Towards Probabilistic Reasoning in Type Theory - The Intersection Type Case

2020 ◽  
pp. 122-139
Author(s):  
Silvia Ghilezan ◽  
Jelena Ivetić ◽  
Simona Kašterović ◽  
Zoran Ognjanović ◽  
Nenad Savić
Keyword(s):  
Type Theory ◽  
Probabilistic Reasoning

scholarly journals Probabilistic Type Theory and Natural Language Semantics

2015 ◽  
Vol 10 ◽  
Author(s):  
Robin Cooper ◽  
Simon Dobnik ◽  
Shalom Lappin ◽  
Staffan Larsson
Keyword(s):  
Natural Language ◽  
Programming Languages ◽  
Type Theory ◽  
Probabilistic Reasoning ◽  
Syntactic Structure ◽  
Formal Semantics ◽  
Semantic Interpretation ◽  
Semantic Learning ◽  
History Of ◽  
Learning And Cognition

Type theory has played an important role in specifying the formal connection between syntactic structure and semantic interpretation within the history of formal semantics. In recent years rich type theories developed for the semantics of programming languages have become influential in the semantics of natural language. The use of probabilistic reasoning to model human learning and cognition has become an increasingly important part of cognitive science. In this paper we offer a probabilistic formulation of a rich type theory, Type Theory with Records (TTR), and we illustrate how this framework can be used to approach the problem of semantic learning. Our probabilistic version of TTR is intended to provide an interface between the cognitive process of classifying situations according to the types that they instantiate, and the compositional semantics of natural language.

Type Theory and Formal Proof

2009 ◽  
Author(s):  
Rob Nederpelt ◽  
Herman Geuvers
Keyword(s):  
Type Theory ◽  
Formal Proof

Computational Models for Multilayered Composite Shells with Application to Tires

1996 ◽  
Vol 24 (1) ◽  
pp. 11-38 ◽  
Author(s):  
G. M. Kulikov
Keyword(s):  
Type Theory ◽  
Computational Models ◽  
Geometrical Nonlinearity ◽  
Higher Order ◽  
Stress Strain ◽  
Strain Fields ◽  
First Order ◽  
Timoshenko Type ◽  
Joint Influence ◽  
Discrete Layer

Abstract This paper focuses on four tire computational models based on two-dimensional shear deformation theories, namely, the first-order Timoshenko-type theory, the higher-order Timoshenko-type theory, the first-order discrete-layer theory, and the higher-order discrete-layer theory. The joint influence of anisotropy, geometrical nonlinearity, and laminated material response on the tire stress-strain fields is examined. The comparative analysis of stresses and strains of the cord-rubber tire on the basis of these four shell computational models is given. Results show that neglecting the effect of anisotropy leads to an incorrect description of the stress-strain fields even in bias-ply tires.

Campus Social Climate Correlates of Environmental Type Dimensions

NASPA Journal ◽  
2004 ◽  
Vol 41 (4) ◽  
Author(s):  
Daniel W. Salter ◽  
Reynol Junco ◽  
Summer D. Irvin
Keyword(s):  
Work Environment ◽  
Personal Growth ◽  
Type Theory ◽  
General Purpose ◽  
Myers Briggs Type Indicator ◽  
Environment Scale ◽  
Type Indicator ◽  
Group Environment ◽  
System Maintenance ◽  
Environment Type

To address the ability of the Salter Environment Type Assessment (SETA) to measure different kinds of campus environments, data from three studies of the SETA with the Work Environment Scale, Group Environment Scale, and University Residence Environment Scale were reexamined (n = 534). Relationship dimension scales were very consistent with extraversion and feeling from environmental type theory. System maintenance and systems change scales were associated with judging and perception on the SETA, respectively. Results from the SETA and personal growth dimension scales were mixed. Based on this analysis, the SETA may serve as a general purpose environmental assessment for use with the Myers-Briggs Type Indicator.

Coq plugin for the Reasonably Exceptional Type Theory

2019 ◽  
Author(s):  
Pierre-Marie P�drot ◽  
Nicolas Tabareau ◽  
Hans Jacob Fehrmann ◽  
�ric Tanter
Keyword(s):  
Type Theory

Propositions and specifications of programs in Martin-Löf's type theory

1984 ◽  
Vol 24 (3) ◽  
pp. 288-301 ◽  
Author(s):  
Bengt Nordström ◽  
Jan Smith
Keyword(s):  
Type Theory

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.

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