In this paper we propose a Multi-Objective Ant Colony Optimization (MOACO) algorithm called CHAC, which has been designed to solve the problem of finding the path on a map (corresponding to a simulated battlefield) that minimizes resources while maximizing safety. CHAC has been tested with two different state transition rules: an aggregative function that combines the heuristic and pheromone information of both objectives and a second one that is based on the dominance concept of multiobjective optimization problems. These rules have been evaluated in several different situations (maps with different degree of difficulty), and we have found that they yield better results than a greedy algorithm (taken as baseline) in addition to a military behaviour that is also better in the tactical sense. The aggregative function, in general, yields better results than the one based on dominance.

We show how solution concepts in games such as Nash equilibrium, correlated equilibrium, rationalizability, and sequential equilibrium can be given a uniform definition in terms of \emph{knowledge-based programs}. Intuitively, all solution concepts are implementations of two knowledge-based programs, one appropriate for games represented in normal form, the other for games represented in extensive form. These knowledge-based programs can be viewed as embodying rationality. The representation works even if (a) information sets do not capture an agent's knowledge, (b) uncertainty is not represented by probability, or (c) the underlying game is not common knowledge.

It is well known that the planning problem (namely, the probl em of obtaining a valid sequence of transformations that moves a sys tem from an initial state to a goal state) is intractable in general [3]. However, it is widely believed that many real-life problems have a particu lar structure, and that by exploiting this structure general planners will be able to efficiently handle more meaningful problems. One of the most fruitful tools researchers have been using to characterize structure in planning problems is the so called causal graph ([6]). In short, the causal graph of a problem instance is a graph that c aptures the degree of interdependence among the state variables of the p roblem.The causal graph has been used both as a tool for describing tract able subclasses of planning problems (e.g., [7], [2], [4]) and as a ke y property which algorithms that adress the general planning problem take in to consideration [5]. In the present work we show that solving planning domains whe re the causal graph is a polytree (that is, the underlying undir ected graph is acyclic) is NP-complete, even if we restrict to domains wi th binary variables and unary operators. This result closes the compl exity gap that appears in [4], where it is shown that plan existence is NP-co mplete for planning domains with singly connected causal graphs, and t hat plan generation is polynomial for planning domains with polytre e causal graphs of bounded indegree. Additionally, it is known that solving unary operator plann ing problems on binary variables is essentially equivalent to solvi ng dominance queries for binary-valued CP-nets (see [1]). Under this ref ormulation the causal graph becomes the CP-net, so the present work also sho ws that dominance testing for binary-valued polytree CP-nets is NP -complete.

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.

The quality of the ranking function is an important factor that determines the quality of the Information Retrieval system. Each document is assigned a score by the ranking function; the score indicates the likelihood of relevance of the document given a query. In the vector space model, the ranking function is defined by a mathematic expression. We propose a fuzzy logic (FL) approach to defining the ranking function. FL provides a convenient way of converting knowledge expressed in a natural language into fuzzy logic rules. The resulting ranking function could be easily viewed, extended, and verified: * if (tf is high) and (idf is high) > (relevance is high); * if (overlap is high) > (relevance is high). By using above FL rules, we are able to achieve performance approximately equal to the state of the art search engine Apache Lucene (deltaP10 +0.92%; deltaMAP -0.1%). The fuzzy logic approach allows combining the logic-based model with the vector model. The resulting model possesses simplicity and formalism of the logic based model, and the flexibility and performance of the vector model.

We are developing a system that aims to perf orm the same inferences as a human reader, on car-crash reports. More precisely, we expect it to determine the causes of the accident as they appear from the text. We describe the genera l semantic framework in which our study takes place, the linguistic and semantic levels of analysis, and the inference rules used by the system.

To understand texts written in natural language (LN), we use our knowledge about the norms of the domain. Norms allow to infer more implicit information from the text. This kind of information can, in general, be defeasible, but it remains useful and acceptable while the text do not contradict it explicitly. In this paper we describe a non-monotonic reasoning system based on the norms of the car crash domain. The system infers the cause of an accident from its textual description. The cause of an accident is seen as the most specific norm which has been violated. The predicates and the rules of the system are stratified: organized on layers in order to obtain an efficient reasoning.

Truth based entailments are not sufficient for a good comprehension of NL. In fact, it can not deduce implicit information necessary to understand a text. On the other hand, norm based entailments are able to reach this goal. This idea was behind the development of Frames (Minsky 75) and Scripts (Schank 77, Schank 79) in the 70's. But these theories are not formalized enough and their adaptation to new situations is far from being obvious. In this paper, we present a reasoning system which uses norms in a causal reasoning process in order to find the cause of an accident from a text describing it.