Case-Based Reasoning and Deep Learning Workshop: CBRDL 2018

Introduction

Recent advances in deep learning (DL) have helped to usher in a new wave of confidence in the capability of artificial intelligence. Increasingly, we are seeing DL architectures outperform long established state-of-the-art algorithms in a number of diverse tasks. In fact, DL has reached a point where it currently rivals or has surpassed human performance in a number of challenges e.g. image classification, speech recognition and board games.

The successes of DL call for novel methods and techniques that exploit DL for the benefit of Case-based reasoning (CBR). The potentials of DL for CBR include improvement in knowledge aggregation and feature extraction for case representation, efficient indexing and retrieval architectures as well as assisting with case adaptation.


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.

Workshop Topics

  • 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

Workshop Format

The workshop will include the following activities.

– Presentation of peer-reviewed (application/research) papers;

– Presentation of short position papers;

– Demonstration of applications and

– Invited talks.