A Probabilistic Theory of Abductive Reasoning

被引：0

作者：

Dice, Nicolas A. Espinosa ^{[1
]}

Kaye, Megan L. ^{[1
]}

Ahmed, Hana ^{[1
,2
]}

Montanez, George D. ^{[1
]}

机构：

[1] Harvey Mudd Coll, AMISTAD Lab, Dept Comp Sci, Claremont, CA 91711 USA

[2] Scripps Coll, Claremont, CA 91711 USA

来源：

ICAART: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2 | 2021年

基金：

美国国家科学基金会;

关键词：

Abductive Logic; Machine Learning; Creative Abduction; Creativity; Graphical Model; Bayesian Network; Probabilistic Abduction;

D O I：

10.5220/0010195405620571

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present an abductive search strategy that integrates creative abduction and probabilistic reasoning to produce plausible explanations for unexplained observations. Using a graphical model representation of abductive search, we introduce a heuristic approach to hypothesis generation, comparison, and selection. To identify creative and plausible explanations, we propose 1) applying novel structural similarity metrics to a search for simple explanations, and 2) optimizing for the probability of a hypothesis' occurrence given known observations.

引用

页码：562 / 571

页数：10

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