Invited Talks

Case-Based Reasoning and Expert Systems

Abstract

Case-based reasoning (CBR) and expert systems have a long tradition in artificial intelligence: CBR since the late 1970s and expert systems since the late 1960s. While expert systems are based on expertise and expert reasoning capabilities for a specific area of responsibility, CBR is an approach for problem solving and learning of humans and computers. Starting from different research activities, CBR and expert systems have become overlapping research fields. In this talk the relationships between CBR and expert systems are analyzed from different perspectives like problem solving, learning, competence development, and knowledge types. As human case-based reasoners are quite successful in integrating problem-solving and learning, combining different problem solving strategies, utilizing different kinds of knowledge, and becoming experts for specific areas of responsibility, computer based expert systems do not have the reputation to be successful at these tasks. Based on this, the potential of CBR succeeding as future expert systems is discussed.

Short Bio

Klaus-Dieter Althoff is professor for Artificial Intelligence at the University of Hildesheim (UHI), Germany, and since May 2010 he is leading the Competence Center Case-Based Reasoning at the German Research Center on Artificial Intelligence (DFKI) in Kaiserslautern based on a cooperation contract between DFKI and UHI. Klaus received a PhD on learning expert systems for technical diagnosis (1992) and a habilitation degree on evaluation of case-based reasoning (CBR) systems (1997) both from University of Kaiserslautern. Before entering UHI in 2004, he worked for the Fraunhofer Institute for Experimental Software Engineering since 1997, where he was responsible for experience management systems and processes. He was program co-chair of ICCBR’99 and ECCBR’08 as well as local co-chair of EWCBR’93. He was/is team member and/or project leader of a number of projects on CBR and related research topics. The current research focus includes modeling expertise in its different facets, knowledge engineering and extraction for CBR, distributed architectures with CBR, integration of CBR with various semantic technologies, deep integration between CBR and explanation reasoning, and learning expert systems. At DFKI, the Competence Centers CBR (Kerstin Bach) and Multi-Media Analysis and Data Mining (Dr. Armin Stahl) are responsible for the CBR tool myCBR, which is available as an open source project and further developed in the CBR-related projects at DFKI as well as in a joint project between DFKI and Prof. Roth-Berghofer (University of West London).

Contact

Prof. Dr. Klaus-Dieter Althoff Intelligent Information Systems, University of Hildesheim Head of Competence Center Case-Based Reasoning German Research Center for Artificial Intelligence (DFKI) Trippstadter Strasse 122 D-67663 Kaiserslautern, Germany Phone: +49 631-20575-1460 Fax: +49 631-20575-1010 Email: klaus-dieter.althoff@dfki.de

Reproducibility and Efficiency of Scientific Data Analysis

Scientific Workflows and Case-Based Reasoning

Yolanda Gil

Abstract

Scientists carry out complex scientific data analyses by managing and executing many related computational steps. Typically, scientists find a type of analysis relevant to their data, implement it step by step to try it out, and run many variants as they explore different datasets or method configurations. These processes are often done manually and are prone to error, slowing the pace of discoveries. Scientific workflows have emerged as a formalism to represent how the individual steps work and how they relate to the overall process. Workflows can be published, discovered, and reused to make data analysis processes more efficient through automation and assistance. In this talk, I will argue that integrating case-based reasoning techniques with workflows research would result in improved approaches to workflow sharing, retrieval, and adaptation. I will describe our initial work on semantic workflow matching using labeled graphs and knowledge intensive similarity measures. Furthermore, I will argue that if scientists followed a case-based approach more closely, scientific results would be more easily inspectable and reproducible. Through scientific workflows and case-based reasoning, scientific data analysis could be made more efficient and more rigorous.

Short Bio

Dr. Yolanda Gil is Director of Knowledge Technologies and Associate Division Director at the Information Sciences Institute of the University of Southern California, and Research Professor in the Computer Science Department. She received her M.S. and Ph. D. degrees in Computer Science from Carnegie Mellon University. Dr. Gil leads a group that conducts research on various aspects of Interactive Knowledge Capture. Her research interests include intelligent user interfaces, knowledge-rich problem solving, scientific and grid computing, and the semantic web. An area of recent interest is large-scale distributed data analysis through semantic workflows. Dr. Gil was elected to the Council of the American Association of Artificial Intelligence (AAAI), and served in the Advisory Committee of the Computer Science and Engineering Directorate of the National Science Foundation. She recently led the W3C Provenance Group, an effort to chart the state-of-the-art and posit standardization efforts in this area. In 2010 she was elected Chair of ACM SIGART, the Association for Computing Machinery's Special Interest Group on Artificial Intelligence.