Breaking the search space symmetry in partitioning problems: An application to the graph coloring problem

Many problems consist in splitting a set of objects into different groups so that each group verifies some properties. In practice, a partitioning is often encoded by an array mapping each object to its group numbering. In fact, the group number of an object does not really matter, and one can simply rename each group to obtain a new encoding. That is what we call the symmetry of the search space in a partitioning problem. This property may be prejudicial for optimization methods such as evolutionary algorithms (EA) which require some diversity during the search.     

A flexible architecture for distributed knowledge based systems with nomadic access through handheld devices

Nowadays any Knowledge Based System (KBS) realization needs of intercommunication among distributed components and to use non-connected and distributed data sources, which poses several challenges to the classical Artificial Intelligence field of KBS.

The multiagent paradigm and the use of ontologies are considered to be suitable tools to face the problems of designing and developing today KBS. On the other hand, using such networked KBS through handheld devices makes more efficient exploitation and interaction with the system.

This paper presents an open and flexible architecture for a distributed KBS and an application of it to construct a system for Psychological Disorders consulting, the so called PDA² (Psychological Disorder Assistant through PDA). We analyze the main features of the architecture as well as the agent tools we may use to construct it. Additionally, we present a support ontology for Psychological Disorders.     

A simple approximation algorithm for the weighted matching problem

We present a linear time approximation algorithm with a performance ratio of 1/2 for finding a maximum weight matching in an arbitrary graph. Such a result is already known and is due to Preis [STACS'99, Lecture Notes in Comput. Sci., Vol. 1563, 1999, pp. 259-269]. Our algorithm uses a new approach which is much simpler than the one given by Preis and needs no amortized analysis for its running time.     

The validity of psychophysiological detection of information with the Guilty Knowledge Test: A meta-analytic review.

The authors performed a meta-analysis based on 169 conditions, gathered from 80 laboratory studies, to estimate the validity of the Guilty Knowledge Test (GKT) with the electrodermal measure. The over-all average effect size was 1.55, but there were considerable variations among studies. In particular, mock-crime studies produced the highest average effect size (2.09). Three additional moderators, were identified: Motivational instructions, deceptive ("no") verbal responses, and the use of at least 5 questions were associated with enhanced validity. Finally, a set of 10 studies that best approximated applications of the GKT under optimal conditions produced an average effect size of 3.12. The authors discuss factors that might limit the generalizability of these results and recommend further research of the GKT in realistic setups. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Sub-optimal solutions to track detection problem using graph theoretic concepts

This paper demonstrates how the problem of tracking targets, which appear as either straight or curved lines in two-dimensional display images (or data images) can be formulated in terms of a directed weighted graph model and how dynamic programming techniques can be efficiently applied to reach an optimal or sub-optimal solution. In general, track detection algorithms providing optimal solutions have good detective ability, but most of them suffer from the inability to detect discontinuous lines or to resolve efficiently pairs of crossing lines. A sub-optimal solution is provided that efficiently overcomes these weaknesses. We focus on modeling the track detection problem in terms of a graph, formulating fast sequential/parallel sub-optimal track detection algorithms and testing them on simulated data in order to show their detective ability. Moreover, we specify the conditions under which sub-optimal algorithms can perform at least as well as their corresponding optimal algorithms. This is significant for the track detection problem where fast, accurate and real-time detection is considered a necessity.     

MKR──一种有效的增量式概念获取系统

本文对示例式学习的ＩＤ５Ｒ算法进行了深入的研究并作了进一步的改进，提高了其计算效率．并提出一种适合于增量式学习的抗干扰机制，拓广了算法的适用范围．     

知识驱动的UG中的产品开发

重点论述了UG中的KBE即KF模块的知识驱动产品开发设计过程及其关键技术，介绍了知识驱动自动化过程，实例剖析了UG中的KF在产品开发设计过程中的应用。