Results 31 to 40 of about 90,486 (306)

Probabilistic logic with independence

International Journal of Approximate Reasoning, 2008
AbstractThis paper investigates probabilistic logics endowed with independence relations. We review propositional probabilistic languages without and with independence. We then consider graph-theoretic representations for propositional probabilistic logic with independence; complexity is analyzed, algorithms are derived, and examples are discussed ...
Cozman, Fabio Gagliardi, de Campos, Cassio Polpo, da Rocha, Jose Carlos Ferreira   +2 more
openaire   +4 more sources

Probabilistic-Input, Noisy Conjunctive Models for Cognitive Diagnosis

Frontiers in Psychology, 2018
Existing cognitive diagnosis models conceptualize attribute mastery status discretely as either mastery or non-mastery. This study proposes a different conceptualization of attribute mastery as a probabilistic concept, i.e., the probability of mastering ...
Peida Zhan, Wen-Chung Wang, Hong Jiao, Yufang Bian   +3 more
doaj   +1 more source

Flowsheet generation through hierarchical reinforcement learning and graph neural networks

AIChE Journal, Volume 69, Issue 1, January 2023., 2023
Abstract Process synthesis experiences a disruptive transformation accelerated by artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state‐of‐the‐art actor‐critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from ...
Laura Stops, Roel Leenhouts, Qinghe Gao, Artur M. Schweidtmann   +3 more
wiley   +1 more source

A History of Probabilistic Inductive Logic Programming

Frontiers in Robotics and AI, 2014
The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20 years, with many proposals for languages that combine probability with logic programming.
Fabrizio eRiguzzi, Elena eBellodi, Riccardo eZese   +2 more
doaj   +1 more source

A probabilistic dynamic logic

Proceedings of the fourteenth annual ACM symposium on Theory of computing - STOC '82, 1982
AbstractA logic, PrDL, is presented, which enables formal reasoning about probabilistic programs or, alternatively, reasoning probabilistically about conventional programs. The syntax of PrDL derives from Pratt's first-order dynamic logic and the semantics extends Kozen's semantics of probabilistic programs.
David Harel, Yishai A. Feldman
openaire   +2 more sources

A Probabilistic Logic of Cyber Deception [PDF]

IEEE Transactions on Information Forensics and Security, 2017
Malicious attackers often scan nodes in a network in order to identify vulnerabilities that they may exploit as they traverse the network. In this paper, we propose that the system generates a mix of true and false answers in response to scan requests. If the attacker believes that all scan results are true, then he will be on a wrong path.
Jajodia, Sushil, Park, Noseong, Pierazzi, Fabio, Pugliese, Andrea, Serra, Edoardo, Simari, Gerardo I., Subrahmanian, V. S.   +6 more
openaire   +7 more sources

Logic and Probabilistic Update [PDF]

, 2014
This chapter surveys recent work on probabilistic extensions of epistemic and dynamic-epistemic logics (the latter include the basic system of public announcement logic as well as the full product update logic). It emphasizes the importance of higher-order information as a distinguishing feature of these logics.
Demey, Lorenz, Kooi, Barteld
openaire   +3 more sources

“What if?” in Probabilistic Logic Programming

Theory and Practice of Logic Programming, 2023
AbstractA ProbLog program is a logic program with facts that only hold with a specified probability. In this contribution, we extend this ProbLog language by the ability to answer “What if” queries. Intuitively, a ProbLog program defines a distribution by solving a system of equations in terms of mutually independent predefined Boolean random variables.
RAFAEL KIESEL, KILIAN RÜCKSCHLOß, FELIX WEITKÄMPER   +2 more
openaire   +3 more sources

Numerical Markov Logic Network: A Scalable Probabilistic Framework for Hybrid Knowledge Inference

Information, 2021
In recent years, the Markov Logic Network (MLN) has emerged as a powerful tool for knowledge-based inference due to its ability to combine first-order logic inference and probabilistic reasoning.
Ping Zhong, Zhanhuai Li, Qun Chen, Boyi Hou, Murtadha Ahmed   +4 more
doaj   +1 more source

Twin neural network regression

Applied AI Letters, Volume 3, Issue 4, December 2022., 2022
We propose to reformulate a regression problem into predicting differences between target values. This allows for leveraging consistency conditions which can be used as uncertainty estimates and enable the production of an ensemble of predictions while training only a single neural network.
Sebastian Johann Wetzel, Kevin Ryczko, Roger Gordon Melko, Isaac Tamblyn   +3 more
wiley   +1 more source