Epistemic Logic and Planning
Epistemic Logic and Planning
复制标题
认知逻辑和规划
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
I. Watson
中科院分区：
文献类型：
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作者：
S. Maghsoudi;I. Watson
根据所解决问题的类型，人工智能算法可以分为两组。知识密集型领域包含显性知识，而知识贫乏领域包含隐性知识。逻辑方法更适合于第一种类型。神经网络和基于案例的推理(CBR)更适合于第二种类型。该项目将认知逻辑(类型1)在CBR适应阶段的推理能力与基于案例的规划(类型2)的性能相结合。这种方法被证明比单独使用规划算法更有效。规划算法在计算上是昂贵的。使用CBR，使用最近邻算法(KNN)来加快该过程。带材计划员为工厂中运送零件的机器人的案例库创建计划。管理者定义问题，KNN提取计划，逻辑子系统根据信念修正定理调整计划以解决计划不一致问题。
Artificial Intelligence algorithms can be divided into two groups according to the type of problems they solve. Knowledge-intensive domains contain explicit knowledge, whereas knowledge-poor domains contain implicit knowledge. Logical methods are more suitable for the first type. Neural networks and case-based reasoning (CBR) are more suitable for the second type. This project combines the inferencing power of epistemic logic (type 1) in the adaptation phase of CBR with the performance of case-based planning (type 2). This method is proved to be more efficient then using planning algorithms alone. Planning algorithms are computationally expensive. CBR, using a nearest neighbor algorithm (KNN) is used to make the process faster. A STRIPS planner creates plans for the case-base of a robot that delivers parts in a factory. The manager defines the problem, KNN extracts a plan and a logic sub-system adapts it according to belief revision theorems to resolve the plan inconsistencies.