大规模多机器人团队中的复杂网络效应通信决策

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"《利用复杂网络效应优化大型多机器人团队通信决策》在当前的科研背景下，随着机器人技术的快速发展和广泛应用，大规模多机器人团队在动态且部分可观察的环境中进行协作时，有效的信息共享变得至关重要。机器人之间的有效通信不仅涉及减少冗余信息的传播，还需合理分配有限的通信带宽，确保只有对目标机器人群体有益的信息被准确地广播。这通常被视为一个多智能体决策理论问题，然而，当团队规模急剧扩大时，寻找最优解决方案的复杂性提升到了非确定性多项式完全类（NEXP-COMLETE）级别，这意味着传统的解决方法在面对庞大的团队时难以提供高效解决方案。为了克服这一挑战，本文提出了一个新的创新思路。首先，研究者们开发了基于信息传播上下文的启发式方法来解决决策理论问题。这种方法考虑了即将广播的信息内容，旨在提高决策的效率和针对性。然而，为了进一步适应大规模团队的复杂网络特性，作者引入了一种新颖的“上下文自由”决策模型。这个模型的核心在于它不再受限于单一的信息传播背景，而是充分考虑了机器人网络的复杂结构，如连接度、节点重要性、信息扩散路径等因素。通过整合网络分析与多智能体决策，这种模型能够快速处理大规模团队中的通信决策，使得机器人能够在保持通信效率的同时，根据网络动态调整其通信策略，确保信息的精准传递。具体来说，该模型可能包括以下步骤： 1. 网络分析：识别团队中每个机器人的重要性和它们之间的连接强度，以便理解哪些信息可能会通过哪种方式快速传播。 2. 信息评估：根据信息的价值和接收者的利益，确定是否以及何时向特定机器人发送特定信息。 3. 动态决策：通过模拟或学习算法，实时调整通信策略，以适应网络变化和任务需求。 4. 效果优化：持续监控和评估通信决策的效果，通过迭代优化来改进通信效率和团队整体性能。这篇研究论文为应对大型多机器人团队的通信决策问题提供了创新的策略，它突破了传统方法的局限，利用复杂网络理论来指导通信行为，从而在保障任务执行效率的同时，降低了决策问题的计算复杂性。这对于未来机器人协作和分布式系统的设计具有重要意义，有望推动相关领域的理论发展和技术进步。"

Using Complex Network Effects for Communication

Decisions in Large Multi-robot Teams

Yang Xu, Xuemei Hu, Yan Li, Dong Li, and Mengjun Yang

School of Computer Science and Engineering

University of Electronic Science and Technology of China

Chengdu, Sichuan, P.R.China

xuyang@uestc.edu.cn

ABSTRACT

Sharing information is critical to multi-robot team coordi-

nation when rob ots are widely deployed in a dynamic and

partially observable environment. To be eﬃcient, robots

should balance well between broadcasting information and

reserving limited bandwidth so that only the right informa-

tion should be broadcast to the interested receivers. Robots’

communication decision is normally modeled as a multi-

agent decision theoretical problem. However, when the team

expands to very large, the solution is classiﬁed as NEXP-

COMPLETE. In this paper, in addition to building heuristic

approaches to solve the decision theoretical problem based

on the information context to be broadcast, we put forward

a novel context-free decision model that allows fast commu-

nication decision by considering complex network attributes

in large teams. Similar to human society, information should

b e broadcast if the action can make a go od information cov-

erage in the team. We analyze how complex network at-

tributes can improve communication in a broadcast network.

By putting forward a heuristic model to estimate those com-

plex network attributes from robots’ local view, we can build

decision models either from robots’ experiences or from their

lo cal incoming communications. Finally, we incorporate our

algorithm in well-known information sharing algorithms and

the results manifest the feasibility of our design.

Categories and Subject Descriptors

I.2.11 [Artiﬁcial Intelligence]: Distributed Artiﬁcial In-

telligence -Intelligent Agents, Multiagent Systems.

Keywords

Complex network; Information Sharing; Coordination;

1. INTRODUCTION

Coordinating large groups of robots or unmanned vehi-

cles in a dynamic and partially observable environment is

comp elling in various domains such as urban search and res-

cue [1], planet exploration [2] and military operations [3]. In

such applications, information sharing is necessary because

rob ots have to share their observations and intentions so

Appears in: Alessio Lomuscio, Paul Scerri, Ana Bazzan,

and Michael Huhns (eds.), Proceedings of the 13th Inter-

national Conference on Autonomous Agents and Multiagent

Systems (AAMAS 2014), May 5-9, 2014, Paris, France.

 2014, International Foundation for Autonomous Agents and

that their mutual beliefs as well as joint activities towards

the best team performance can be reached [4]. In a mobile

rob ot team, robots use wireless connections to broadcast

information to their neighbors. But when the team scales

up, communication bandwidth becomes a serve bottleneck.

Rob ots then are required to balance well between broadcast-

ing valuable information and reserving limited bandwidth.

Their decision is hard because robots in this scenario are

normally highly distributed and have only a partial view of

the team. Without a complete knowledge on what the others

know, robots may not be able to make rational decisions [5].

The information sharing problem on how to decide to

broadcast valuable information in large robot teams has

b een intensively studied in recent years. In the computer

network research community, researchers have focused on

how to avoid redundant information coverage overlay no

matter what the information is. Typically, Scalable Broad-

cast Algorithm (SBA) [6] shares information and avoids robots

rebroadcasting if most of their neighbors already have it.

Low-Energy Adaptive Clustering Hierarchy (LEACH) [7]

uses static hierarchical protocol and its improved proto-

col [13] combines clustering with predeﬁned forming chains

to proactively share information. Sensor Protocols for In-

formation via Negotiation (SPIN) [8] is a reactive protocol

which avoids redundant data transmission by meta-data ne-

gotiation between neighbors. Robots only need to forward

data to the neighbors who need it. But if no request is

received, robots will prefer to not sending the information.

Information sharing research in AI community starts from

the view of how the information to be shared can help the

rob ot team improve its performance [5]. Therefore, infor-

mation sharing is modeled as a decision theoretical problem

that, for each speciﬁc piece of information, robots evalu-

ate the utilities if it were broadcast. When the utility of

broadcasting it is beyond the cost, the robot will broad-

cast. However, with the partial observable capability of

each robot, the information sharing utility calculation is

a typical DEC-POMDP problem with the expand of team

scale [9]. Extra eﬀorts have been done on building heuristic

algorithms to solve this intrinsic NEXP-COMPLETE prob-

lem in each speciﬁc coordination domain, such as myopic

decision model [10], and State-Connection-Reward matrix-

based model [11]. Although their p erformances are proven

to be goo d, they are usually hard to be optimized.

In this paper, addition to the information sharing algo-

rithm design in previous studies, we explore how the com-

plex network eﬀects can be used in the robot communication

decision, and put forward an interesting approach to help lo-

685

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