Irrelevance and Conditioning in First-Order Probabilistic Logic

Conditioning in First-Order Knowledge Compilation and Lifted Probabilistic Inference

Knowledge compilation is a powerful technique for compactly representing and efficiently reasoning about logical knowledge bases. It has been successfully applied to numerous problems in artificial intelligence, such as probabilistic inference and conformant planning. Conditioning, which updates a knowledge base with observed truth values for some propositions, is one of the fundamental operati...

Probabilistic Default Logic Based on Irrelevance and Relevance Assumptions

Designing Fuzzy Controller for Air Conditioning Systems in order to Save Energy Consumption and Provide Optimal Conditions in Closed Environments (Indoors)

Today air conditioning systems have been considered by all people as one of welfarerequirements in buildings and closed environments. Since a considerable part of energy lossoccurs in ordinary modern systems, new strategies and solutions are developed in the field inorder to save amount of energy consumption and observe environmental considerations. Fuzzycontrol is one of these methods which pr...

A Design Methodology for Reliable MRF-Based Logic Gates

Probabilistic-based methods have been used for designing noise tolerant circuits recently. In these methods, however, there is not any reliability mechanism that is essential for nanometer digital VLSI circuits. In this paper, we propose a novel method for designing reliable probabilistic-based logic gates. The advantage of the proposed method in comparison with previous probabilistic-based met...

Conditioning, Conditional Independence and Irrelevance in Evidence Theory

The goal of the paper is to reveal the relationships between recently introduced concept of conditional independence in evidence theory and those (dependent on the choice of conditioning rule) of conditional irrelevance.