Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Constraint propagation with interval labels
Artificial Intelligence
Representations of commonsense knowledge
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Problems of interval-based qualitative reasoning
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Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Integrating rules and connectionism for robust commonsense reasoning
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CYC: a large-scale investment in knowledge infrastructure
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Advances in Automatic Text Summarization
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Formalizing Commonsense: Papers by John McCarthy
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A Constraint-Based Fuzzy Inference System
EPIA '91 Proceedings of the 5th Portuguese Conference on Artificial Intelligence
Approximate and Commonsense Reasoning: From Theory to Practice
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Fuzzy logic to cope with complex problems: some examples of real-world applications
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
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Humans have a remarkable capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Familiar examples are parking a car, driving in city traffic, playing golf, cooking a meal, and summarizing a story. In performing such tasks, humans use perceptions of time, direction, speed, shape, possibility, likelihood, truth, and other attributes of physical and mental objects. Reflecting the bounded ability of the human brain to resolve detail, perceptions are intrinsically imprecise. In more concrete terms, perceptions are f-granular, meaning that (1) the boundaries of perceived classes are unsharp and (2) the values of attributes are granulated, with a granule being a clump of values (points, objects) drawn together by indistinguishability, similarity, proximity, and function. For example, the granules of age might be labeled very young, young, middle aged, old, very old, and so on.F-granularity of perceptions puts them well beyond the reach of traditional methods of analysis based on predicate logic or probability theory. The computational theory of perceptions (CTP), which is outlined in this article, adds to the armamentarium of AI a capability to compute and reason with perception-based information. The point of departure in CTP is the assumption that perceptions are described by propositions drawn from a natural language; for example, it is unlikely that there will be a significant increase in the price of oil in the near future.In CTP, a proposition, p, is viewed as an answer to a question, and the meaning of p is represented as a generalized constraint. To compute with perceptions, their descriptors are translated into what is called the generalized constraint language (GCL). Then, goal-directed constraint propagation is utilized to answer a given query. A concept that plays a key role in CTP is that of precisiated natural language (PNL).The computational theory of perceptions suggests a new direction in AI--a direction that might enhance the ability of AI to deal with real-world problems in which decision-relevant information is a mixture of measurements and perceptions. What is not widely recognized is that many important problems in AI fall into this category.