Showing papers on "Disjunctive normal form published in 2013"

Semantics and Inference for Probabilistic Description Logics

Abstract: We present a semantics for Probabilistic Description Logics that is based on the distribution semantics for Probabilistic Logic Programming. The semantics, called DISPONTE, allows to express assertional probabilistic statements. We also present two systems for computing the probability of queries to probabilistic knowledge bases: BUNDLE and TRILL. BUNDLE is based on the Pellet reasoner while TRILL exploits the declarative Prolog language. Both algorithms compute a propositional Boolean formula that represents the set of explanations to the query. BUNDLE builds a formula in Disjunctive Normal Form in which each disjunct corresponds to an explanation while TRILL computes a general Boolean pinpointing formula using the techniques proposed by Baader and Penaloza. Both algorithms then build a Binary Decision Diagram BDD representing the formula and compute the probability from the BDD using a dynamic programming algorithm. We also present experiments comparing the performance of BUNDLE and TRILL.

Computing the Output Distribution and Selection Probabilities of a Stack Filter from the DNF of Its Positive Boolean Function

Abstract: Many nonlinear filters used in practise are stack filters. An algorithm is presented which calculates the output distribution of an arbitrary stack filter S from the disjunctive normal form (DNF) of its underlying positive Boolean function (PBF). Our algorithm avoids to enumerate the models of the PBF one by one, and thus is considerably more efficient than previous methods. The so called rank selection probabilities can be computed along the way.

System and method for rapid management of logic formulas

Abstract: A method for simplifying a complex well-formed formula (WFF) may include receiving, in a formula processor (18), an input WFF (100) and converting the input WFF (100) into an initial bit array (200). The method may further include simplifying the initial bit array (200) into a simplified bit array (212) by removing predicates (114) from the initial bit array (200) not necessary to represent the input WFF (100). The method may include converting the simplified bit array (212) into a return WFF (138) in either a conjunctive normal form (142) or a disjunctive normal form (140).

On computation of De Morgan and quasi-De Morgan functions

Abstract: In this paper we give various algorithms for computation of De Morgan and quasi-De Morgan functions. We define the disjunctive and conjunctive normal forms for De Morgan and quasi-De Morgan functions and introduce the concept of Zhegalkin polynomial for the case of quasi-De Morgan functions as well as prove some results analogous to the classical Zhegalkin representation theorem on Boolean functions. The obtained results have richer matter that those in the classical case.

A New Attribute Reduction Recursive Algorithm Based On Granular Computing

Abstract: Existing representative research achievement of attribute reduction mainly focused on two aspects. One is how to improve the efficiency of attribute reduction algorithms for all attributes including the added properties. Such as the recursive algorithm to change conjunctive normal form into disjunctive normal form based on the Boolean matrix and algorithm based on radix sorting for computing core and reductions of a given information system, etc. On the other hand focus on objects recursive algorithms. The drawback is that these methods have not fully use knowledge gained when some attributes was added to a discussion on domain. Therefore, in this paper, the regularity of core and reduction’s changes under adding new attributes into a given information system were discussed. Moreover, the new incremental recursive reduction algorithms from an information system were proposed based on Granular computing. Experiments show that these algorithms can quickly and exactly calculate new core and reduction of new information system by taking advantage of knowledge of previous information system.

Research on Fuzzy-logic Algorithm of Intelligent Generating Test Paper

Abstract: Intelligent generating test paper can be thought of as a typical fuzzy mathematical logic problem. First, the paper analyzes the quantitative evaluation criteria of the traditional test paper generation questions and shows the existing problems in practical application of it. Then the paper introduces two different fuzzy constraints principal disjunctive normal form concept and proposes an evaluation system combining quantitative with qualitative. Viewing at the particularity of constraint satisfaction problems of intelligent generating test paper, the paper uses heuristic knowledge and applies constraint satisfaction method combining generation test with membership correction constraint satisfaction methods to realize intelligent generating test paper. The experiment shows that this method obtains good results.

Access-road logic generating system and method for automatic train supervision system

Abstract: The invention discloses an access-road logic generating system and method for an automatic train supervision system. The system comprises a client, a database, a maintenance assembly and a viewing assembly. The method comprises the following steps: (1) carrying out unified definition on equipment value and state and logical relation thereof; (2) generating a logic relation expression by the client; (3) converting the logic relation expression into an expression in disjunctive normal form; (4) generating really designated and falsely designated data logic values by the logic relation in disjunctive normal form. According to the invention, the complexity of access-road supervision of the automatic train supervision system can be reduced, and the safety and reliability of the system can be improved.

Machine-learning-based circuit synthesis

Abstract: Multi-level logic synthesis is a problem of immense practical significance, and is a key to developing circuits that optimize a number of parameters, such as depth, energy dissipation, reliability, etc The problem can be defined as the task of taking a collection of components from which one wants to synthesize a circuit that optimizes a particular objective function This problem is computationally hard, and there are very few automated approaches for its solution To solve this problem we propose an algorithm, called Circuit-Decomposition Engine (CDE), that is based on learning decision trees, and uses a greedy approach for function learning We empirically demonstrate that CDE, when given a library of different component types, can learn the function of Disjunctive Normal Form (DNF) Boolean representations and synthesize circuit structure using the input library We compare the structure of the synthesized circuits with that of well-known circuits using a range of circuit similarity metrics

Method for quickly solving seismic attribute reduction

Abstract: The invention discloses a method for quickly solving seismic attribute reduction. The method comprises the steps of (a), expressing a distinguishing matrix M of a system S; (b) establishing a disjunctive logic expression by element cij of all nonempty sets in the distinguishing matrix and carrying out conjunction operation to obtain a conjunction normal form; (c) calculating a distinguishing function f corresponding to M by the conjunction normal form; and (d) calculating the minimum disjunctive normal form of the f, wherein each disjunctive component corresponds to a reduction, then the seismic attribute reduction can be obtained. The method can quickly solve the seismic attribute reduction, has simple steps, and is accurate in result, and the labor cost is greatly lowered.

Disjunctive Logic Programs versus Normal Logic Programs

Abstract: This paper focuses on the expressive power of disjunctive and normal logic programs under the stable model semantics over finite, infinite, or arbitrary structures. A translation from disjunctive logic programs into normal logic programs is proposed and then proved to be sound over infinite structures. The equivalence of expressive power of two kinds of logic programs over arbitrary structures is shown to coincide with that over finite structures, and coincide with whether or not NP is closed under complement. Over finite structures, the intranslatability from disjunctive logic programs to normal logic programs is also proved if arities of auxiliary predicates and functions are bounded in a certain way.

Algorithms using first-order neighborhoods for minimization of Boolean functions

Abstract: A class of minimization algorithms for Boolean functions that involve conjunctions from a reduced disjunctive normal form and first-order neighborhoods of such conjunctions is investigated. A particular algorithm is selected that is the best in the class in many cases.

High dimension CNF to DNF conversion using grid computing

Abstract: CNF to DNF Conversion is considered as vast area of research by scientists for PLA's, circuit designs, FPGA's, artificial intelligence, etc High dimension variable conversion has become a key demand in the current business standard Various applications are in its requirement like gnome analysis, grid computing, bioinformatics, imaging system, rough sets requires higher variable processing algorithm Problem statement is - Design and implementation of High dimension optimal conjunctive normal form to optimal (prime implicants) disjunctive normal form conversion which is an “NP hard problem conversion to an NP complete” Thus CNF to DNF can only be considered to evaluate best performance for higher variable processing on high end systems The best-known representations of Boolean functions f are those as disjunctions of terms (DNFs) and as conjunctions of clauses (CNFs) It is convenient to define the DNF size of f as the minimal number of terms in a DNF representing f and the CNF size as the minimal number of clauses in a CNF representing f