimality-complexity tradeoffs, it is impossible to determine whether the assumptions and approximations made by a particular theory gain enough efficiency to justify the losses in overall performance. To provide a tool for use by multiagent researchers in evaluating this tradeoff, we present a unified framework, the COMmunicative Multiagent Team Decision Problem (COM-MTDP). The COM-MTDP model combines and extends existing multiagent theories, such as decentralized partially observable Markov decision processes and economic team theory. In addition to their generality of representation, COM-MTDPs also support the analysis of both the optimality of team performance and the computational complexity of the agents' decision problem. In analyzing complexity, we present a breakdown of the computational complexity of constructing optimal teams under various classes of problem domains, along the dimensions of observability and communication cost. In analyzing optimality, we exploit the COM-MTDP's ability to encode existing teamwork theories and models to encode two instantiations of joint intentions theory taken from the literature. Furthermore, the COM-MTDP model provides a basis for the development of novel team coordination algorithms. We derive a domain-independent criterion for optimal communication and provide a comparative analysis of the two joint intentions instantiations with respect to this optimal policy. We have implemented a reusable, domain-independent software package based on COM-MTDPs to analyze teamwork coordination strategies, and we demonstrate its use by encoding and evaluating the two joint intentions strategies within an example domain.
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PDF链接：
https://arxiv.org/pdf/1106.4569