Plausible inference: A multi-valued logic for problem solving

A new logic is developed which permits continuously variable strength of belief in the truth of assertions. Four inference rules result, with formal logic as a limiting case. Quantification of belief is defined. Propagation of belief to linked assertions results from dependency-based techniques of truth maintenance so that local consistency is achieved or contradiction discovered in problem solving. Rules for combining, confirming, or disconfirming beliefs are given, and several heuristics are suggested that apply to revising already formed beliefs in the light of new evidence. The strength of belief that results in such revisions based on conflicting evidence are a highly subjective phenomenon. Certain quantification rules appear to reflect an orderliness in the subjectivity. Several examples of reasoning by plausible inference are given, including a legal example and one from robot learning. Propagation of belief takes place in directions forbidden in formal logic and this results in conclusions becoming possible for a given set of assertions that are not reachable by formal logic

Problem of oscillating cone in supersonic flow is solved by small perturbation techniques

Small perturbation technique solves the problem of an oscillating cone in supersonic flow. The logic of the program is straightforward, as reflected in the actual instructions for solving the problem

Stable Model Counting and Its Application in Probabilistic Logic Programming

Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the probability of given queries being true provided a set of mutually independent random variables, a model (a logic program) and some evidence. The core of solving this inference task involves translating the logic program to a propositional theory and using a model counter. In this paper, we show that for some problems that involve inductive definitions like reachability in a graph, the translation of logic programs to SAT can be expensive for the purpose of solving inference tasks. For such problems, direct implementation of stable model semantics allows for more efficient solving. We present two implementation techniques, based on unfounded set detection, that extend a propositional model counter to a stable model counter. Our experiments show that for particular problems, our approach can outperform a state-of-the-art probabilistic logic programming solver by several orders of magnitude in terms of running time and space requirements, and can solve instances of significantly larger sizes on which the current solver runs out of time or memory.Comment: Accepted in AAAI, 201

THE INFLUENCE OF MATHEMATICAL LOGIC INTELLIGENCE AND STUDENTS’ CONFIDENCE ON PROBLEM SOLVING ABILITY

This study aims to determine the influence of mathematical logic intelligence and students' confidence on problem-solving abilities. The method used in this study was a quantitative survey method by taking a sample of 34 students randomly. The data collection technique uses a questionnaire instrument for intelligence, logic, mathematics and self-confidence, while the test instrument for problem solving ability is in the form of a description question consisting of 5 questions. Testing the hypothesis of mathematical logic intelligence variables with problem-solving ability obtained tcount 2.750 > ttable 2.040 and Sig. value of 0.010 ttable 2.040 and Sig. value of 0.017 Ftable 3.295 and Sig. values of 0.000 < 0.05 which means that there is a significant influence of mathematical logic intelligence and confidence on problem solving ability. The R Square value obtained is 0.526, which means that simultaneously the variables of intelligence, mathematical logic and self-confidence affect problem solving ability by 52.6%

Generalized disjunction decomposition for the evolution of programmable logic array structures

Evolvable hardware refers to a self reconfigurable electronic circuit, where the circuit configuration is under the control of an evolutionary algorithm. Evolvable hardware has shown one of its main deficiencies, when applied to solving real world applications, to be scalability. In the past few years several techniques have been proposed to avoid and/or solve this problem. Generalized disjunction decomposition (GDD) is one of these proposed methods. GDD was successful for the evolution of large combinational logic circuits based on a FPGA structure when used together with bi-directional incremental evolution and with (1+ë) evolution strategy. In this paper a modified generalized disjunction decomposition, together with a recently introduced multi-population genetic algorithm, are implemented and tested for its scalability for solving large combinational logic circuits based on Programmable Logic Array (PLA) structures