Extraction of association rules is widely used as a data mining method. However, one of the limit of this approach comes from the large number of extracted rules and the difficulty for a human expert to deal with the totality of these rules. We propose to solve this problem by structuring the set of rules into hierarchy. The expert can then therefore explore the rules, access from one rule to another one more general when we raise up in the hierarchy, and in other hand, or a more specific rules. Rules are structured at two levels. The global level aims at building a hierarchy from the set of rules extracted. Thus we define a first type of rule-subsomption relying on Galois lattices. The second level consists in a local and more detailed analysis of each rule. It generate for a given rule a set of generalization rules structured into a local hierarchy. This leads to the definition of a second type of subsomption. This subsomption comes from inductive logic programming and integrates a terminological model.

This paper deals with computational approaches to storytelling, or the production of stories by computers, with a particular attention on the way human creativity is modelled or emulated, also in computational terms. Features relevant to creativity and to stories are analysed, and existing systems are reviewed under the light of that analysis.The extent to which they implement the key features proposed in recent models of computational creativity is discussed. Limitations, avenues of future research and expected trends are outlined.

Motivated by Zadeh's paradigm of computing with words rather than numbers, several formal models of computing with words have recently been proposed. These models are based on automata and thus are not well-suited for concurrent computing. In this paper, we incorporate the well-known model of concurrent computing, Petri nets, together with fuzzy set theory and thereby establish a concurrency model of computing with words--fuzzy Petri nets for computing with words (FPNCWs). The new feature of such fuzzy Petri nets is that the labels of transitions are some special words modeled by fuzzy sets. By employing the methodology of fuzzy reasoning, we give a faithful extension of an FPNCW which makes it possible for computing with more words. The language expressiveness of the two formal models of computing with words, fuzzy automata for computing with words and FPNCWs, is compared as well. A few small examples are provided to illustrate the theoretical development.

This research has been supported by Air Force Research Laboratory, Rome, NY, USA, in June and July 2009. Florentin Smarandache, Mark Alford Air Force Research Laboratory, RIEA, 525 Brooks Rd., Rome, NY 13441-4505, USA Abstract: In this paper we introduce two new DSm fusion conditioning rules with example, and as a generalization of them a class of DSm fu sion conditioning rules, and then extend them to a class of DSm conditioning rules. Keywords: conditional fusion rules, Dempster's conditioning rule, Dezert-Smarandache Theory, DSm conditioning rules 0. Introduction In order to understand the material in this paper, it is first necessary to define the terms that we'll be using: - Frame of discernment th e set of all hypotheses. This research has been supported by Air Force Research Laboratory, Rome, NY, USA, in June and July 2009. In the case when their intersection is empty, we consider these hypotheses disjoint.}

CSIEC (Computer Simulation in Educational Communication), is not only an intelligent web-based human-computer dialogue system with natural language for English instruction, but also a learning assessment system for learners and teachers. Its multiple functions—including grammar-based gap filling exercises, scenario show, free chatting and chatting on a given topic—can satisfy the various requirements for students with different backgrounds and learning abilities. After a brief explanation of the conception of our dialogue system, as well as a survey of related works, we will illustrate the system structure, and describe its pedagogical functions with the underlying AI techniques in detail such as NLP and rule-based reasoning. We will summarize the free Internet usage within a six month period and its integration into English classes in universities and middle schools. The evaluation findings about the class integration show that the chatting function has been improved and frequently utilized by the users, and the application of the CSIEC system on English instruction can motivate the learners to practice English and enhance their learning process. Finally, we will conclude with potential improvements.

The hardness of finite domain Constraint Satisfaction Problems (CSPs) is a very important research area in Constraint Programming (CP) community. However, this problem has not yet attracted much attention from the researchers in the association rule mining community. As a popular data mining technique, association rule mining has an extremely wide application area and it has already been successfully applied to many interdisciplines. In this paper, we study the association rule mining techniques and propose a cascaded approach to extract the interesting patterns of the hard CSPs. As far as we know, this problem is investigated with the data mining techniques for the first time. Specifically, we generate the random CSPs and collect their characteristics by solving all the CSP instances, and then apply the data mining techniques on the data set and further to discover the interesting patterns of the hardness of the randomly generated CSPs.

The problem of detecting terms that can be interesting to the advertiser is considered. If a company has already bought some advertising terms which describe certain services, it is reasonable to find out the terms bought by competing companies. A part of them can be recommended as future advertising terms to the company. The goal of this work is to propose better interpretable recommendations based on FCA and association rules.

In rules considered in this paper, the conclusion may contain existentially quantified variables, which makes reasoning tasks (as deduction) non-decidable. These rules have the same logical form as TGD (tuple generating dependencies) in databases and as conceptual graph rules. We extend known decidable cases by combining backward and forward chaining schemes, in association with a graph that captures exactly the notion of dependency between rules. Finally, we draw a map of known decidable cases, including an extension obtained by combining our approach with very recent results on TGD.

When mathematicians present proofs they usually adapt their explanations to their didactic goals and to the (assumed) knowledge of their addressees. Modern automated theorem provers, in contrast, present proofs usually at a fixed level of detail (also called granularity). Often these presentations are neither intended nor suitable for human use. A challenge therefore is to develop user- and goal-adaptive proof presentation techniques that obey common mathematical practice. We present a flexible and adaptive approach to proof presentation that exploits machine learning techniques to extract a model of the specific granularity of proof examples and employs this model for the automated generation of further proofs at an adapted level of granularity.

The predicates that are used to encode a planning domain in PDDL often do not include concepts that are important for effectively reasoning about problems in the domain. In particular, the effectiveness of rule-based policies in a domain depend on the concepts that can be expressed in the language used to capture those policies. In this work we investigate complimenting planning domain descriptions with abstract concepts and methods for making distinctions between similar objects. We present an architecture that allows a rule-based policy to reason with these additional concepts, using them to reason over structures that the rules would not be able to reason over without support. We demonstrate that this is sufficient to allow a rule-based policy to provide control in benchmark domains with interesting structures and we argue that our architecture could allow control knowledge learners to learn policies that provide control in these domains.