A particular strength of case-based reasoning (CBR) over most other methods is its inherent combination of problem solving with sustained learning through problem solving experience. This is therefore a particularly important topic of study, and an issue that has now become mature enough to be addressed in a more systematic way. To enable such an analysis of problem solving and learning, we have initiated work towards the development of an analytic framework for studying CBR methods. It provides an explicit ontology of basic CBR task types, domain characterisations, and types of problem solving and learning methods. Further, it incorporates within this framework a methodology for combining a knowledge-level, top-down analysis with a bottom-up, case-driven one. In this article, we present the underlying view and the basic approach being taken, the main components of the framework and accompanying methodology, examples of studies recently done and how they relate to the framework. 1.

. The paper describes a novel architecture for image understanding. It is based on acquisition of radiologist knowledge, and combines low-level structure analysis with high-level interpretation of image content, within a task-oriented model. A case based reasoner working on a segment case-base contains the individual image segments. These cases with labels are considered indexes for another case based reasoner working on an organ interpretation case base. Both are Creek type case based reasoners, here operating within a propose-critique -modify task structure. Methods for criticizing suggested interpretations by way of explanation, and how interpretations may be modified, are presented. An example run illustrates the system architecture and its key concepts. 1 Introduction Image understanding has turned out to be a very difficult application task for AI methods. Methods exist that are able to do edge detection, and to some extent object identification, but methods for interp...

. The recent progress in Case- Based Reasoning has shown that one of the most important challenges in developing future AI methods will be to combine and synergistically utilize general and case-based knowledge. In this paper a very rudimentary kind of integration for the classification task, based on simple heuristics, is sketched: "To solve a problem, first try to use the conventional rulebased approach. If it does not work, try to remember a similar problem you have solved in the past and adapt the old solution to the new situation". This heuristic approach is based on the knowledge base that consists of rule base and exception case base. The method of generating this kind of knowledge base from a set of examples is described. The proposed approach is tested, and compared with alternative approaches. The experimental results show that the presented integration method can lead to an improvement in accuracy and comprehensibility. 1 Introduction The recent progress in Case- Based Rea...

We have argued elsewhere that user goals should be taken into account when deciding what kind of explanation of its results a CBR system should give. In this paper, we propose the use of an Activity Theory based methodology for identifying di#erent user goals and expectations towards explanations given by a system supporting a work process.

Abstract. One of the cornerstones of any intelligent entity is the ability to understand how occurrences in the surrounding world influence its own behaviour. Different states, or situations, in its environment should be taken into account when reasoning or acting. When dealing with different situations, context is the key element used to infer possible actions and information needs. The activities of the perceiving agent and other entities are arguably one of the most important features of a situation; this is equally true whether the agent is artificial or not. This work proposes the use of Activity Theory to first model context and further on populate the model for assessing situations in a pervasive computing environment. Through the socio-technical perspective given by Activity Theory, the knowledge intensive context model, utilised in our ambient intelligent system, is designed. 1

The ability to build intelligent agents is significantly constrained by the knowledge acquisition effort required. Many iterations by human experts and knowledge engineers are currently necessary to develop knowledge-based agents with acceptable performance. We have developed a novel approach, called Disciple, for building intelligent agents that relies on an interactive tutoring paradigm, rather than the traditional knowledge engineering paradigm. In the Disciple approach, an expert teaches an agent through five basic types of interactions. Such rich interaction is rare among machine learning systems, but is necessary to develop more powerful systems. These interactions, from the point of view of the expert, include: specifying knowledge to the agent; giving the agent a concrete problem and its solution that the agent is to learn a general rule for; validating analogical problems and solutions proposed by the agent; explaining to the agent reasons for the validation; and being guide...

Computer science technology that can support police activities is wide ranging, from the well known geographical information systems display (’pins in maps’), clustering and link analysis algorithms, to the more complex use of data mining technology for profiling single and series of crimes or offenders, and matching and predicting crimes. This paper presents a discussion of data mining and decision support technologies for police, considering the range of computer science technologies that are available to assist police activities. The discussion is very practical, with examples taken from the authors ’ own work with three United Kingdom police forces. The lessons learned are presented, along with their relevance to future work. We describe significant aspects of the knowledge discovery from databases process, starting with an examination of the data that police collect and the reasons for storing such data, and progressing to the development of crime matching and predictive knowledge which are operationalised in decision support software. Discussion and experimentation include decision support techniques based around spatial statistics, and a wide range of data mining technologies, including case-based reasoning, logic programming and ontologies, survival analysis, Bayesian networks, and the comparison of models that use either behavioural features, spatio-temporal features, or a combination of both. The paper concludes with a discussion of the operational lessons relevant to future work.

The issue of mixed-initiative intelligent systems has gained increasing interest in recent years. In particular, much attention has been paid on sharing the initiative between the user and the system on the tool level. In this paper, we are focusing on the problem of embedding the system into a workplace.

The goal of the presentation is to give an overview about how to build a commodity PC based GNU/Linux cluster for High Performance Computing (HPC) in a research environment. Due to the extreme flexibility of the GNU/Linux operating system and the large variety of hardware components, building a cluster for High Performance Computing (HPC) is still a challenge in many cases. At the Division of Intelligent Systems at the Norwegian University of Science and Technology (NTNU), we have build a 40 node HPC cluster for research purposes using the source-based GNU/Linux distribution Source Mage.