Klaus-Dieter Althoff

A case-based reasoning approach for providing machine diagnosis from service reports

This paper presents a case-based reasoning system that has been applied in a machine diagnosis customer support scenario. Complex machine problems are solved by sharing machine engineers experiences among technicians. Within our approach we made use of existing service reports, extracted machine diagnosis information and created a case base out it that provides solutions faster and more efficient than the traditional approach. The problem solving knowledge base is a data set that has been collected over about five years for quality assurance purposes and we explain how existing data can be used to build a case-based reasoning system by creating a vocabulary, developing similarity measures and populating cases using information extraction techniques.

Migrating the Classical Pen-and-Paper Based Conceptual Sketching of Architecture Plans Towards Computer Tools - Prototype Design and Evaluation

While computer-based design tools are widely used in architecure during late design phases for creating final floor plans, early design phases usually still take place in a traditional manner, using pen, paper and scissors. At the beginning of these phases, there is often only a rough idea of how a building should look like. Viewing existing floorplans of similar buildings can help an architect in his/her creative work, but searching for those plans manually is very time-consuming. Automated tools for searching similar floor plans could help to lower the amount of time needed for such investigations tremendously. In order to employ such search mechanisms, proper user interfaces are needed that fit to the architect’s working process. These interfaces should be useable easily and naturally, requiring less initial training. They should be capable of creating search requests that can be processed by the attached search mechanism. In this article, we describe two different user interfaces to serve this purpose. We describe their structures and interaction principles. Afterwards we show their general usability and user acceptance by the means of a users study.

Multi-Agent-Based Generation of Explanations for Retrieval Results Within a Case-Based Support Framework for Architectural Design.

In this paper, we describe the general structure and evaluation of a multi-agent based system module that was conceptualized to explain, and therefore, enrich the search results of the retrieval process within a distributed case-based framework for support of early conceptual design phase in architecture. This explanation module is implemented as an essential part of the framework and uses case-based agents, explanation ontology, and explanation patterns as its underlying foundational components. The module’s main goal is to provide the user with additional information about the search results to make the framework’s behavior during the retrieval stage more transparent and traceable. System’s justification for displaying of results plays an important role as well, and is also included in the explanations. We evaluated the explanation generation process with a ground-truth set of explanations and a case-based validation process to ensure the suitability of the generated explanation expressions for displaying in user interfaces connected to the framework. The results of the evaluation confirmed our expectation and showed the general validity of the explanations.

Answering with Cases: A CBR Approach to Deep Learning

Every year tenths of thousands of customer support engineers around the world deal with, and proactively solve, complex help-desk tickets. Daily, almost every customer support expert will turn his/her attention to a prioritization strategy, to achieve the best possible result. To assist with this, in this paper we describe a novel case-based reasoning application to address the tasks of: high solution accuracy and shorter prediction resolution time. We describe how appropriate cases can be generated to assist engineers and how our solution can scale over time to produce domain-specific reusable cases for similar problems. Our work is evaluated using data from 5000 cases from the automotive industry.

Dynamic Case Bases and the Asymmetrical Weighted One-Mode Projection

Building a case base for a case-based reasoning (CBR) system is incomplete without similarity measures. For the attribute-value case structure similarity between values of an attribute should logically fit their relationship. Bipartite graphs have been shown to be a good representation of relationships between values of symbolic attributes and the diagnosis of the cases in a technical diagnosis CBR system, while using an asymmetrical weighted one-mode projection on the values to model their similarity.

Agent Based Framework to Support Manufacturing Problem Solving Integrating Product Lifecycle Management and Case-Based Reasoning

During the execution of manufacturing processes, problems arise and they have to be solved systematically to reach and exceed production targets. Normally, a production team analyzes and solves these problems, with the support of different methodologies and working directly on the shop floor. This paper presents an ontology-based approach to easily capture and reuse the knowledge generated in such a process of Manufacturing Problem Solving (MPS). The proposed ontology is used as basis in an ad-hoc MPS software system. The architecture of the MPS system is based on the integration of three technologies: PLM (Product Lifecycle Management), CBR (Case-Based Reasoning) and software agents. The PLM system is used as an automatic source of the problem context information. The CBR system is used as repository of cases and artificial intelligence tool to support the efficient reuse of knowledge during the resolution of new problems. A software agent platform allows developing an integrated prototype of an ad-hoc software system. This paper shows the architecture of the MPS system prototype.