Call for Papers

The goal of this workshop is to provide a forum to identify opportunities and challenges for the use of deep learning techniques and architectures in the context of case-based reasoning systems. Particular interests this workshop will explore include:

- How DL can be used to improve knowledge aggregation strategies for case representation

- The role of DL in making similarity computations easier and more efficient

- Application of DL to help with solution adaptation

- How DL architectures can be used to inspire more efficient indexing and retrieval architectures

Accordingly, we expect to draw interest from researchers from a number of related areas including Case-based Reasoning, Deep Learning and Machine Learning. We expect that this diversity would allow us to address the challenges in the field and identify where our efforts, as a research community, should focus.

Topics of Interest

• All Deep Learning approaches applied to CBR, including but not limited to:
  • Knowledge representation;
  • Similarity and retrieval;
  • Adaptation;
  • case-base maintenance;
  • Dynamic systems
  • Cognitive modelling
  • Conversational CBR
  • Textual CBR
  • Temporal reasoning
  • Mixed initiative CBR
  • Systems and application
• CBR approaches applied to Deep Learning
• Novel interdisciplinary ideas

Submissions

We welcome the following submissions:

– Full (application/research) papers up to 10 pages;

– Short papers up to 5 pages;

– Demo supported by a brief abstract.