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摘要翻译：
我们描述了一个用于指定动作效果的系统。与人工智能规划中常用的方法不同，我们的系统使用了一种动作描述语言，允许用户使用域规则来指定动作的效果，域规则是状态约束，可以从旧的动作效果中产生新的动作效果。在陈述性上，我们语言中的一个动作域对应于情境演算中的一个非单调因果理论。在程序上，这样一个动作域被编译成一组逻辑理论，域中的每个动作都有一个逻辑理论，然后从这些逻辑理论中生成完全实例化的后继状态类公理和条带类系统。我们期望该系统能成为知识工程师为经典的AI规划系统、GOLOG系统和其他需要动作规范的系统编写动作规范的有用工具。
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英文标题：
《Compiling Causal Theories to Successor State Axioms and STRIPS-Like
Systems》
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作者：
F. Lin
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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：
We describe a system for specifying the effects of actions. Unlike those commonly used in AI planning, our system uses an action description language that allows one to specify the effects of actions using domain rules, which are state constraints that can entail new action effects from old ones. Declaratively, an action domain in our language corresponds to a nonmonotonic causal theory in the situation calculus. Procedurally, such an action domain is compiled into a set of logical theories, one for each action in the domain, from which fully instantiated successor state-like axioms and STRIPS-like systems are then generated. We expect the system to be a useful tool for knowledge engineers writing action specifications for classical AI planning systems, GOLOG systems, and other systems where formal specifications of actions are needed.
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PDF链接：
https://arxiv.org/pdf/1106.4867
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