The Qualification Problem: A solution to the problem of anomalous models
The Qualification Problem: A solution to the problem of anomalous models
复制标题
资格问题：异常模型问题的解决方案
DOI：
10.1016/s0004-3702(01)00131-x
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
M. Thielscher
中科院分区：
文献类型：
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作者：
M. Thielscher
开放环境中的智能代理不可避免地面临资格问题：永远无法绝对确定地预测动作的可执行性;意外情况（尽管不太可能）可能随时阻止动作的成功执行。现实环境中的推理代理依赖于资格问题的解决方案，以便做出有用的预测，但也要解释和从意外的行动失败中恢复。然而，在这种背景下，今天已知的主要理论结果是否定的：虽然资格问题的解决方案需要默认地假设行为的异常资格，但异常福尔斯的直接最小化却成为产生异常模型的牺牲品。我们提出了一种解决这种异常的资格问题的方法。反常的模型被证明是由于忽略因果关系而产生的，它们可以通过诉诸这个概念来避免。我们的理论建立在已建立的谓词逻辑形式主义的流畅演算作为解决框架问题和分支问题的推理行动。单调流畅演算增强了默认的理论，以获得非单调的方法所要求的资格问题。该方法已在一个基于Fluent演算的动作编程语言中实现，并成功地应用于机器人的高级控制。
Intelligent agents in open environments inevitably face the Qualification Problem: The executability of an action can never be predicted with absolute certainty; unexpected circumstances, albeit unlikely, may at any time prevent the successful performance of an action. Reasoning agents in real-world environments rely on a solution to the Qualification Problem in order to make useful predictions but also to explain and recover from unexpected action failures. Yet the main theoretical result known today in this context is a negative one: While a solution to the Qualification Problem requires to assume away by default abnormal qualifications of actions, straightforward minimization of abnormality falls prey to the production of anomalous models. We present an approach to the Qualification Problem which resolves this anomaly. Anomalous models are shown to arise from ignoring causality, and they are avoided by appealing to just this concept. Our theory builds on the established predicate logic formalism of the Fluent Calculus as a solution to the Frame Problem and to the Ramification Problem in reasoning about actions. The monotonic Fluent Calculus is enhanced by a default theory in order to obtain the nonmonotonic approach called for by the Qualification Problem. The approach has been implemented in an action programming language based on the Fluent Calculus and successfully applied to the high-level control of robots.