Thomas Roth-Berghofer

Rapid Prototyping of CBR Applications with the Open Source Tool myCBR

Although Case-Based Reasoning (CBR) claims to reduce the effort required for developing knowledge-based systems substantially compared with more traditional Artificial Intelligence approaches, the implementation of a CBR application from scratch is still a time consuming task. In this paper we present a novel, freely available tool for rapid prototyping of CBR applications that focuses on the similarity-based retrieval step, like for example case-based product recommender systems. By providing easy to use model generation, data import, similarity modeling, explanation, and testing functionality together with comfortable graphical user interfaces, the tool enables even CBR novices to rapidly create their first CBR applications. Nevertheless, at the same time it ensures enough flexibility to enable expert users to implement advanced CBR applications.

Explanations and Case-Based Reasoning: Foundational Issues

By design, Case-Based Reasoning (CBR) systems do not need deep general knowledge. In contrast to (rule-based) expert systems, CBR systems can already be used with just some initial knowledge. Further knowledge can then be added manually or learned over time. CBR systems are not addressing a special group of users. Expert systems, on the other hand, are intended to solve problems similar to human experts. Because of the complexity and difficulty of building and using expert systems, research in this area addressed generating explanations right from the beginning. But for knowledge-intensive CBR applications, the demand for explanations is also growing. This paper is a first pass on examining issues concerning explanations produced by CBR systems from the knowledge containers perspective. It discusses what naturally can be explained by each of the four knowledge containers (vocabulary, similarity measures, adaptation knowledge, and case base) in relation to scientific, conceptual, and cognitive explanations.

KI 2008: Advances in Artificial Intelligence

The research in this thesis aims to enable robots to imitate humans. Learning by imitation is a fundamental part of human behaviour, since it allows humans to acquire motor skills simply by demonstration; seen from a robotic viewpoint you can easily “program” your fellow humans by showing them what to do. Would it not be great if the same mechanism could be used to program robots? A robot is programmed by specifying the torque of its motors. The torque can be regarded as the force or strength that is the result of muscles contracting or relaxing. Typical approaches to determine motor torques that will lead to a desired behaviour include setting them manually, i.e. on a trial-and-error basis, or specifying them by mathematical equations. Neither of these are intuitive to most humans, so most robot behaviours are programmed by engineers. However, if an engineer was to design a preprogrammed housekeeping robot, it would be very hard to program all the possible behaviours the robot could be expected to perform, even in such a limited domain. It is much more cost-efficient to make the robot learn what to do. This would allow the robot to adapt to its human owner, and not the other way around. Since humans easily learn new behaviours by imitating others, it would be ideal if humans could use the same technique to transfer motor knowledge to robots. I believe research in this area could be of great help to bridge the human-robot interaction gap that currently exists, so that you could have truly intelligent robots that could assist people in everyday life. To understand imitation learning, knowledge of psychology and neuroscience is required. The research in this thesis has taken an interdisciplinary approach, studying the desired mechanism on both a behavioural and neuroscientific level. I have focused on imitation in a musical setting. The system can both see and hear, and seeks to imitate the perceived behaviour. The application has been to create an intelligent virtual drummer, that imitates both the physical playing style (i.e. the movement of the arms) as well as the musical playing style (i.e. the groove) of the teacher. The virtual drummer will then both look and sound like a human drummer. The research in this thesis presents a multi-modal architecture for imitation of human movements. I have been working on simulated robots due to limits of time and money, however the principles of my research have been developed in a platformindependent way, so it should be applicable to real robots as well.

The development and utilization of the case-based help-desk support system HOMER

Abstract Current case-based reasoning (CBR) process models present CBR as a low-maintenance AI-technology and do not take the processes that have to be enacted during system development and utilization into account. Since a CBR system can only be useful if it is integrated into an organizational structure and used by more than one user, processes for continuous knowledge acquisition, utilization and maintenance have to be put in place. In this paper the shortcomings of classical CBR process models are analyzed, and, based on the experiences made during the development of the case-based help-desk support system HOMER, the managerial, organizational and technical processes related to the development and utilization of CBR systems are described.

Review and Restore for Case‐Base Maintenance

Case‐base maintenance is one of the most important issues for current research in case‐based reasoning (CBR). In this article we propose an extended six‐step CBR cycle and discuss its two additional steps as part of the maintenance phase of the CBR process. The review step covers assessment and monitoring of the knowledge containers, whereas the restore step actually modifies the contents of the containers according to recommendations resulting from the review step in order to keep the knowledge containers in a usable state. Here we focus our attention on the case base. For the review step, we define several quality measures based on different case and case‐base properties that describe specific characteristics of the case base such as correctness, consistency, uniqueness, minimality, and incoherence. Then we use these measures to realize monitoring capabilities for the case‐base container that indicate when the restore step is necessary. Finally, we also describe several methods for modifications of the case base in the restore step and their relation to the review step. An initial experimental evaluation shows the appropriateness of the proposed concepts and methods before we conclude the article with a discussion of related work and an outline of future directions to extend these aspects of maintenance in CBR.

On Quality Measures for Case Base Maintenance

Case base maintenance is one of the most important issues for current research in Case-Based Reasoning (CBR). In this paper, we outline two novel steps as part of the maintenance phase of the CBR process. The review step covers assessment and monitoring of the knowledge containers whereas the restore step actually modifies the contents of the containers according to recommendations resulting from the review step. Here, we focus our attention on the review step for the case base. For this purpose, we define several quality measures based on different case and case base properties that describe specific characteristics of the case base such as correctness, consistency, uniqueness, minimality, and incoherence. These measures allow an initial implementation of the review step for the case base container. We conclude the paper with an outline of future work to extend these aspects of maintenance in CBR.

Mapping goals and kinds of explanations to the knowledge containers of case-based reasoning systems

Research on explanation in Case-Based Reasoning (CBR) is a topic that gains momentum. In this context, fundamental issues on what are and to which end do we use explanations have to be reconsidered. This article presents a prelimenary outline of the combination of two recently proposed classifications of explanations based on the type of the explanation itself and user goals which should be fulfilled. Further on, the contribution of the different knowledge containers for modeling the necessary knowledge is examined.

Learning from HOMER, a Case-Based Help Desk Support System

The Homer help desk support system, developed in the course of the Inreca-II project, is an example of applying Case-Based Reasoning to problems of a software-intensive organization. This paper discusses, in retrospect, Homer with respect to change management processes at DaimlerChrysler and various feedback and learning possibilites that the help desk support system provided for itself, the help desk personnel, and the supported IT infrastructure of the car development department in Sindelfingen.

The Development of HOMER: A Case-Based CAD/CAM Help-Desk Support Tool

The increasing number of hardware and software at Daimler-Benz personal car development in Sindelfingen combined with the constant number of help-desk operators demanded a help-desk system which goes beyond the classical trouble-ticket approach. In this application paper we give an overview of the situation at the CAD/CAM Help-Desk in Sindelfingen and the development of the case-based help-desk support tool HOMER. We describe our modeling approach and its influence on the system architecture as well as the different user roles and the help-desk tool itself. We conclude with the lessons learned during the course of this project and future prospects.

The Mining and Analysis Continuum of Explaining Uncovered

The result of data mining is a set of patterns or models. When presenting these, all or part of the result needs to be explained to the user in order to be understandable and for increasing the user acceptance of the patterns. In doing that, a variety of dimensions for explaining needs to be considered, e.g., from concrete to more abstract explanations. This paper discusses a continuum of explaining for data mining and analysis: It describes how data mining results can be analysed on continuous dimensions and levels.