Artificial Intelligence

1405 Submissions

[3] viXra:1405.0335 [pdf] submitted on 2014-05-27 15:31:17

Solving NP-Complete Problem by Simulations.

Authors: Michail Zak
Comments: 5 Pages.

Abstract. The mathematical formalism of quantum resonance combined with tensor product decomposability of unitary evolutions is mapped onto a class of NP-complete combinatorial problems. It has been demonstrated that nature has polynomial resources for solving NP-complete problems and that may help to develop a new strategy for artificial intelligence, as well as to re-evaluate the role of natural selection in biological evolution.
Category: Artificial Intelligence

[2] viXra:1405.0222 [pdf] submitted on 2014-05-12 17:28:46

Learning Markov Networks Structures Constrained by Context-Specific Independences

Authors: Alejandro Edera, Federico Schlüter, Facundo Bromberg
Comments: 41 Pages. This work is under revision in the IJAIT

This work focuses on learning the structure of Markov networks. Markov networks are parametric models for compactly representing complex probability distributions. These models are composed by: a structure and a set of numerical weights. The structure describes independences that hold in the distribution. Depending on the goal of learning intended by the user, structure learning algorithms can be divided into: density estimation algorithms, focusing on learning structures for answering inference queries; and knowledge discovery algorithms, focusing on learning structures for describing independences qualitatively. The latter algorithms present an important limitation for describing independences as they use a single graph, a coarse grain representation of the structure. However, many practical distributions present a flexible type of independences called context-specific independences, which cannot be described by a single graph. This work presents an approach for overcoming this limitation by proposing an alternative representation of the structure that named canonical model; and a novel knowledge discovery algorithm called CSPC for learning canonical models by using as constraints context-specific independences present in data. On an extensive empirical evaluation, CSPC learns more accurate structures than state-of-the-art density estimation and knowledge discovery algorithms. Moreover, for answering inference queries, our approach obtains competitive results against density estimation algorithms, significantly outperforming knowledge discovery algorithms.
Category: Artificial Intelligence

[1] viXra:1405.0214 [pdf] submitted on 2014-05-11 19:30:31

Communication Neutrosophic Routes

Authors: editors Florentin Smarandache, Stefan Vladutescu
Comments: 217 Pages.

The life of human beings is a place of communication. Consequently, any cognitive and cogitative manifestation presents a route of communication. People consume their lives relating by communicational. Some communicational relationships are contradictory, others are neutral, since within the manifestations of life there are found conflicts meanings and/or neutral meanings. Communicational relations always comprise a set of neutral, neutrosophic meanings. Particularly, we talk about scientific sculptural communication, esthetic communication and so on, as specific manifestations of life. It can be asserted that in any communication there are routes of access and neutrosophic routes. Any communication is traversed by neutrosophic routes of communication. [F. Smarandache & S. Vladutescu] The book has 10 chapters written by the following authors and co-authors: Florentin Smarandache, Stefan Vladutescu, Ioan Constantin Dima, Mariana Man, Alexandra Iorgulescu, Alina Tenescu, Madalina Strechie, Daniela Gifu, MIhaela Gabriela Paun, Maria Nowicka-Skowron, Sorin Mihai Radu, Janusz Grabara, Ion Cosmescu, and Bianca Teodorescu.
Category: Artificial Intelligence