Real-time retrieval for case-based reasoning in interactive multiagent-based simulations

TL;DR

This paper develops a real-time case-based reasoning framework for multiagent simulations, focusing on efficient retrieval mechanisms to handle dynamic, interactive environments like football games.

Contribution

It introduces a novel model for case and case base structure with a dedicated recall algorithm optimized for real-time retrieval in multiagent simulations.

Findings

Improved retrieval performance under time constraints

Effective handling of dynamic situations in simulations

Framework applicable to various interactive multiagent scenarios

Abstract

The aim of this paper is to present the principles and results about case-based reasoning adapted to real- time interactive simulations, more precisely concerning retrieval mechanisms. The article begins by introducing the constraints involved in interactive multiagent-based simulations. The second section pre- sents a framework stemming from case-based reasoning by autonomous agents. Each agent uses a case base of local situations and, from this base, it can choose an action in order to interact with other auton- omous agents or users' avatars. We illustrate this framework with an example dedicated to the study of dynamic situations in football. We then go on to address the difficulties of conducting such simulations in real-time and propose a model for case and for case base. Using generic agents and adequate case base structure associated with a dedicated recall algorithm, we improve retrieval performance under time pressure compared to classic CBR techniques. We present some results relating to the performance of this solution. The article concludes by outlining future development of our project.