Method for evaluating plan recovery strategies in dynamic multi-agent environments

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
L. Moreira, C. G. Ralha
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引用次数: 1

Abstract

ABSTRACT Plan execution in dynamic environments can be affected by unexpected events leading to failures. Research on multi-agent planning area presents recovery strategies with replanning and repairing with evaluation based simply on average values. Thus, in this work, we propose a statistical method to evaluate plan recovery strategies in dynamic environments using a domain-independent approach. To validate the proposed method, we conducted simulated experiments with varying the number of agents, goals, actions, failure probability, and agents’ coupling levels. The evaluation metrics include plan length and planning time. The results highlight with at least 94% certainty that repairing planning time is lower than replanning, and replanning builds plans with fewer actions than repairing. Considering plan recovery strategies in dynamic multi-agent environments, we demonstrate that repairing presents better results as it is faster, but replanning builds better plans as the final plan length is strongly correlated to failure occurrence.
动态多智能体环境下的计划恢复策略评估方法
动态环境中的计划执行可能会受到导致失败的意外事件的影响。对多智能体规划区域的研究,提出了基于简单均值评价的重新规划和修复恢复策略。因此,在这项工作中,我们提出了一种统计方法,使用领域独立的方法来评估动态环境中的计划恢复策略。为了验证提出的方法,我们进行了模拟实验,改变了智能体的数量、目标、动作、失败概率和智能体的耦合水平。评估指标包括计划长度和计划时间。结果显示,至少有94%的确定性,修复计划时间低于重新计划,并且重新计划构建的计划比修复的行动更少。考虑到动态多智能体环境下的计划恢复策略,我们证明修复可以更快地获得更好的结果,但重新规划可以构建更好的计划,因为最终计划长度与故障发生密切相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.10
自引率
4.50%
发文量
89
审稿时长
>12 weeks
期刊介绍: Journal of Experimental & Theoretical Artificial Intelligence (JETAI) is a world leading journal dedicated to publishing high quality, rigorously reviewed, original papers in artificial intelligence (AI) research. The journal features work in all subfields of AI research and accepts both theoretical and applied research. Topics covered include, but are not limited to, the following: • cognitive science • games • learning • knowledge representation • memory and neural system modelling • perception • problem-solving
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