Invited Talks

Mary Lou Maher
Professor and Chair Department of Software and Information Systems, University of North Carolina Charlotte

Talk Abstract: The benefit of Case-based Reasoning (CBR) and Recommender Systems is the direct use of past experience to guide the synthesis or selection of the best solution for a specific context or user. Typically, the solution presented to the user is based on a value system that privileges the closest match in a query and the solution that performs best when evaluated according to predefined requirements. In domains in which creativity is desirable or the user is engaged in a learning activity, there is a benefit to moving beyond the expected or “best match” and include results based on computational models of novelty and surprise. In this presentation, models of novelty and surprise are integrated with CBR and Recommender Systems to encourage user curiosity.

Biography: Mary Lou Maher is Professor and Chair of Software and Information Systems at UNC Charlotte and Honorary Professor of Design Computing in the Design Lab at the University of Sydney. She completed a Bachelor of Engineering at Columbia University in 1979, and a Master of Science and PhD at Carnegie Mellon University, completing the PhD in 1984. Dr. Maher’s research interests include computational creativity, design cognition and computing, and CS education. She is Director of the Center for Education Innovation in the College of Computing and Informatics at UNCC and is Principle Investigator on NSF funded projects on cognitive models of curiosity in learning, crowdsourcing design for citizen science, transforming CS education using active learning.

Mary Lou Maher

Henri Prade
CNRS Research Advisor

Talk Abstract: Analogical proportions are statements of the form “a is to b as c is to d”. For more than a decade now, their formalization and use have raised the interest of a number of researchers. In this talk we shall primarily focus on their modeling in logical settings, both in the Boolean and in the multiple-valued cases. This logical view makes clear that analogy is as much a matter of dissimilarity as a matter of similarity. Moreover analogical proportions emerge as being especially remarkable in the framework of logical proportions. The analogical proportion and seven other code independent logical proportions can be shown as being of particular interest. Besides, analogical proportions are at the basis of an inference mechanism which enables us to complete or create a fourth item from three other items. The relation with case-based reasoning and decision will be emphasized. Potential applications and current developments will also be discussed.

Henri Prade

Agnar Aamodt
Professor of Computer Science and Artificial Intelligence, and Head of Research, at the Department of Computer Science, Norwegian University of Science and Technology (NTNU)

Enric Plaza
Research Professor at the IIIA-CSIC (Artificial Intelligence Institute of the Spanish Council for Scientific Research)

Talk Abstract: The history and evolution of AI has shaped Case-based Reasoning research and applications. In this talk we start out from the early days of AI, and discuss the alternations of AI winters and AI summers that have encompassed the main AI landmarks. We then turn to the recent growth in scope and strength of AI and Machine Learning in society, discuss its relevance to CBR and which lessons can be learnt, both for data-driven and knowledge-intensive CBR. The influence of recent contributions from Cognitive Science is also discussed. Finally, some uture challenges and opportunities for CBR will be presented for an open debate.

Biography: Agnar Aamodt is Professor of Computer Science and Artificial Intelligence, and Head of Research, at the Department of Computer Science, Norwegian University of Science and Technology (NTNU). He has more than 30 years of experience within AI and Machine Learning, and more than 20 years within Case-Based Reasoning. His research centers around active computer-support for human problem solving and learning through domain knowledge modelling, automated data analysis, and reuse of human experience. CBR is his main approach to problem solving and learning, targeting situation understanding, decision support, and action control. A particular interest is how reasoning from concrete experiences can be integrated with generalization-based and bio-inspired easoning and learning methods. Application focus is pro-active decision support in complex domains, with oil well-drilling, medicine, transportation, and fish farming as most recent examples. He has been involved in a range of European research projects, including the current H2020 project selfBACK on activity recognition and advice giving to patients with physical disorders. He was Program co-chair of the 1st ICCBR in 1995, and local chair of ICCBR 2003.

Agnar Aamodt

Biography: Enric Plaza holds the position of Research Professor at the IIIA-CSIC (Artificial Intelligence Institute of the Spanish Council for Scientific Research), that he joined in 1988, where he is currently Head of the Learning Systems Department. He holds a title of Engineer in Computer Science by the Technical University of Catalonia (UPC) since 1984 and a Ph.D. in Artificial Intelligence by UPC in 1987. His research has spanned different areas of Artificial Intelligence, including knowledge acquisition and validation for expert systems, case-based reasoning, machine learning, and multiagent systems. His research is now focused on new techniques for case-based reasoning, the web of experiences, learning from argumentation, and computational creativity. He is author or co-author of more than 150 scientific papers in journals and conferences, and has worked on 17 international projects and 24 Spanish projects in Artificial Intelligence. He is also an ECCAI Fellow and has been appointed chairman of several international conferences like EKAW-97, ICCBR-97, ECML-2000 and ICCBR-14. He was the first President of the Catalan Association for Artificial Intelligence (ACIA) from 1994 to 1998.

Enric Plaza

Accepted Papers

  • A Hybrid CBR approach for the Long Tail Problem in Recommender Systems
    Gharbi Alshammari, Jose L. Jorro-Aragoneses, Stelios Kapetanakis, Miltos Petridis, Juan A. Recio-Garcia and Belen Diaz-Agudo
  • Extending the Flexibility of Case-Based Design Support Tools: A Use Case in Architectural Domain
    Viktor Ayzenshtadt, Christoph Langenhan, Syed Saqib Bukhari, Klaus-Dieter Althoff, Frank Petzold and Andreas Dengel
  • A Reasoning Model based on Perennial Crop Allocation Cases and Rules
    Florence Le Ber, Xavier Dolques, Laura Martin, Marc Benoit and Alain Mille
  • A SPARQL Query Transformation Rule Language: Application to Retrieval and Adaptation in Case-Based Reasoning
    Olivier Bruneau, Emmanuelle Gaillard, Nicolas Lasolle, Jean Lieber, Emmanuel Nauer, and Justine Reynaud
  • Similar Users or Similar Items? Comparing Similarity-based Approaches for Recommender Systems in Online Judges
    Marta Caro-Martinez and Guillermo Jimenez-Diaz
  • Tetra: A Case-Based Decision Support System Assisting Nuclear Physicians in Image Interpretation
    Mohammad B. Chawki, Emmanuel Nauer, Nicolas Jay, and Jean Lieber
  • Case-based Team Recognition Using Learned Opponent Models
    Michael W. Floyd, Justin Karneeb, and David W. Aha
  • The Mechanism of Influence of a Case-based Health Knowledge System on Hospital Management Systems
    Dongxiao Gu, Jingjing Li, Isabelle Bichindaritz, Shuyuan Deng and Changyong Liang
  • Scaling Up Ensemble of Adaptations for Classification by Approximate Nearest Neighbor Retrieval
    Vahid Jalali and David Leake
  • A CBR System for Efficient Face Recognition under Partial Occlusion
    Daniel López-Sánchez, Juan M. Corchado and Angélica Gonzalez Arrieta
  • Time Series and Case-based Reasoning for an Intelligent Tetris Game
    Diana Lora, Antonio A. Sánchez-Ruiz and Pedro González Calero
  • Case-Based Reasoning for Inert Systems in Building Energy Management
    Mirjam Minor and Lutz Marx
  • Semantic Trace Comparison at Multiple Levels of Abstraction
    Stefania Montani, Manuel Striani, Silvana Quaglini, Anna Cavallini and Giorgio Leonardi
  • On the Pros and Cons of Explanation-Based Ranking
    Khalil Muhammad, Aonghus Lawlor and Barry Smyth
  • A User Controlled System for the Generation of Melodies Applying Case-Based Reasoning
    María Navarro-Cáceres, Sara Rodríguez, Diego Milla, Belén Pérez Lancho and Juan Manuel Corchado
  • Towards a Case-Based Reasoning Approach to Dynamic Adaptation for Large-Scale Distributed Systems
    Sorana Tania Nemes and Andreea Buga
  • Evolutionary Inspired Adaptation of Exercise Plans for Increasing Solution Variety
    Tale Prestmo, Kerstin Bach, Agnar Aamodt and Paul Jarle Mork
  • Intelligent Control System for Back Pain Therapy
    Juan Recio-Garcia, Belen Diaz-Agudo, Jose Luis Jorro Aragoneses and Alireza Kazemi
  • Dependency Modeling for Knowledge Maintenance in Distributed CBR Systems
    Pascal Reuss, Christian Witzke and Klaus-Dieter Althoff
  • Case-Based Recommendation for Online Judges using Learning Itineraries
    Antonio A. Sánchez-Ruiz, Guillermo Jimenez-Diaz, Pedro P. Gómez-Martín and Marco A. Gómez-Martín
  • kNN Sampling for Personalised Human Activity Recognition
    Sadiq Sani, Nirmalie Wiratunga, Stewart Massie and Kay Cooper
  • Case-Based Interpretation of Best Practices: Application to Data Collection for Cancer Registries
    Michael Schnell, Sophie Couffignal, Jean Lieber, Stephanie Saleh, and Nicolas Jay
  • Running with Cases: A CBR Approach to Running Your Best Marathon
    Barry Smyth and Padraig Cunningham
  • Weighted One Mode Projection of a Bipartite Graph as a Local Similarity Measure
    Rotem Stram, Pascal Reuss and Klaus-Dieter Althoff
  • SCOUT: A Case-Based Reasoning Agent for Playing Race for The Galaxy
    Ian Watson and Michael Woolford
  • Conversational Process-Oriented Case-Based Reasoning
    Christian Zeyen, Gilbert Müller and Ralph Bergmann
  • Maintenance for Case Streams: A Streaming Approach to Competence-Based Deletion
    Yang Zhang, Su Zhang and David Leake

Social Program


We are planning a social program in and around Trondheim on Sunday. It will be possible to attend both, the social gathering and DC opening on Sunday. Participation is at each other's own cost.

During the Conference

Monday will close with a reception, on Tuesday we're planning the conference dinner at EC Dahls and on Wednesday will go on a hike followed by dinner in the woods around town. Participation and dinners are covered in the conference fee.

After the Conference

On Thursday, we are planning an AI Industry Day which is open for all ICCBR participants. Lunch will be served. We will organize an informal gathering after the AI day.