Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Stablization of an inverted pendulum by a high-speed fuzzy logic controller hardware system
Fuzzy Sets and Systems - On Applications of Fuzzy Logic Control to Industry
Fuzzy algorithmic control of a model car by oral instructions
Fuzzy Sets and Systems - On Applications of Fuzzy Logic Control to Industry
APPLAUSE: an implementation of the Collins-Michalski theory of plausible reasoning
Information Sciences: an International Journal
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Information Sciences: an International Journal - Special issue on information sciences—past, present, and future
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Proceedings of the first international conference on Principles of knowledge representation and reasoning
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Fuzzy Sets and Systems
Advanced fuzzy logic control of a model car in extreme situations
Fuzzy Sets and Systems
Implementing fuzzy logic controllers using a neural network
Fuzzy Sets and Systems
Backpropagation neural networks for fuzzy logic
Information Sciences: an International Journal
Organizing and Understanding Beliefs in Advice-Giving Diagnostic Systems
IEEE Transactions on Knowledge and Data Engineering
Concepts and fuzzy sets: Misunderstandings, misconceptions, and oversights
International Journal of Approximate Reasoning
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International Journal of Approximate Reasoning
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
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SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
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Autonomous Agents and Multi-Agent Systems
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Artificial Intelligence Review
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This paper investigates the question of which aspects of fuzzy logic are essential to its practical usefulness. We show that as a formal system, a standard version of fuzzy logic collapses mathematically to two-valued logic, while empirically, fuzzy logic is not adequate for reasoning about uncertain evidence in expert systems. Nevertheless, applications of fuzzy logic in heuristic control have been highly successful. We argue that the inconsistencies of fuzzy logic have not been harmful in practice because current fuzzy controllers are far simpler than other knowledge-based systems. In the future, the technical limitations of fuzzy logic can be expected to become important in practice, and work on fuzzy controllers will also encounter several problems of scale already known for other knowledge-based systems.