{"title":"基于 ADOPT 算法的终止和最优性的反例和修正","authors":"Koji Noshiro , Koji Hasebe","doi":"10.1016/j.artint.2024.104083","DOIUrl":null,"url":null,"abstract":"<div><p>A distributed constraint optimization problem (DCOP) is a framework to model multi-agent coordination problems. Asynchronous distributed optimization (ADOPT) is a well-known complete DCOP algorithm, and many of its variants have been proposed over the last decade. It is considered proven that ADOPT-based algorithms have the key properties of termination and optimality, which guarantee that the algorithms terminate in a finite time and obtain an optimal solution, respectively. In this paper, we present counterexamples to the termination and optimality of ADOPT-based algorithms. They are classified into three types, at least one of which exists in each of ADOPT and eight of its variants that we analyzed. In other words, the algorithms may potentially not terminate or terminate with a suboptimal solution. Furthermore, we show that the bounded-error approximation of ADOPT, which enables the algorithm to terminate faster with the quality of the solution guaranteed within a predefined error bound, also suffers from flaws. Additionally, we propose an amended version of ADOPT that avoids the flaws in existing algorithms and prove that it has the properties of termination and optimality.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"329 ","pages":"Article 104083"},"PeriodicalIF":5.1000,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Counterexamples and amendments to the termination and optimality of ADOPT-based algorithms\",\"authors\":\"Koji Noshiro , Koji Hasebe\",\"doi\":\"10.1016/j.artint.2024.104083\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A distributed constraint optimization problem (DCOP) is a framework to model multi-agent coordination problems. Asynchronous distributed optimization (ADOPT) is a well-known complete DCOP algorithm, and many of its variants have been proposed over the last decade. It is considered proven that ADOPT-based algorithms have the key properties of termination and optimality, which guarantee that the algorithms terminate in a finite time and obtain an optimal solution, respectively. In this paper, we present counterexamples to the termination and optimality of ADOPT-based algorithms. They are classified into three types, at least one of which exists in each of ADOPT and eight of its variants that we analyzed. In other words, the algorithms may potentially not terminate or terminate with a suboptimal solution. Furthermore, we show that the bounded-error approximation of ADOPT, which enables the algorithm to terminate faster with the quality of the solution guaranteed within a predefined error bound, also suffers from flaws. Additionally, we propose an amended version of ADOPT that avoids the flaws in existing algorithms and prove that it has the properties of termination and optimality.</p></div>\",\"PeriodicalId\":8434,\"journal\":{\"name\":\"Artificial Intelligence\",\"volume\":\"329 \",\"pages\":\"Article 104083\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2024-01-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0004370224000195\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0004370224000195","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Counterexamples and amendments to the termination and optimality of ADOPT-based algorithms
A distributed constraint optimization problem (DCOP) is a framework to model multi-agent coordination problems. Asynchronous distributed optimization (ADOPT) is a well-known complete DCOP algorithm, and many of its variants have been proposed over the last decade. It is considered proven that ADOPT-based algorithms have the key properties of termination and optimality, which guarantee that the algorithms terminate in a finite time and obtain an optimal solution, respectively. In this paper, we present counterexamples to the termination and optimality of ADOPT-based algorithms. They are classified into three types, at least one of which exists in each of ADOPT and eight of its variants that we analyzed. In other words, the algorithms may potentially not terminate or terminate with a suboptimal solution. Furthermore, we show that the bounded-error approximation of ADOPT, which enables the algorithm to terminate faster with the quality of the solution guaranteed within a predefined error bound, also suffers from flaws. Additionally, we propose an amended version of ADOPT that avoids the flaws in existing algorithms and prove that it has the properties of termination and optimality.
期刊介绍:
The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.