{"title":"蚂蚁利用简单的局部规则寻找最短路径。","authors":"Chris R Reid","doi":"10.3758/s13420-023-00580-6","DOIUrl":null,"url":null,"abstract":"<p><p>Garg et al. (2023, Proceedings of the National Academy of Sciences, 120[6], e2207959120) build simulation models to understand how turtle ants collectively find efficient paths through branched networks, highlighting the importance of bidirectional traffic, leakage of ants at junctions, and the ability to increase flow as key components for efficiency. Their findings provide new, biologically realistic mechanisms that could improve applications in our own engineered networks.</p>","PeriodicalId":49914,"journal":{"name":"Learning & Behavior","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ants find shortest paths using simple, local rules.\",\"authors\":\"Chris R Reid\",\"doi\":\"10.3758/s13420-023-00580-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Garg et al. (2023, Proceedings of the National Academy of Sciences, 120[6], e2207959120) build simulation models to understand how turtle ants collectively find efficient paths through branched networks, highlighting the importance of bidirectional traffic, leakage of ants at junctions, and the ability to increase flow as key components for efficiency. Their findings provide new, biologically realistic mechanisms that could improve applications in our own engineered networks.</p>\",\"PeriodicalId\":49914,\"journal\":{\"name\":\"Learning & Behavior\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Learning & Behavior\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.3758/s13420-023-00580-6\",\"RegionNum\":4,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/3/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"BEHAVIORAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Learning & Behavior","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3758/s13420-023-00580-6","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/3/17 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
Ants find shortest paths using simple, local rules.
Garg et al. (2023, Proceedings of the National Academy of Sciences, 120[6], e2207959120) build simulation models to understand how turtle ants collectively find efficient paths through branched networks, highlighting the importance of bidirectional traffic, leakage of ants at junctions, and the ability to increase flow as key components for efficiency. Their findings provide new, biologically realistic mechanisms that could improve applications in our own engineered networks.
期刊介绍:
Learning & Behavior publishes experimental and theoretical contributions and critical reviews concerning fundamental processes of learning and behavior in nonhuman and human animals. Topics covered include sensation, perception, conditioning, learning, attention, memory, motivation, emotion, development, social behavior, and comparative investigations.