MontyLingua, an integral part of ConceptNet which is currently the largest commonsense knowledge base, is an English text processor developed using Python programming language in MIT Media Lab. The main feature of MontyLingua is the coverage for all aspects of English text processing from raw input text to semantic meanings and summary generation, yet each component in MontyLingua is loosely-coupled to each other at the architectural and code level, which enabled individual components to be used independently or substituted. However, there has been no review exploring the role of MontyLingua in recent research work utilizing it. This paper aims to review the use of and roles played by MontyLingua and its components in research work published in 19 articles between October 2004 and August 2006. We had observed a diversified use of MontyLingua in many different areas, both generic and domain-specific. Although the use of text summarizing component had not been observe, we are optimistic that it will have a crucial role in managing the current trend of information overload in future research.

A fuzzy logic based classification engine has been developed for classifying mass spectra obtained with an imaging internal source Fourier transform mass spectrometer (I^2LD-FTMS). Traditionally, an operator uses the relative abundance of ions with specific mass-to-charge (m/z) ratios to categorize spectra. An operator does this by comparing the spectrum of m/z versus abundance of an unknown sample against a library of spectra from known samples. Automated positioning and acquisition allow I^2LD-FTMS to acquire data from very large grids, this would require classification of up to 3600 spectrum per hour to keep pace with the acquisition. The tedious job of classifying numerous spectra generated in an I^2LD-FTMS imaging application can be replaced by a fuzzy rule base if the cues an operator uses can be encapsulated. We present the translation of linguistic rules to a fuzzy classifier for mineral phases in basalt. This paper also describes a method for gathering statistics on ions, which are not currently used in the rule base, but which may be candidates for making the rule base more accurate and complete or to form new rule bases based on data obtained from known samples. A spatial method for classifying spectra with low membership values, based on neighboring sample classifications, is also presented.

Evolutionary processes proved very useful for solving optimization problems. In this work, we build a formalization of the notion of cooperation and competition of multiple systems working toward a common optimization goal of the population using evolutionary computation techniques. It is justified that evolutionary algorithms are more expressive than conventional recursive algorithms. Three subclasses of evolutionary algorithms are proposed here: bounded finite, unbounded finite and infinite types. Some results on completeness, optimality and search decidability for the above classes are presented. A natural extension of Evolutionary Turing Machine model developed in this paper allows one to mathematically represent and study properties of cooperation and competition in a population of optimized species.

We present tableau calculi for some logics of nonmonotonic reasoning, as defined by Kraus, Lehmann and Magidor. We give a tableau proof procedure for all KLM logics, namely preferential, loop-cumulative, cumulative and rational logics. Our calculi are obtained by introducing suitable modalities to interpret conditional assertions. We provide a decision procedure for the logics considered, and we study their complexity.

It is suggested that a new area of CSCR (Computer Supported Collaborative Research) is distinguished from CSCW and CSCL and that the demarcation between the three areas could do with greater clarification and prescription. Keywords: HCI, CSCW, CSCL, CSCR 1. Introduction The twin fields of Computer Supported Collaborative Work (CSCW) and Computer supported Collaborative Learning (CSCL) have been the subject of intense interest in the HCI research community during the past seven years. The split between CSCW and CSCL has grown wider in response to the recognition that the learning process is more distinct from the working pattern and is more intensively understood through new theories of pedagogy and education. It has become apparent that CSCL requires all of the facets of CSCW but in addition is constraint by these pedagogical theories and as such it is argued here that CSCL is a subset of CSCW (see figure1) The process of research is also a learning process but one which is more highly refined and involves learning in a particular way using special techniques and tools. As such it is argued further that research which is supported by computer collaboration is a subset of CSCL (fig.1) Figure 1 2. Differences between CSCW and CSCL Diaper (2005) maintains that the History of HCI shows a lack of coherent development.

The few available French resources for evaluating linguistic models or algorithms on other linguistic levels than morpho-syntax are either insufficient from quantitative as well as qualitative point of view or not freely accessible. Based on this fact, the FREEBANK project intends to create French corpora constructed using manually revised output from a hybrid Constraint Grammar parser and annotated on several linguistic levels (structure, morpho-syntax, syntax, coreference), with the objective to make them available on-line for research purposes. Therefore, we will focus on using standard annotation schemes, integration of existing resources and maintenance allowing for continuous enrichment of the annotations. Prior to the actual presentation of the prototype that has been implemented, this paper describes a generic model for the organization and deployment of a linguistic resource archive, in compliance with the various works currently conducted within international standardization initiatives (TEI and ISO/TC 37/SC 4).

INRA Toulouse, France Abstract Weighted Max-SA T is the optimization version of SA T and many important problems can be naturally encoded as such. Solving weighted Max-SA T is an important problem from both a theoretical and a practical point of view. In recent ye ars, there has been considerable interest in finding efficient solving techniques. Most of thi s work focus on the computation of good quality lower bounds to be used within a branch and bou nd DPLL-like algorithm. Most often, these lower bounds are described in a procedural way. Because of that, it is difficult to realize the logic that is behind. In this paper we introduce an original framework for Max-SA T that stresses the parallelism with classical SA T. Then, we extend the two basic SA T s olving techniques: search and inference. We show that many algorithmic tricks used in state-of-the-art Max-SA T solvers are easily expressable in logic terms with our framework in a unified manner. Besides, we introduce an original search algorithm that per forms a restricted amount of weighted resolution at each visited node. We empirically compare our algorithm w ith a variety of solving alternatives on several benchmarks. Our experiments, which constitute to the best of our knowledge the most comprehensive Max-sat eva luation ever reported, show that our algorithm is generally orders of magnitude faster t han any competitor. Preprint submitted to Elsevier Science 11 September 2018 1 Introduction Weighted Max-SA T is the optimization version of the SA T prob lem and many important problems can be naturally expressed as such. In recent years, there has been a considerable effort in finding efficient exact algorithms. A common drawback of all these alg orithms is that albeit the close relationship between SA T and Max-SA T, they cannot be easily described with logic terminology. For instance, the contributions of [11,12,13,14] are good quality lower bounds to be incorporated into a depth-first branch and bound procedure. These lower bounds are mostly defined in a procedural way and it is very difficult to see the logic that is behind the execution of the procedure. This is in contrast with SA T algorithms where the solving process can b e easily decomposed into atomic logical steps. In this paper we introduce an original framework for (weight ed) Max-SA T in which the notions of upper and lower bound are incorporated into the problem definition. Under this framework classical SA T is just a particular case of Max-SA T, and the main SA T solving techniques can be naturally extended. In pa rticular, we extend the basic simplification rules (for example, idempotency, absorption, unit clause reduction, etc) and introduce a new one, hardening, that does not make sense in the SA T context.

Functional decomposition of logic circuits has profound influence on all quality aspects of the cost-effective implementation of modern digital systems. In this paper, a relational approach to the decomposition of logic circuits is proposed. This approach is parallel to the normalization of relational databases, they are governed by the same concepts of functional dependency (FD) and multi-valued dependency (MVD). It is manifest that the functional decomposition of switching function actually exploits the same idea and serves a similar purpose as database normalization. Partitions play an important role in the decomposition. The interdependency of two partitions can be represented by a bipartite graph. We demonstrate that both FD and MVD can be represented by bipartite graphs with specific topological properties, which are delineated by partitions of minterms. It follows that our algorithms are procedures of constructing those specific bipartite graphs of interest to meet the information-lossless criteria of functional decomposition.

This paper presents a coordination algorithm for mobile autonomous robots. Relying upon distributed sensing the robots achieve rendezvous, that is, they move to a common location. Each robot is a point mass moving in a nonconvex environment according to an omnidirectional kinematic model. Each robot is equipped with line-of-sight limited-range sensors, i.e., a robot can measure the relative position of any object (robots or environment boundary) if and only if the object is within a given distance and there are no obstacles in-between. The algorithm is designed using the notions of robust visibility, connectivity-preserving constraint sets, and proximity graphs. Simulations illustrate the theoretical results on the correctness of the proposed algorithm, and its performance in asynchronous setups and with sensor measurement and control errors.

That car racing is a challenging problem, generating considerable public excitement, is evident from the huge amount of time and money invested both in practising and watching physical car racing, and in developing and playing racing games. For the same reasons, the problem(s) cannot sensibly be considered "trivial" or "solved" - no one would want to watch a race where the drivers were perfect. Though experiments with neural and evolutionary methods have undoubtedly taken place in commercial game studios, these have not been published for reasons of commercial confidentiality. The academic evolutionary computation community has apparently not devoted much energy to the car racing domain. One exception is Wloch and Bentley [11], who used evolutionary algorithms to optimize the parameters of simulated Formula 1 racing car with good results. However, they did not try to evolve the car controller, but rather used the simulator's built-in controller.