Workshop on Case-Based Agents

In the ever-evolving landscape of Artificial Intelligence (AI), the synergy between intelligent agents and case-based reasoning (CBR) stands as a promising interaction, holding the potential to advance both areas. Intelligent agents, designed to cope with limited computational resources, must perceive their environment and make decisions to achieve goals, while CBR techniques are rooted in harnessing sometimes incomplete knowledge of past experiences to reason effectively and efficiently in complex situations. Both disciplines have progressed in recent years, but there have been few dedicated meetings on their intersection. This workshop proposal aims to facilitate the cross-pollination of ideas, methodologies, and innovations among these two disciplines. By merging intelligent agents, which typically leverage abstract knowledge representations, with the experience-focused design of the case-based reasoning methodology, this collaborative endeavor seeks to advance the frontiers of AI, fostering more in-depth insights and novel applications.

We welcome long (10-16 pages, including references) and short (5-9 pages, including references) submissions. We specifically encourage short submissions (5-9 pages) on demonstrations. Papers may be submitted simultaneously to the main track and this workshop, however, they can either be published in the main track or in the workshop. Our Organizing Committee will select a subset of the submitted papers for oral presentation and, possibly, poster presentation. Accepted papers will be included in the CEUR-WS proceedings, indexed by SCOPUS and Google Scholar, ensuring wide dissemination of your valuable work. For a paper to appear in the proceedings, at least one of the authors must register for the AI track by the camera-ready copy deadline. Papers may be presented in-person at the conference, or in a pre-recorded video with the author attending virtually on live chat.

• Integration of CBR with generative AI (GenAI) agents

  • CBR as a plugin for a GenAI (e.g., LLM) agent

  • Using LLMs to learn models for case-based agents

  • Using CBR to facilitate GenAI agent development

• CBR and agent architectures

  • Models of agency

  • Integrated models of memory

  • Reactive and deliberative approaches

  • Agent development methodologies and frameworks

  • Evaluation of agent architectures

• CBR and agent reasoning

  • Integrated planning and acting

  • Goal reasoning for agents with changing goals

  • Plan recognition and theory of mind

  • Metacognition

  • State estimation and conceptual inference

  • Explanations and justifications

  • Recovery from failure

• CBR and agent communication and coordination

  • CBR for dynamic multi-agent environments

  • Case-based teamwork and coordination

  • Case-based negotiation and collective decision-making

• CBR and learning agents

  • Self-improving CBR agents

  •  Case-based learning by observation/demonstration

  • CBR and reinforcement learning

  • CBR and multi-strategy learning

• Applications: CBR and ...

  • Robotics

  • Virtual agents (e.g., Game AI, simulation and training environments, interactive storytelling)

  • Assistive agents (e.g., recommender agents, chatbots)

  • Intelligent decision aids

  • Tutoring agents

April 1 -- Deadline for workshop paper submission

April 17 -- Notification deadline of accepted workshop papers

May 1 -- Deadline for camera ready copy

July 1 -- Workshop

Paper submissions should be formatted according to the CEUR-WS format with a maximum of 16 pages including references. Shorter submissions (at least 5 pages and a maximum of 9 pages) are also welcome, such as to describe more preliminary work. Authors will be required to submit their papers through the workshop submission website. Papers will be reviewed by qualified reviewers drawn from our Workshop Program Committee.

All submissions must adhere to the formatting instructions of CEUR-WS.

Submissions must be submitted electronically through the EasyChair submission system under the dedicated track "Case-based agents Workshop" available at: https://easychair.org/conferences/?conf=iccbr2024

David W. Aha, Naval Research Laboratory, david.w.aha.civ@us.navy.mil

Andreas Korger, University of Würzburg, a.korger@informatik.uni-wuerzburg.de

Lukas Malburg, University of Trier & German Research Center for Artificial Intelligence (DFKI), malburgl@uni-trier.de

David H. Ménager (POC ), Parallax Advanced Research, david.menager@parallaxresearch.org