{"title":"Imprecise evidence in social learning","authors":"Zixuan Liu, Michael Crosscombe, Jonathan Lawry","doi":"10.1007/s11721-024-00238-7","DOIUrl":null,"url":null,"abstract":"<p>Social learning is a collective approach to decentralised decision-making and is comprised of two processes; evidence updating and belief fusion. In this paper we propose a social learning model in which agents’ beliefs are represented by a set of possible states, and where the evidence collected can vary in its level of imprecision. We investigate this model using multi-agent and multi-robot simulations and demonstrate that it is robust to imprecise evidence. Our results also show that certain kinds of imprecise evidence can enhance the efficacy of the learning process in the presence of sensor errors.</p>","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"3 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm Intelligence","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11721-024-00238-7","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: 0
Abstract
Social learning is a collective approach to decentralised decision-making and is comprised of two processes; evidence updating and belief fusion. In this paper we propose a social learning model in which agents’ beliefs are represented by a set of possible states, and where the evidence collected can vary in its level of imprecision. We investigate this model using multi-agent and multi-robot simulations and demonstrate that it is robust to imprecise evidence. Our results also show that certain kinds of imprecise evidence can enhance the efficacy of the learning process in the presence of sensor errors.
期刊介绍：
Swarm Intelligence is the principal peer-reviewed publication dedicated to reporting on research
and developments in the multidisciplinary field of swarm intelligence. The journal publishes
original research articles and occasional review articles on theoretical, experimental and/or
practical aspects of swarm intelligence. All articles are published both in print and in electronic
form. There are no page charges for publication. Swarm Intelligence is published quarterly.
The field of swarm intelligence deals with systems composed of many individuals that coordinate
using decentralized control and self-organization. In particular, it focuses on the collective
behaviors that result from the local interactions of the individuals with each other and with their
environment. It is a fast-growing field that encompasses the efforts of researchers in multiple
disciplines, ranging from ethology and social science to operations research and computer
engineering.
Swarm Intelligence will report on advances in the understanding and utilization of swarm
intelligence systems, that is, systems that are based on the principles of swarm intelligence. The
following subjects are of particular interest to the journal:
• modeling and analysis of collective biological systems such as social insect colonies, flocking
vertebrates, and human crowds as well as any other swarm intelligence systems;
• application of biological swarm intelligence models to real-world problems such as distributed
computing, data clustering, graph partitioning, optimization and decision making;
• theoretical and empirical research in ant colony optimization, particle swarm optimization,
swarm robotics, and other swarm intelligence algorithms.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
