scholarly journals Incidence calculus: A mechanism for probabilistic reasoning

1985 ◽  
Vol 1 (3) ◽  
Author(s):  
Alan Bundy
Keyword(s):  
Probabilistic Reasoning ◽  
Incidence Calculus

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.

scholarly journals Making design decisions under uncertainties: probabilistic reasoning and robust product design

2021 ◽  
Author(s):  
Paul Christoph Gembarski ◽  
Stefan Plappert ◽  
Roland Lachmayer
Keyword(s):  
Product Development ◽  
Product Life Cycle ◽  
Probabilistic Reasoning ◽  
Late Phase ◽  
Development Project ◽  
Product Development Process ◽  
Bayesian Decision ◽  
Rotary Valve ◽  
Design Decisions ◽  
Decision Network

AbstractMaking design decisions is characterized by a high degree of uncertainty, especially in the early phase of the product development process, when little information is known, while the decisions made have an impact on the entire product life cycle. Therefore, the goal of complexity management is to reduce uncertainty in order to minimize or avoid the need for design changes in a late phase of product development or in the use phase. With our approach we model the uncertainties with probabilistic reasoning in a Bayesian decision network explicitly, as the uncertainties are directly attached to parts of the design artifact′s model. By modeling the incomplete information expressed by unobserved variables in the Bayesian network in terms of probabilities, as well as the variation of product properties or parameters, a conclusion about the robustness of the product can be made. The application example of a rotary valve from engineering design shows that the decision network can support the engineer in decision-making under uncertainty. Furthermore, a contribution to knowledge formalization in the development project is made.

Probabilistic reasoning in patients with body dysmorphic disorder

2011 ◽  
Vol 42 (3) ◽  
pp. 270-276 ◽  
Author(s):  
Hannah E. Reese ◽  
Richard J. McNally ◽  
Sabine Wilhelm
Keyword(s):  
Probabilistic Reasoning ◽  
Body Dysmorphic Disorder

scholarly journals Human visual motion perception shows hallmarks of Bayesian structural inference

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sichao Yang ◽  
Johannes Bill ◽  
Jan Drugowitsch ◽  
Samuel J. Gershman
Keyword(s):  
Motion Perception ◽  
Visual Motion ◽  
Probabilistic Reasoning ◽  
Choice Model ◽  
Human Motion ◽  
Object Motion ◽  
Ideal Observer ◽  
Structure Identification ◽  
Structural Inference ◽  
Visual Motion Perception

AbstractMotion relations in visual scenes carry an abundance of behaviorally relevant information, but little is known about how humans identify the structure underlying a scene’s motion in the first place. We studied the computations governing human motion structure identification in two psychophysics experiments and found that perception of motion relations showed hallmarks of Bayesian structural inference. At the heart of our research lies a tractable task design that enabled us to reveal the signatures of probabilistic reasoning about latent structure. We found that a choice model based on the task’s Bayesian ideal observer accurately matched many facets of human structural inference, including task performance, perceptual error patterns, single-trial responses, participant-specific differences, and subjective decision confidence—especially, when motion scenes were ambiguous and when object motion was hierarchically nested within other moving reference frames. Our work can guide future neuroscience experiments to reveal the neural mechanisms underlying higher-level visual motion perception.

A Bayesian formulation of the kalam cosmological argument

2014 ◽  
Vol 50 (4) ◽  
pp. 521-534
Author(s):  
CALUM MILLER
Keyword(s):  
Probabilistic Reasoning ◽  
Natural Theology ◽  
Cosmological Argument ◽  
Bayesian Formulation ◽  
Kalam Cosmological Argument ◽  
Kalām Cosmological Argument

AbstractThere has been a trend within natural theology to present arguments for theism deductively, such that at least one of the premises is likely to be extremely controversial. For those arguments with less controversial premises, the conclusion is usually something short of theism. On these grounds, some have employed probabilistic reasoning to revive classical arguments – to use less controversial premises in achieving a conclusion directly relevant to whether theism is true or not. Here, I formulate the kalam cosmological argument in Bayesian terms, and argue that doing so renders many objections levelled against it obsolete.

scholarly journals Assignment methods for incidence calculus

1995 ◽  
Vol 12 (1) ◽  
pp. 21-41 ◽  
Author(s):  
R.G. McLean ◽  
A. Bundy ◽  
W. Liu
Keyword(s):  
Incidence Calculus
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