Analogical reasoning: Perspectives of artificial intelligence, cognitive science, and philosophy
The articles of this volume will not be indexed individually. This volume consists of papers contributed by researchers in different disciplines. From view of mathematics and AI especially the following papers are of interest: Some authors present their computational programs for simulating analogy: \textit{Gentner} et al. use the Structure Mapping Engine for analysing and handling metaphors, \textit{Ashley} shows how HYPO makes case-based analogical arguments in law, \textit{Darden} et al. place the system SUTTON for analogical hypothesis formation in the context of special scientific discovery, \textit{Burstein} describes how CARL learns assignment statements in BASIC using causal templates. Some papers contain theoretical considerations like \textit{Kedar-Cabelli} who examines computational models within a simplified model and formulates requirements on models for analogical reasoning. \textit{Greiner} applies a model-based approach using abstractions and defines useful analogical inferences. \textit{Davies} gives a logical formulation of analogy and tries to solve justification and non-redundancy problems using determinations. He defines uniformity as a statistical analogue of determination and relevance. \textit{Russell} views analogy as a logical inference or as probability-based inference. He develops a quantitative theory for the probability that an analogy is correct. \textit{Kuipers} and \textit{Niinilouto} develop systems modified and based upon Carnap's probabilistic system for analogy. \textit{Kuipers} shows how analogy by proximity of future occurrences leads to Markov chains.
scientific article; zbMATH DE number 41320