Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
SOAR: an architecture for general intelligence
Artificial Intelligence
Automated generation of model-based knowledge acquisition tools
Automated generation of model-based knowledge acquisition tools
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
An experimental study of criteria for hypothesis plausibility
Journal of Experimental & Theoretical Artificial Intelligence
Designing interaction
The computational complexity of abduction
Artificial Intelligence - Special issue on knowledge representation
Probabilistic similarity networks
Probabilistic similarity networks
Coping with the complexities of multiple-solution problems: a case study
International Journal of Man-Machine Studies
Cognitively plausible heuristics to tackle the computational complexity of abductive reasoning
Cognitively plausible heuristics to tackle the computational complexity of abductive reasoning
Computers and Biomedical Research
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Complexity results for planning
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Modelling diagnostic skills in the domain of skeletal dysplasias
Computer Methods and Programs in Biomedicine
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If our goal in Artificial Intelligence in Medicine (AIM) is to engineer systems health-care providers will both use and, in the process, improve their performance, we must concentrate on the development of causal theories of knowledge and problem solving. One broad direction in pursuing this goal is understanding the relationships between existing models of rationality and bounded rationality for similar tasks. Models of rationality refer to those approaches in which the optimal properties of the models are deductively provable, i.e. in which the processing is rational. Representative models of rationality used in AIM are deductive logical models, statistical models such as Bayesian inference models, and decision-analytic models. Models of bounded rationality are those which do not guarantee such optimal properties nor yield to deductive correctness proofs. These models have their roots in cognitive psychology. In this article we show how explicating the relationship between models of rationality and bounded rationality might be done in the case of abductive tasks in medicine. This is done by positioning these modeling approaches within the same framework (an abstract computational model) and interpreting in this context both computational complexity results concerning the nature of the task and empirical results from studies of human problemsolving behavior.