Solving linear programming problems with neural networks: acomparative study

In this paper we study three different classes of neural network models for solving linear programming problems. We investigate the following characteristics of each model: model complexity, complexity of individual neurons, and accuracy of solutions. Simulation examples are given to illustrate the dynamical behavior of each model.     

Solving fuzzy shortest path problems by neural networks

In this paper, we introduce the neural networks for solving fuzzy shortest path problems. The penalization of the neural networks is realized after transforming into crisp shortest path model. The procedure and efficiency of this approach are shown with numerical simulations.     

Neural networks in robotics: A survey

The purpose of this paper is to provide an overview of the research being done in neural network approaches to robotics, outline the strengths and weaknesses of current approaches, and predict future trends in this area.This work was supported, in part, by Sandia National Laboratories under contract No. 06-1977, Albuquerque, New Mexico.     

Solving satisfiability problems with preferences

Propositional satisfiability (SAT) is a success story in Computer Science and Artificial Intelligence: SAT solvers are currently used to solve problems in many different application domains, including planning and formal verification. The main reason for this success is that modern SAT solvers can successfully deal with problems having millions of variables. All these solvers are based on the Davis–Logemann–Loveland procedure (dll). In its original version, dll is a decision procedure, but it can be very easily modified in order to return one or all assignments satisfying the input set of clauses, assuming at least one exists. However, in many cases it is not enough to compute assignments satisfying all the input clauses: Indeed, the returned assignments have also to be “optimal” in some sense, e.g., they have to satisfy as many other constraints—expressed as preferences—as possible. In this paper we start with qualitative preferences on literals, defined as a partially ordered set (poset) of literals. Such a poset induces a poset on total assignments and leads to the definition of optimal model for a formula ψ as a minimal element of the poset on the models of ψ. We show (i) how dll can be extended in order to return one or all optimal models of ψ (once converted in clauses and assuming ψ is satisfiable), and (ii) how the same procedures can be used to compute optimal models wrt a qualitative preference on formulas and/or wrt a quantitative preference on literals or formulas. We implemented our ideas and we tested the resulting system on a variety of very challenging structured benchmarks. The results indicate that our implementation has comparable performances with other state-of-the-art systems, tailored for the specific problems we consider.     

Artificial neural networks for robotics coordinate transformation

Artificial neural networks with such characteristics as learning, graceful degradation, and speed inherent to parallel distributed architectures might provide a flexible and cost solution to the real time control of robotics systems. In this investigation artificial neural networks are presented for the coordinate transformation mapping of a two-axis robot modeled with Fischertechnik physical modeling components. The results indicate that artificial neural systems could be utilized for practical situations and that extended research in these neural structures could provide adaptive architectures for dynamic robotics control.     

Applications of neural networks for coordinate transformations in robotics

The use of artificial neural networks is investigated for application to trajectory control problems in robotics. The relative merits of position versus velocity control is considered and a control scheme is proposed in which neural networks are used as static maps (trained off-line) to compute the inverse of the manipulator Jacobian matrix. A proof of the stability of this approach is offered, assuming bounded errors in the static map. A representative two-link robot is investigated using an artificial neural network which has been trained to compute the components of the inverse of the Jacobian matrix. The controller is implemented in the laboratory and its performance compared to a similar controller with the analytical inverse Jacobian matrix.     

Solving workplace problems associated with VDTs

The introduction of VDTs into the vast majority of modern working environments represents a substantial change in the disciplines of work for many people. This paper describes how the health and safety issues associated with this change were managed in one organisation by the acquisition of ergonomics skills and their combination with occupational health surveillance techniques. The results show that ergonomics problems far outweighed symptomatic complaints, but symptomatic complaints were strongly associated with high VDT occupancy. The processes used resulted in high response rates from VDT users and an excellent 'clear-up' rate of problems. The solution of these problems was quantified in terms of difficulty and cost.     

Solving obstacle problems with guaranteed accuracy

In this paper, we consider a numerical technique which enables us to verify the existence of solutions for some simple obstacle problems. Using the finite element approximation and constructive error estimates, we construct, on a computer, a set of solutions which satisfies the hypothesis of the Schauder fixed-point theorem for a compact map on a certain Sobolev space. We describe the numerical verification algorithm for solving a two-dimensional obstacle problems and report some numerical results.     

Solving complex problems with a bioinspired model

Membrane systems are parallel and bioinspired systems which simulate membranes behavior when processing information. As a part of unconventional computing, P-systems are proven to be effective in solving complex problems.A software technique is presented here that obtain good results when dealing with such problems. The rules application phase is studied and updated accordingly to obtain the desired results. Certain rules are candidate to be eliminated which can make the model improving in terms of time.     

Solving subgraph isomorphism problems with constraint programming

The subgraph isomorphism problem consists in deciding if there exists a copy of a pattern graph in a target graph. We introduce in this paper a global constraint and an associated filtering algorithm to solve this problem within the context of constraint programming. The main idea of the filtering algorithm is to label every node with respect to its relationships with other nodes of the graph, and to define a partial order on these labels in order to express compatibility of labels for subgraph isomorphism. This partial order over labels is used to filter domains. Labelings can also be strengthened by adding information from the labels of neighbors. Such a strengthening can be applied iteratively until a fixpoint is reached. Practical experiments illustrate that our new filtering approach is more effective on difficult instances of scale free graphs than state-of-the-art algorithms and other constraint programming approaches.     

Rewiring strategies for semantic overlay networks

Semantic overlay networks cluster peers that are semantically, thematically or socially close into groups, by means of a rewiring procedure that is periodically executed by each peer. This procedure establishes new connections to similar peers and disregards connections to peers that are dissimilar. Retrieval effectiveness is then improved by exploiting this information at query time (as queries may address clusters of similar peers). Although all systems based on semantic overlay networks apply some rewiring technique, there is no comprehensive study showing the effect of rewiring on system’s performance. In this work, a framework for studying the attribution of rewiring strategies in semantic overlay networks is proposed. A generic approach to rewiring is presented and several variants of this approach are reviewed and evaluated. We show how peer organisation is affected by the different design choices of the rewiring mechanism and how these choices affect the performance of the system overall (both in terms of communication overhead and retrieval effectiveness). Our experimental evaluation with real-word data and queries confirms the dependence between rewiring strategies and retrieval performance, and gives insights on the trade-offs involved in the selection of a rewiring strategy.     

Solving graph algorithms with networks of spiking neurons

Spatio-temporal coding that combines spatial constraints with temporal sequencing is of great interest to brain-like circuit modelers. In this paper we present some new ideas of how these types of circuits can self-organize. We introduce a temporal correlation rule based on the time difference between the firing of neurons. With the aid of this rule we show an analogy between a graph and a network of spiking neurons. The shortest path, clustering based on the nearest neighbor, and the minimal spanning tree algorithms are solved using the proposed approach.     

Generating paraphrases from meaning-text semantic networks

This paper describes a first attempt to base a paraphrase generation system upon Meľčuk and Žolkovskij's linguistic meaning-text (MT) model whose purpose is to establish correspondences between meanings, represented by networks, and (ideally) all synonymous texts having this meaning. The system described here contains a Prolog implementation of a small explanatory and combinatorial dictionary (the MT lexicon) and, using unification and backtracking, generates from a given network the sentences allowed by the dictionary and the lexical transformations of the model. The passage from a net to the final texts is done through a series of transformations of intermediary structures that closely correspond to MT utterance representations (semantic, deep-syntax, surface-syntax, and morphological representations). These are graphs and trees with labeled arcs. The Prolog unification (equality predicate) was extended to extract information from these representations and build new ones. The notion of utterance path, used by many authors, is replaced by that of covering by defining subnetworks.     

Solving propositional satisfiability problems

We describe an algorithm for the satisfiability problem of prepositional logic, which is significantly more efficient for this problem than is a general mixed-integer programming code. Our algorithm is a list processor using a tree-search method, and is based on Loveland's form of the algorithm of Davis and Putnam.Deceased. Research partially supported by a research grant from the National Science Foundation (DMS-8513970).