Knowledge-based recommenders support users in the identification of interesting items from large and potentially complex assortments. In cases where no recommendation could be found for a given set of requirements, such systems propose explanations that indicate minimal sets of faulty requirements. Unfortunately, such explanations are not personalized and do not include repair proposals which triggers a low degree of satisfaction and frequent cancellations of recommendation sessions. In this paper we present a personalized repair approach that integrates the calculation of explanations with collaborative problem solving techniques. In order to demonstrate the applicability of our approach, we present the results of an empirical study that show significant improvements in the accuracy of predictions for interesting repairs.

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.

This is also a key issue in formalizing deals with incomplete information, preferences, negotiation, which seems to prefer argumentationbased and changing goals. We assume that each negotiation [Rahwan et al., 2003]. Recent proposals agent is equipped with a knowledge base for negotiation on formalizing negotiation (see, e.g., [Amgoud et al., 2006; which consists of a CRprogram, a set of possible Kakas and Moraitis, 2006; Rahwan et al., 2003]) seem to be assumptions, and a set of ordered goals.

People have to make important decisions within a time frame. Hence, it is imperative to employ means or strategy to aid effective decision making. Consequently, Economic Intelligence (EI) has emerged as a field to aid strategic and timely decision making in an organization. In the course of attaining this goal: it is indispensable to be more optimistic towards provision for conservation of intellectual resource invested into the process of decision making. This intellectual resource is nothing else but the knowledge of the actors as well as that of the various processes for effecting decision making. Knowledge has been recognized as a strategic economic resource for enhancing productivity and a key for innovation in any organization or community. Thus, its adequate management with cognizance of its temporal properties is highly indispensable. Temporal properties of knowledge refer to the date and time (known as timestamp) such knowledge is created as well as the duration or interval between related knowledge. This paper focuses on the needs for a user-centered knowledge management approach as well as exploitation of associated temporal properties. Our perspective of knowledge is with respect to decision-problems projects in EI. Our hypothesis is that the possibility of reasoning about temporal properties in exploitation of knowledge in EI projects should foster timely decision making through generation of useful inferences from available and reusable knowledge for a new project.

I discuss (ontologies_and_ontological_knowledge_bases / formal_methods_and_theories) duality and its category theory extensions as a step toward a solution to Knowledge-Based Systems Theory. In particular I focus on the example of the design of elements of ontologies and ontological knowledge bases of next three electronic courses: Foundations of Research Activities, Virtual Modeling of Complex Systems and Introduction to String Theory.

Empirical testing is a very popular evaluation method for the development of intelligent systems. Here, previously solved problems with correct solutions are given as cases to the system. Validity is tested by comparing the expected results with the derived solutions. Besides classic forms of boolean testing of occurring solutions more refined methods are required for a thorough evaluation of real world knowledge systems. We present extended precision and recall functions for interactive knowledge systems that are generalizations of the existing measures. Additionally, we propose a visualization method for inspecting the validation result for interactive systems. A case study with a second-opinion system from the medical domain demonstrates the usefulness of the approach.

Generally, learning systems suffer from a lack of an explicit and adaptable didactic design. A previously introduced modeling approach called storyboarding is setting the stage to apply Knowledge Engineering Technologies to verify and validate the didactics behind a learning process. Moreover, didactics can be refined according to revealed weaknesses and proven excellence. Successful didactic patterns can be explored by applying mining techniques to the various ways students went through the storyboard and their associated level of success.

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.

Recently, the development of distributed knowledge systems has become more attractive due to the existence of new social semantic applications such as semantic knowledge wikis. User-friendly tools like wikis allow for a simple acquisition of formal knowledge, but also pose new challenges in knowledge engineering. In this paper, we reconsider classic criteria for verification in the light of a distributed knowledge base and we discuss novel anomalies that possibly occur during the collaborative development of a distributed knowledge base.

Quality management, analysis and evaluation of intelligent systems are important tasks. This paper proposes a data mining approach based on the technique of subgroup mining utilizing a data warehouse that contains data from the respective intelligent system to be evaluated and from other external sources. The context of our work is given by an intelligent documentation and consultation system in the medical domain of sonography. For demonstrating the applicability and benefit of the presented approach, we provide several realworld examples of a case-study applying the approach in the medical domain of sonography.