A Framework for learning multi-agent dynamic formation strategy in real-time applications

TL;DR

This paper introduces a flexible framework for multi-agent formation strategy learning that adapts dynamically to changing contexts, reduces feature complexity, and learns from expert or observed behaviors in restricted environments.

Contribution

It proposes a novel modular learning framework that enables dynamic formation assignment and strategy adaptation without extensive expert intervention.

Findings

Framework effectively learns formation strategies from limited features

System dynamically adapts formations to changing contexts

Automatically generates complex formation algorithms

Abstract

Formation strategy is one of the most important parts of many multi-agent systems with many applications in real world problems. In this paper, a framework for learning this task in a limited domain (restricted environment) is proposed. In this framework, agents learn either directly by observing an expert behavior or indirectly by observing other agents or objects behavior. First, a group of algorithms for learning formation strategy based on limited features will be presented. Due to distributed and complex nature of many multi-agent systems, it is impossible to include all features directly in the learning process; thus, a modular scheme is proposed in order to reduce the number of features. In this method, some important features have indirect influence in learning instead of directly involving them as input features. This framework has the ability to dynamically assign a group of positions to a group of agents to improve system performance. In addition, it can change the formation strategy when the context changes. Finally, this framework is able to automatically produce many complex and flexible formation strategy algorithms without directly involving an expert to present and implement such complex algorithms.