2006
NIPS
NeurIPS 2006
Combining causal and similarity-based reasoning
Abstract
Everyday inductive reasoning draws on many kinds of knowledge, including knowledge about relationships between properties and knowledge about relationships between objects. Previous accounts of inductive reasoning generally focus on just one kind of knowledge: models of causal reasoning often focus on relationships between properties, and models of similarity-based reasoning often focus on similarity relationships between objects. We present a Bayesian model of inductive reasoning that incorporates both kinds of knowledge, and show that it accounts well for human inferences about the properties of biological species.
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Conference Pioneer
— NIPS 2006
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Topic Pioneer
— Causal Inference
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Interdisciplinary Bridge
— Artificial Intelligence and Knowledge & Reasoning
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Trend Setter
— Causal Inference
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Keyword Pioneer
— causal reasoning
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
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Hot Topic Early Bird
— probabilistic modeling
Authors
Topics
Artificial Intelligence > Core AI > Causal Inference
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Knowledge & Reasoning > Reasoning > Causal Inference
Interdisciplinary > Cognitive Science > Cognitive Modeling
Machine Learning > Bayesian & Probabilistic > Bayesian Inference