Computational philosophy of science
Computational philosophy of science
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
What computers still can't do: a critique of artificial reason
What computers still can't do: a critique of artificial reason
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
Artificial intelligence and scientific method
Artificial intelligence and scientific method
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
On the relation between abduction and inductive learning
Handbook of defeasible reasoning and uncertainty management systems
Advances in Inductive Logic Programming
Advances in Inductive Logic Programming
The Philosophy of Artificial Intelligence
The Philosophy of Artificial Intelligence
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part I
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part I
Logicism and the Development of Computer Science
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Bayesian Nets And Causality: Philosophical And Computational Foundations
Bayesian Nets And Causality: Philosophical And Computational Foundations
The Crystallizing Substochastic Sequential Machine Extractor: CrySSMEx
Neural Computation
Fuzzy rough sets and multiple-premise gradual decision rules
International Journal of Approximate Reasoning
Fuzzy rough sets and multiple-premise gradual decision rules
WILF'03 Proceedings of the 5th international conference on Fuzzy Logic and Applications
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The relationship between machine learning and the philosophy of science can be classed as a dynamic interaction: a mutually beneficial connection between two autonomous fields that changes direction over time. I discuss the nature of this interaction and give a case study highlighting interactions between research on Bayesian networks in machine learning and research on causality and probability in the philosophy of science.