{"title":"Method for evaluating plan recovery strategies in dynamic multi-agent environments","authors":"L. Moreira, C. G. Ralha","doi":"10.1080/0952813X.2022.2078887","DOIUrl":null,"url":null,"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.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"92 1","pages":"1225 - 1249"},"PeriodicalIF":1.7000,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Experimental & Theoretical Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1080/0952813X.2022.2078887","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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