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Detecting, Classifying, and Handling Contradictions in a Large, Dynamic Information Environment

机译：在大型动态信息环境中检测，分类和处理矛盾

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A new approach to perturbation tolerance was identified -- the Meta- Cognitive Loop (MCL) -- for responding to contradictions and other anomalies in complex settings. Further investigations with MCL included identifying architectural requirements, and applying MCL to various domains including reinforcement learning, common-sense reasoning, and a task-oriented natural- language interface system. A series of experiments empirically demonstrated the efficacy of MCL in improving the perturbation tolerance of certain machine learning techniques, including Q-learning, SARSA and Prioritized Sweeping. Formal metrics were given for measuring the complexity, dynamicity and overall difficulty of test domains, which allow for derivative measures of perturbation tolerance. A semantics was developed for Active Logic -- the underlying logic on which MCL's contradiction handling is based -- in the propositional case.

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