Logics of knowledge based on possible-world semantics are u seful in many areas of knowledge representation and reasoning, ranging from security t o distributed computing to game theory. In these models, an agent is said to know a fact ϕ if ϕ is true in all the worlds she considers possible. While reasoning about knowledge with t his semantics has proved useful, as is well known, it suffers from what is known in the literature as the logical omniscience problem: under possible-world semantics, agents know all t autologies and know the logical consequences of their knowledge. While logical omniscience is certainly not always an issue, in many applications it is. For example, in the context of distributed computing, we are interested in polynomial-time algorithms, although in some cases the knowledge needed to p erform optimally may require calculations that cannot be performed in polynomial time (u nless P=NP) [Moses and Tuttle 1988]; in the context of security, we may want to reason about computationally bounded adversaries who cannot factor a large composite number, and thus cannot be logically omniscient; in game theory, we may be interested in the impac t of computational resources on solution concepts (for example, what will agents do if com puting a Nash equilibrium is difficult). Not surprisingly, many approaches for dealing with the logi cal omniscience problem have been suggested (see [Fagin, Halpern, Moses, and Vardi 1 995, Chapter 9] and [Moreno 1998]).

Intelligent Input Methods (IM) are essential for making text entries in many East Asian scripts, but their application to other languages has not been fully explored. This paper discusses how such tools can contribute to the deve lopment of computer processing of other oriental languages. We propose a design philosophy that regards IM as a text service platform, and treats the study of IM as a cross disciplinary subject from the perspectives of software engineering, human - computer interaction (HCI), and natural language processing (NLP). We discuss these three perspectives and indicate a number of possible future research directions.

Community structure identification has been one of the most popular research areas in recent years due to its applicability to the wide scale of disciplines. To detect communities in varied topics, there have been many algorithms proposed so far. However, most of them still have some drawbacks to be addressed. In this paper, we present an agent-based based community detection algorithm. The algorithm that is a stochastic one makes use of agents by forcing them to perform biased moves in a smart way. Using the information collected by the traverses of these agents in the network, the network structure is revealed. Also, the network modularity is used for determining the number of communities. Our algorithm removes the need for prior knowledge about the network such as number of the communities or any threshold values. Furthermore, the definite community structure is provided as a result instead of giving some structures requiring further processes. Besides, the computational and time costs are optimized because of using thread like working agents. The algorithm is tested on three network data of different types and sizes named Zachary karate club, college football and political books. For all three networks, the real network structures are identified in almost every run.

Distributed representation is a way of representing information in a pattern of activation over a set of neurons, in which each concept is represented by activation over multiple neuro ns, and each neuron participates in the representation of multiple concepts [1]. Examples of distributed representat ions include Recursive Auto-Associative Memory (RAAM) [2], Tensor Product Representations [3], Holographic Reduc ed Representations (HRRs) [4, 5], and Binary Spatter Codes (BSC) [6, 7, 8]. BSC is a powerful and simple method of representing hierarchical st ructures in connectionist systems and may be regarded as a binary version of HRRs. Yet, BSC has some drawback s associated with the representation of chunking. This is why different versions of BSC can be found in the literature.

The opening book is an important component of a chess engine, and thus computer chess programmers have been developing automated methods to improve the quality of their books. For chess, which has a very rich opening theory, large databases of high-quality games can be used as the basis of an opening book, from which statistics relating to move choices from given positions can be collected. In order to find out whether the opening books used by modern chess engines in machine versus machine competitions are ``comparable'' to those used by chess players in human versus human competitions, we carried out analysis on 26 test positions using statistics from two opening books one compiled from humans' games and the other from machines' games. Our analysis using several nonparametric measures, shows that, overall, there is a strong association between humans' and machines' choices of opening moves when using a book to guide their choices.

The k -modes algorithm [1] extends the k -means paradigm to cluster categorical data by using (1) a simple matching dissimilarity measure for categorical objects, (2) modes instead of means for clusters, and (3) a frequency-based method to update modes in the k -means fashion to minimize the cost function of clustering. Because the k -modes algorithm uses the same clustering process as k -means, it preserves the efficiency of the k -means algorithm. Although the k -modes algorithm is very efficient, it suffers the problem that the clustering results are sensitive to the selection of the initial points. Hence, a better initial points selection procedure would improve the reliability and accuracy of clustering results. To that end, an iterative initial-points refinement algorithm for k -modes clustering has been presented in [2]. As shown in [2], the new initialization pr ocedure greatly improves the reliability and accuracy of final clustering results. Despite the su ccess of Ref. [2], the following observations motivate us to further pursue other alternative initialization methods.

There has been a lot of recent interest in mining patterns from graphs. Often, the exact structure of the patterns of interest is not known. This happens, for example, when molecular structures are mined to discover fragments useful as features in chemical compound classification task, or when web sites are mined to discover sets of web pages representing logical documents. Such patterns are often generated from a few small subgraphs (cores), according to certain generalization rules (GRs). We call such patterns "generalized patterns"(GPs). While being structurally different, GPs often perform the same function in the network. Previously proposed approaches to mining GPs either assumed that the cores and the GRs are given, or that all interesting GPs are frequent. These are strong assumptions, which often do not hold in practical applications. In this paper, we propose an approach to mining GPs that is free from the above assumptions. Given a small number of GPs selected by the user, our algorithm discovers all GPs similar to the user examples. First, a machine learning-style approach is used to find the cores. Second, generalizations of the cores in the graph are computed to identify GPs. Evaluation on synthetic data, generated using real cores and GRs from biological and web domains, demonstrates effectiveness of our approach.

We are presenting a method to recognise geographical references in free text. Our tool must work on various languages with a mi ni-mum of language-dependent resources, except a gazetteer. The main difficulty is to disa mbiguate these place names by distinguis hing places from persons and by selecting the mo st likely place out of a list of homographi c place names world-wide. The system uses a number of language-independent clues and he uristics to disambiguate place name homogra phs. The final aim is to index texts with the countries and cities they mention and to automatically visualise this information on geographical maps using various tools.

We present an exploratory tool that extracts person names from multilingual news collections, matches name variants referring to the same person, and infers relationships between people based on the co-occurrence of their names in related news. A novel feature is the matching of name variants across languages and writing systems, including names written with the Greek, Cyrillic and Arabic writing system. Due to our highly multilingual setting, we use an internal standard representation for name representation and matching, instead of adopting the traditional bilingual approach to transliteration. This work is part of the news analysis system NewsExplorer that clusters an average of 25,000 news articles per day to detect related news within the same and across different languages.

In this note we introduce the notion of islands for restricting local search. We show how we can construct islands for CNF SAT problems, and how much search space can be eliminated by restricting search to the island.