Test-score semantics as a basis for a computational approach to the representation of meaning
Literary & Linguistic Computing
Mathematical logic in artificial intelligence
The artificial intelligence debate: false starts, real foundations
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
An analysis of first-order logics of probability
Artificial Intelligence
Logic and artificial intelligence
Artificial Intelligence
Rigor mortis: a response to Nilsson's “logic and artificial intelligence”
Artificial Intelligence
Intelligence without representation
Artificial Intelligence
What is a logical system?
Non-axiomatic reasoning system: exploring the essence of intelligence
Non-axiomatic reasoning system: exploring the essence of intelligence
Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought
Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
A defect in Dempster-Shafer theory
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Belief revision in probability theory
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Three fundamental misconceptions of Artificial Intelligence
Journal of Experimental & Theoretical Artificial Intelligence
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An experience-grounded semantics is introduced for an intelligent reasoning system, which is adaptive, and works with insufficient knowledge and resources. According to this semantics, truth and meaning are defined with respect to the experience of the system - the truth value of a statement indicates the amount of available evidence, and the meaning of a term indicates its experienced relations with other terms. The major difference between experience-grounded semantics and model-theoretic semantics is that the former does not assume the sufficiency of knowledge and resources. This approach provides new ideas to the solution of some important problems in cognitive science.