This paper reports application of neuro- fuzzy inference system (NFIS) and self organizing feature map neural networks (SOM) on detection of contact state in a block system. In this manner, on a simple system, the evolution of contact states, by parallelization of DDA, has been investigated. So, a comparison between NFIS and SOM results has been presented. The results show applicability of the proposed methods, by different accuracy, on detection of contact's distribution.

In this study, under general frame of MAny Connected Intelligent Particles Systems (MACIPS), we reproduce two new simple subsets of such intelligent complex network, namely hybrid intelligent systems, involved a few prominent intelligent computing and approximate reasoning methods: self organizing feature map (SOM), Neuro-Fuzzy Inference System and Rough Set Theory (RST). Over this, we show how our algorithms can be construed as a linkage of government-society interaction, where government catches various fashions of behavior: solid (absolute) or flexible. So, transition of such society, by changing of connectivity parameters (noise) from order to disorder is inferred. Add to this, one may find an indirect mapping among financial systems and eventual market fluctuations with MACIPS.

In this study, we introduce general frame of MAny Connected Intelligent Particles Systems (MACIPS). Connections and interconnections between particles get a complex behavior of such merely simple system (system in system).Contribution of natural computing, under information granulation theory, are the main topics of this spacious skeleton. Upon this clue, we organize two algorithms involved a few prominent intelligent computing and approximate reasoning methods: self organizing feature map (SOM), Neuro- Fuzzy Inference System and Rough Set Theory (RST). Over this, we show how our algorithms can be taken as a linkage of government-society interaction, where government catches various fashions of behavior: solid (absolute) or flexible. So, transition of such society, by changing of connectivity parameters (noise) from order to disorder is inferred. Add to this, one may find an indirect mapping among financial systems and eventual market fluctuations with MACIPS. Keywords: phase transition, SONFIS, SORST, many connected intelligent particles system, society-government interaction

Determining the evolutionary history of a given biological data is an important task in biological sciences. Given a set of quartet topologies over a set of taxa, the Maximum Quartet Consistency (MQC) problem consists of computing a global phylogeny that satisfies the maximum number of quartets. A number of solutions have been proposed for the MQC problem, including Dynamic Programming, Constraint Programming, and more recently Answer Set Programming (ASP). ASP is currently the most efficient approach for optimally solving the MQC problem. This paper proposes encoding the MQC problem with pseudo-Boolean (PB) constraints. The use of PB allows solving the MQC problem with efficient PB solvers, and also allows considering different modeling approaches for the MQC problem. Initial results are promising, and suggest that PB can be an effective alternative for solving the MQC problem.

SimDialog is a visual editor for dialog in computer games. This paper presents the design of SimDialog, illustrating how script writers and non-programmers can easily create dialog for video games with complex branching structures and dynamic response characteristics. The system creates dialog as a directed graph. This allows for play using the dialog with a state-based cause and effect system that controls selection of non-player character responses and can provide a basic scoring mechanism for games.

Undirected graphs are often used to describe high dimensional distributions. Under sparsity conditions, the graph can be estimated using $\ell_1$ penalization methods. However, current methods assume that the data are independent and identically distributed. If the distribution, and hence the graph, evolves over time then the data are not longer identically distributed. In this paper, we show how to estimate the sequence of graphs for non-identically distributed data, where the distribution evolves over time.

The notion of optimality naturally arises in many areas of applied mathematics and computer science concerned with decision making. Here we consider this notion in the context of three formalisms used for different purposes in reasoning about multi-agent systems: strategic games, CP-nets, and soft constraints. To relate the notions of optimality in these formalisms we introduce a natural qualitative modification of the notion of a strategic game. We show then that the optimal outcomes of a CP-net are exactly the Nash equilibria of such games. This allows us to use the techniques of game theory to search for optimal outcomes of CP-nets and vice-versa, to use techniques developed for CP-nets to search for Nash equilibria of the considered games. Then, we relate the notion of optimality used in the area of soft constraints to that used in a generalization of strategic games, called graphical games. In particular we prove that for a natural class of soft constraints that includes weighted constraints every optimal solution is both a Nash equilibrium and Pareto efficient joint strategy. For a natural mapping in the other direction we show that Pareto efficient joint strategies coincide with the optimal solutions of soft constraints.

We present a formal model to represent and solve the unicast/multicast routing problem in networks with Quality of Service (QoS) requirements. To attain this, first we translate the network adapting it to a weighted graph (unicast) or and-or graph (multicast), where the weight on a connector corresponds to the multidimensional cost of sending a packet on the related network link: each component of the weights vector represents a different QoS metric value (e.g. bandwidth, cost, delay, packet loss). The second step consists in writing this graph as a program in Soft Constraint Logic Programming (SCLP): the engine of this framework is then able to find the best paths/trees by optimizing their costs and solving the constraints imposed on them (e.g. delay < 40msec), thus finding a solution to QoS routing problems. Moreover, c-semiring structures are a convenient tool to model QoS metrics. At last, we provide an implementation of the framework over scale-free networks and we suggest how the performance can be improved.

This paper explores the links between Knowledge Management and new community-based models of the organization from both a theoretical and an empirical perspective. From a theoretical standpoint, we look at Communities of Practice (CoPs) and Knowledge Management (KM) and explore the links between the two as they relate to the use of information systems to manage knowledge. We begin by reviewing technologically supported approaches to KM and introduce the idea of "Systemes d'Aide a la Gestion des Connaissances" SAGC (Systems to aid the Management of Knowledge). Following this we examine the contribution that communal structures such as CoPs can make to intraorganizational KM and highlight some of 'success factors' for this approach to KM that are found in the literature. From an empirical standpoint, we present the results of a survey involving the Chief Knowledge Officers (CKOs) of twelve large French businesses; the objective of this study was to identify the factors that might influence the success of such approaches. The survey was analysed using thematic content analysis and the results are presented here with some short illustrative quotes from the CKOs. Finally, the paper concludes with some brief reflections on what can be learnt from looking at this problem from these two perspectives.

Flow cytometry is often used to characterize the malignant cells in leukemia and lymphoma patients, traced to the level of the individual cell. Typically, flow cytometric data analysis is performed through a series of 2-dimensional projections onto the axes of the data set. Through the years, clinicians have determined combinations of different fluorescent markers which generate relatively known expression patterns for specific subtypes of leukemia and lymphoma -- cancers of the hematopoietic system. By only viewing a series of 2-dimensional projections, the high-dimensional nature of the data is rarely exploited. In this paper we present a means of determining a low-dimensional projection which maintains the high-dimensional relationships (i.e. information) between differing oncological data sets. By using machine learning techniques, we allow clinicians to visualize data in a low dimension defined by a linear combination of all of the available markers, rather than just 2 at a time. This provides an aid in diagnosing similar forms of cancer, as well as a means for variable selection in exploratory flow cytometric research. We refer to our method as Information Preserving Component Analysis (IPCA).