Accepted papers

The ICCBR 2022 Proceedings have just been published as part of the Lecture Notes in Computer Science series (LNAI 13405) , now available online and can be accessed at:

List of Accepted Papers

  • Fengrui Liu, Yang Wang, Zhenyu Li, Rui Ren, Hongtao Guan, Xian Yu, Xiaofan Chen and Gaogang Xie. (2022).  MicroCBR: Case-based Reasoning on Spatio-temporal Fault Knowledge Graph for Microservices Troubleshooting. 
  • Thomas Gabel and Fabian Sommer.  (2022). Case-Based Learning and Reasoning Using Layered Boundary Multigraphs.
  • Barry Smyth and Mark T. Keane (2022). A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations.
  • William Blanzeisky, Barry Smyth and Padraig Cunningham (2022).  Algorithmic Bias and Fairness in Case-Based Reasoning.
  • Richard Noll, Jannik Schaaf and Holger Storf (2022).  The Use of Computer-Assisted Case-Based Reasoning to Support Clinical Decision-Making: A Scoping Review.
  • Mathieu d’Aquin, Emmanuel Nauer and Jean Lieber (2022). A Factorial Study of Neural Network Learning from Differences for Regression.
  • Eoin DelaneyDerek Greene, Laurence Shalloo, Michael Lynch and Mark T. Keane (2022).  Forecasting for Sustainable Dairy Produce: Enhanced Long-Term, Milk-Supply Forecasting Using k-NN for Data Augmentation, with Prefactual Explanations for XAI
  • Maximilian Hoffmann, Lukas Malburg, Nico Bach and Ralph Bergmann (2022). GPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning.
  • Katharina Landeis, Gerhard Pews and Mirjam Minor. (2022). Particle Swarm Optimization in Small Case Bases for Software Effort Estimation. 
  • Jonas Nüßlein, Steffen Illium, Robert Müller, Thomas Gabor and Claudia Linnhoff-Popien. (2022). Case-Based Inverse Reinforcement Learning Using Temporal Coherence.
  • Ciara Feely, Brian Caulfield, Aonghus Lawlor and Barry Smyth. (2022). An Extended Case-Based Reasoning Approach to Race-Time Prediction in Recreational Marathon Runners.
  • Justine Duck, Romain Schaller, Frédéric Auber, Yann Chaussy, Julien Henriet, Jean Lieber, Emmanuel Nauer and Henri Prade (2022). Analogy-based post-treatment of CNN Image Segmentations.
  • Greta Warren, Barry Smyth and Mark T. Keane. (2022).  “Better” Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfactuals Using Categorical Features for Explainable AI (XAI).
  • Fadi Badra, Marie-Jeanne Lesot, Aman Barakat and Christophe Marsala. (2022).  Theoretical and Experimental Study of a Complexity Measure for Analogical Transfer.  
  • Anjana Wijekoon, Nirmalie Wiratunga, Ikechukwu Nkisi-Orji, Chamath Palihawadana, David Corsar and Kyle Martin (2022).  How Close is Too Close? Role of Feature Attributions in Discovering Counterfactual Explanations.
  • Jesus Darias, Juan A. Recio-Garcia, Belen Diaz-Agudo and Marta Caro-Martinez. (2022).  Reusing Experiences of Model Agnostic Explanations.
  • Adwait Parsodkar, Deepak P and Sutanu Chakraborti (2022). Never Judge a Case by Its (Unreliable) Neighbors: Estimating Case Reliability for CBR.
  • David Leake, Zachary Wilkerson and David Crandall (2022).  Extracting Case Indices from Convolutional Neural Networks: A Comparative Study.
  • Fakhri Abbas, Nadia Najjar and David Wilson (2022).  Exploring the Effect of Recipe Representation on Critique-based Conversational Recommendation
  • Xiaomeng Ye, David Leake and David Crandall (2022).  Case Adaptation with Neural Networks: Capabilities and Limitations.
  • Ikechukwu Nkisi-Orji, Chamath Palihawadana, Nirmalie Wiratunga, David Corsar and Anjana Wijekoon (2022).   Adapting Semantic Similarity Methods for Case-Based Reasoning in the Cloud.

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