Detecting, Classifying, and Handling Contradictions in a Large, Dynamic Information Environment
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 11, 2006
- Accession Number
- ADA457343
Entities
People
- Darsana Josyula
- Donald Perlis
- Michael Anderson
- Scott Fults
- Waiyian Chong
Organizations
- University of Maryland