{"title":"Quantifying the relationship between software design principles and performance in Jason: a case study with simulated mobile robots","authors":"Patrick Gavigan, Babak Esfandiari","doi":"10.1007/s10472-023-09844-3","DOIUrl":null,"url":null,"abstract":"<div><p>We investigated the relationship between various design approaches of AgentSpeak code for Jason Beliefs-Desires-Intentions (BDI) agents and their performance in a simulated automotive collision avoidance scenario. Also explored was how the approaches affected software maintainability, assessed through coupling, cohesion, and cyclomatic complexity. We then compared each agent’s performance, specifically their reasoning cycle duration and their responsiveness. Our findings revealed that agents with looser coupling and higher cohesion are more responsive to stimuli, implying that more maintainable AgentSpeak code can result in better performing agents. Performance was inversely related to cyclomatic complexity.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"92 4","pages":"775 - 795"},"PeriodicalIF":1.2000,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Mathematics and Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10472-023-09844-3","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
We investigated the relationship between various design approaches of AgentSpeak code for Jason Beliefs-Desires-Intentions (BDI) agents and their performance in a simulated automotive collision avoidance scenario. Also explored was how the approaches affected software maintainability, assessed through coupling, cohesion, and cyclomatic complexity. We then compared each agent’s performance, specifically their reasoning cycle duration and their responsiveness. Our findings revealed that agents with looser coupling and higher cohesion are more responsive to stimuli, implying that more maintainable AgentSpeak code can result in better performing agents. Performance was inversely related to cyclomatic complexity.
期刊介绍：
Annals of Mathematics and Artificial Intelligence presents a range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to diverse areas of Artificial Intelligence, from decision support, automated deduction, and reasoning, to knowledge-based systems, machine learning, computer vision, robotics and planning.
The journal features collections of papers appearing either in volumes (400 pages) or in separate issues (100-300 pages), which focus on one topic and have one or more guest editors.
Annals of Mathematics and Artificial Intelligence hopes to influence the spawning of new areas of applied mathematics and strengthen the scientific underpinnings of Artificial Intelligence.
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