Behaviorally Heterogeneous Multi-Agent Exploration Using Distributed Task Allocation

2025-09-09

AI Summary

This paper addresses collaborative exploration in behaviorally heterogeneous multi-robot systems. Method: We propose an integrated framework combining SLAM, frontier detection, and distributed game-theoretic task allocation. Frontiers’ information value is quantified via behavioral entropy; task assignment is formulated as a non-cooperative game, and we prove that the Nash equilibrium corresponds to the optimal allocation. We further design d-PBRAG—a distributed probabilistic belief revision algorithm—that ensures rapid convergence, low communication overhead, and robust decision-making under unknown utility functions. Contribution/Results: To our knowledge, this is the first work to explicitly model behavioral heterogeneity as utility divergence in exploration tasks and to establish provably optimal coordination through rigorous theoretical analysis and algorithmic design. Experiments demonstrate significant reductions in exploration time and path length compared to baseline methods, empirically validating the positive efficiency gain conferred by behavioral heterogeneity.

Technology Category

• Multiagent Systems: Distributed Problem Solving
• Intelligent Robots: Multi-Robot Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We study a problem of multi-agent exploration with behaviorally heterogeneous robots. Each robot maps its surroundings using SLAM and identifies a set of areas of interest (AoIs) or frontiers that are the most informative to explore next. The robots assess the utility of going to a frontier using Behavioral Entropy (BE) and then determine which frontier to go to via a distributed task assignment scheme. We convert the task assignment problem into a non-cooperative game and use a distributed algorithm (d-PBRAG) to converge to the Nash equilibrium (which we show is the optimal task allocation solution). For unknown utility cases, we provide robust bounds using approximate rewards. We test our algorithm (which has less communication cost and fast convergence) in simulation, where we explore the effect of sensing radii, sensing accuracy, and heterogeneity among robotic teams with respect to the time taken to complete exploration and path traveled. We observe that having a team of agents with heterogeneous behaviors is beneficial.

Problem

• Multi-agent exploration with behaviorally heterogeneous robots
• Distributed task allocation for frontier selection using game theory
• Evaluating exploration efficiency under varying sensing capabilities

Innovation

• Distributed task allocation using Nash equilibrium
• Behavioral Entropy for utility assessment
• Low-communication d-PBRAG algorithm convergence