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.

Design, evaluation and refinement of intelligent systems was a popular topic at FLAIRS 2008. More and more authors have realized that the lack of systematic methods and formal techniques for the design, the evaluation, and the refinement are often important reasons for not using AI systems in practice. The first contributions in this field were limited to classical AI approaches such as rule-based systems. Actually, more and more papers regarding nonclassical types of systems (like case-based systems, for example), knowledge processing principles (learning principles, for example), and intelligent behavior (game strategies, for example) are published. Rule-based systems are still a subject of the track, but the focus is widened from their verification and validation towards also covering the design issue.

In this paper certain knowledge and software engineering methods integration issues are discussed. The principal idea is to consider an effective design and implementation framework for rule design with UML, and implementation with Java. The solution proposed in the paper consists of using a custom knowledge engineering design method for rules in the design stage. The rule base is then transformed to UML behavioral diagrams, which can be considered a visual encoding. The rule implementation involves the serialization to Java language using classes representing the decision tables grouping rules sharing the same attributes.

The success of knowledge discovery in real-world domains often depends on our ability to handle data imperfections. Here we study this problem in the framework of association mining, seeking to identify frequent itemsets in transactional databases where the presence of some items in a given transaction is unknown. We want to use the frequent itemsets to predict "missing items": based on the partial contents of a shopping cart, predict what else will be added. We describe a technique that addresses this task, and report experiments illustrating its behavior.

Evaluating changes in documentation of technical products is a key issue in knowledge management. A product may be declined in different versions and one way to evaluate changes is to compare the sets of documents which describe each version. The aim of this paper is to propose a framework for exhibiting changes between sets of documents. This framework is based on the representation of the sets of documents in terms of association rules and on the definition of first order predicates for reasoning with these association rules. The aim of the reasoning stage is to exhibit the differences between the sets of documents. These predicates show what rules are specific to a corpus or how differs the usage of concepts appearing in the associations rules. The framework is  experimented with the comparison of two corpuses of documents which describe documentation about two different versions of a spatial component.

In this search, we focused on supervised learning task using association rules algorithms (association based classification). These algorithms, developed in unsupervised learning, extract all the rules whose the support and confidence exceed a prefixed threshold support. After extracting the frequent itemsets, (i.e their support exceeds the threshold support), algorithms subdivide these itemsets to build the rules, and keep only the rules whose confidence exceeds the threshold confidence. The extraction of class association rules, using these algorithms, have several problems, because of the rules' a posteriori filtering. In the first stage, one extracts useless frequent itemsets, those which do not contain class, whereas the second stage can be simplified, since an itemset containing the class gives place only to only one class rule. In order to be able to work with a low threshold support, we propose FCP-Growth an adaptation of FP-Growth which eliminates the frequent itemsets not containing a class. Moreover, to make the minority class be in advantage during the construction of the class itemsets, we adapt the threshold support, in order to use the same threshold support inside each class.

What is the biggest difference between playing a game against a human or against a computer generated player? Why do many people believe it is more challenging to play with humans than playing with an artificial player? The big success of massive multiplayer games and the huge number of so-called "LAN parties", where players meet and play with each other, seems to be related to the human demeanor of the players. All this indicates, that the current state of game AI is unsatisfactory compared to the performance of human players. This paper introduces a tool for analyzing basic computer games with incorporated AI modules which store strategies for performing the behavior of artificial players. This sets the stage for a systematic evaluation and refinement of rule based game AI.

In this paper knowledge representation and inference issues for rule-based systems are discussed. The paper deals with improving the logical calculus of Set Attributive Logic founding an expressive rule language XTT2. Representation extensions are introduced, and practical inference rules provided. The original includes an extended state specification, as well as interpreter design. xamples of rule analysis are given. Visual design tool HQed assuring rule quality is also presented.

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.

Route robustness is therefore a Vehicle route planning is a popular application of AI automated measure against the risk that the solution may not be executed planning methods. In numerous applications it is according to the a priori plan. The main idea behind supported with GPS navigation. Based on a generalized the concept of a robust plan is that such a plan should consist shortest-path approach it uses a directed graph as the search of numerous alternative plans, represented in a concise way, domain and edge weights set to match the required optimality and enable switching from the plan currently being executed criteria. Moreover, various additional constraints and to a new one as often as may become necessary. The degree heuristic information can be explored. (Nau, Ghallab, and of robustness is a qualitative factor referring to numerous Traverso 2004)