Explanatory Coherence and Belief Revision in Naive Physics

1988 ◽  
Author(s):  
Michael Ranney ◽  
Paul Thagard
Keyword(s):  
Belief Revision ◽  
Explanatory Coherence ◽  
Naive Physics

Knowing-it-all but still learning: Perceptions of one’s own knowledge and belief revision.

2017 ◽  
Vol 53 (12) ◽  
pp. 2319-2332 ◽  
Author(s):  
Sara Hagá ◽  
Kristina R. Olson
Keyword(s):  
Belief Revision ◽  
Learning Perceptions

Children change their answers in response to neutral follow-up questions by a knowledgeable asker

2019 ◽  
Author(s):  
Elizabeth Bonawitz ◽  
Patrick Shafto ◽  
Yue Yu ◽  
Sophie Elizabeth Colby Bridgers ◽  
Aaron Gonzalez
Keyword(s):  
Social Learning ◽  
Belief Revision ◽  
Correct Answer ◽  
New Information ◽  
Common Practices ◽  
Parent Child

Burgeoning evidence suggests that when children observe data, they use knowledge of the demonstrator’s intent to augment learning. We propose that the effects of social learning may go beyond cases where children observe data, to cases where they receive no new information at all. We present a model of how simply asking a question a second time may lead to belief revision, when the questioner is expected to know the correct answer. We provide an analysis of the CHILDES corpus to show that these neutral follow-up questions are used in parent-child conversations. We then present three experiments investigating 4- and 5-year-old children’s reactions to neutral follow-up questions posed by ignorant or knowledgeable questioners. Children were more likely to change their answers in response to a neutral follow-up question from a knowledgeable questioner than an ignorant one. We discuss the implications of these results in the context of common practices in legal, educational, and experimental psychological settings.

LEARNING NAIVE PHYSICS BY VISUAL OBSERVATION: USING QUALITATIVE SPATIAL REPRESENTATIONS AND PROBABILISTIC REASONING

2001 ◽  
Vol 01 (03) ◽  
pp. 273-285 ◽  
Author(s):  
PAUL A. BOXER
Keyword(s):  
Bayesian Network ◽  
Probabilistic Reasoning ◽  
Autonomous Robots ◽  
Visual Observation ◽  
Spatial Representations ◽  
Physical Behavior ◽  
Naive Physics ◽  
Action At A Distance ◽  
General Physical ◽  
Test Scenarios

Autonomous robots are unsuccessful at operating in complex, unconstrained environments. They lack the ability to learn about the physical behavior of different objects through the use of vision. We combine Bayesian networks and qualitative spatial representation to learn general physical behavior by visual observation. We input training scenarios that allow the system to observe and learn normal physical behavior. The position and velocity of the visible objects are represented as qualitative states. Transitions between these states over time are entered as evidence into a Bayesian network. The network provides probabilities of future transitions to produce predictions of future physical behavior. We use test scenarios to determine how well the approach discriminates between normal and abnormal physical behavior and actively predicts future behavior. We examine the ability of the system to learn three naive physical concepts, "no action at a distance", "solidity" and "movement on continuous paths". We conclude that the combination of qualitative spatial representations and Bayesian network techniques is capable of learning these three rules of naive physics.

Positive versus Negative Undermining in Belief Revision

Noûs ◽  
1984 ◽  
Vol 18 (1) ◽  
pp. 39 ◽  
Author(s):  
Gilbert Harman
Keyword(s):  
Belief Revision

Conciliatory Views on Peer Disagreement and the Order of Evidence Acquisition

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Marc Andree Weber
Keyword(s):  
Belief Revision ◽  
Point Of View ◽  
Imprecise Probabilities ◽  
Peer Disagreement

Abstract The evidence that we get from peer disagreement is especially problematic from a Bayesian point of view since the belief revision caused by a piece of such evidence cannot be modelled along the lines of Bayesian conditionalisation. This paper explains how exactly this problem arises, what features of peer disagreements are responsible for it, and what lessons should be drawn for both the analysis of peer disagreements and Bayesian conditionalisation as a model of evidence acquisition. In particular, it is pointed out that the same characteristic of evidence from disagreement that explains the problems with Bayesian conditionalisation also suggests an interpretation of suspension of belief in terms of imprecise probabilities.

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