摘要翻译：
近年来，模型检测、表示和搜索技术被证明可以有效地应用于规划，特别是非确定性规划。这种规划方法利用有序二元决策图将规划域编码为一个非确定性的有限自动机，然后应用模型检测中的快速算法来搜索解。OBDDs可以有效地进行扩展，为复杂的规划领域提供通用的规划。我们特别感兴趣的是解决在非确定性、多智能体领域中出现的复杂性。在本文中，我们提出了一个新的通用的基于OBDD的非确定性多Agent规划框架UMOP。我们引入了一种新的规划领域描述语言NADL来描述非确定性的多Agent领域。该语言提供了可控代理和不可控环境代理的明确定义。我们描述了NADL的语法和语义，并展示了如何构建一个高效的基于OBDD的NADL描述表示。UMOP规划系统使用了NADL和不同的基于OBDD的通用规划算法。它包括以前开发的强和强循环规划算法。此外，我们引入了一种新的乐观规划算法，该算法放宽了最优性保证，并在一些不存在强或强循环解的域中生成了似是而非的通用规划。我们给出了将UMOP应用于从确定性和无环境行为的单Agent到具有复杂环境行为的非确定性和多Agent领域的实证结果。UMOP是一个丰富而高效的规划系统。
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英文标题：
《OBDD-based Universal Planning for Synchronized Agents in
  Non-Deterministic Domains》
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作者：
R. M. Jensen, M. M. Veloso
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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：
  Recently model checking representation and search techniques were shown to be efficiently applicable to planning, in particular to non-deterministic planning. Such planning approaches use Ordered Binary Decision Diagrams (OBDDs) to encode a planning domain as a non-deterministic finite automaton and then apply fast algorithms from model checking to search for a solution. OBDDs can effectively scale and can provide universal plans for complex planning domains. We are particularly interested in addressing the complexities arising in non-deterministic, multi-agent domains. In this article, we present UMOP, a new universal OBDD-based planning framework for non-deterministic, multi-agent domains. We introduce a new planning domain description language, NADL, to specify non-deterministic, multi-agent domains. The language contributes the explicit definition of controllable agents and uncontrollable environment agents. We describe the syntax and semantics of NADL and show how to build an efficient OBDD-based representation of an NADL description. The UMOP planning system uses NADL and different OBDD-based universal planning algorithms. It includes the previously developed strong and strong cyclic planning algorithms. In addition, we introduce our new optimistic planning algorithm that relaxes optimality guarantees and generates plausible universal plans in some domains where no strong nor strong cyclic solution exists. We present empirical results applying UMOP to domains ranging from deterministic and single-agent with no environment actions to non-deterministic and multi-agent with complex environment actions. UMOP is shown to be a rich and efficient planning system.
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PDF链接：
https://arxiv.org/pdf/1106.0229