2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems最新文献_第3页

On the Influence of Inter-Agent Variation on Multi-Agent Algorithms Solving a Dynamic Task Allocation Problem under Uncertainty 求解不确定条件下动态任务分配问题的多agent间变化对算法的影响

2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2012-09-10 DOI: 10.1109/SASO.2012.16

Gerrit Anders, Christian Hinrichs, Florian Siefert, Pascal Behrmann, W. Reif, M. Sonnenschein

{"title":"On the Influence of Inter-Agent Variation on Multi-Agent Algorithms Solving a Dynamic Task Allocation Problem under Uncertainty","authors":"Gerrit Anders, Christian Hinrichs, Florian Siefert, Pascal Behrmann, W. Reif, M. Sonnenschein","doi":"10.1109/SASO.2012.16","DOIUrl":"https://doi.org/10.1109/SASO.2012.16","url":null,"abstract":"Multi-agent systems often consist of heterogeneous agents with different capabilities and objectives. While some agents might try to maximize their system's utility, others might be self-interested and thus only act for their own good. However, because of their limited capabilities and resources, it is often necessary that agents cooperate to be able to satisfy given tasks. To work together on such a task, the agents have to solve a task allocation problem, e.g., by teaming up in groups like coalitions or distributing the task among themselves on electronic markets. In this paper, we introduce two algorithms that allow agents to cooperatively solve a dynamic task allocation problem in uncertain environments. Based on these algorithms, we investigate the influence of inter-agent variation on the system's behavior. One of these algorithms explicitly exploits inter-agent variation to solve the task without communication between the agents, while the other builds upon a fixed overlay network in which agents exchange information. Throughout the paper, the frequency stabilization problem from the domain of decentralized power management serves as a running example to illustrate our algorithms and results.","PeriodicalId":126067,"journal":{"name":"2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130154296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

Collective Attention through Public Displays 通过公共展示的集体关注

2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2012-09-10 DOI: 10.1109/SASO.2012.35

A. Ferscha, K. Zia, Benedikt Gollan

引用次数: 12

Modelling Situated Action Based on Affordances and Stigmergy 基于可视性和污名性的情境行为建模

2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2012-09-10 DOI: 10.1109/SASO.2012.29

Zoubida Afoutni, F. Guerrin, R. Courdier

引用次数: 3

Simulating Human Single Motor Units Using Self-Organizing Agents 用自组织代理模拟人体单个运动单元

2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2012-09-10 DOI: 10.1109/SASO.2012.18

Ö. Gürcan, C. Bernon, K. Türker, J. Mano, P. Glize, Oğuz Dikenelli

引用次数: 11

Fast Precise Distributed Control for Energy Demand Management 面向能源需求管理的快速精确分布式控制

2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2012-09-10 DOI: 10.1109/SASO.2012.12

J. Beal, J. Berliner, Kevin Hunter

引用次数: 28

Gradient-Based Self-Organisation Patterns of Anticipative Adaptation 基于梯度的预期适应自组织模式

2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2012-09-10 DOI: 10.1109/SASO.2012.25

Sara Montagna, Danilo Pianini, Mirko Viroli

引用次数: 14

Optimised Reputation-Based Adaptive Punishment for Limited Observability 有限可观察性下基于声誉的自适应惩罚优化

2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2012-09-10 DOI: 10.1109/SASO.2012.24

Samhar Mahmoud, Daniel Villatoro, Jeroen Keppens, Michael Luck

引用次数: 9

Modeling Adaptative Social Behavior in Collective Problem Solving Algorithms 集体问题解决算法中的适应性社会行为建模

2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2012-09-10 DOI: 10.1109/SASO.2012.20

Diego Noble, L. Lamb, R. M. Araújo

{"title":"Modeling Adaptative Social Behavior in Collective Problem Solving Algorithms","authors":"Diego Noble, L. Lamb, R. M. Araújo","doi":"10.1109/SASO.2012.20","DOIUrl":"https://doi.org/10.1109/SASO.2012.20","url":null,"abstract":"Collective problem solving can lead to the development of new methods and algorithms that can potentially contribute to novel Artificial Intelligence applications and tools. Socially-inspired optimization algorithms are a class of algorithms that aim at conducting a search over a large solution space using mechanisms similar to how humans solve problems in a social context. Several such algorithms exist in the literature, including adaptations of classical ones, such as Genetic Algorithms. These models, however, do not take into account a fundamental concept in human social systems: the individual ability to adapt problem-solving strategies as a function of the social context. In this paper, we propose and investigate an extension inside a socially-inspired model of collective problem solving which allows one to model agents with such adaptability. This extension is based on the concept of humans as ``motivated tacticians'' and it dictates how agents are to adapt their search heuristics according to their respective social context. We show how this rule can speed up the system's convergence to good solutions and improve the search space exploration. The results contribute towards the design of socially inspired computational systems for collective problem-solving.","PeriodicalId":126067,"journal":{"name":"2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131215049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Maintaining Spatial Relationships in Uncertain Environments 在不确定环境中维持空间关系

2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2012-09-10 DOI: 10.1109/SASO.2012.39

Nadeem Jamali, Anil Keela

引用次数: 1

Collective Self-Tuning for Complex Product Design 复杂产品设计的集体自调谐

2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2012-09-10 DOI: 10.1109/SASO.2012.14

Elsy Kaddoum, J. Georgé

{"title":"Collective Self-Tuning for Complex Product Design","authors":"Elsy Kaddoum, J. Georgé","doi":"10.1109/SASO.2012.14","DOIUrl":"https://doi.org/10.1109/SASO.2012.14","url":null,"abstract":"A complex product is generally a system composed of numerous interdependent components, each one representing specific disciplines and developed using associated expertise. When analysing the problem from another point of view, we can see that for each design domain, a generally huge set of real already designed elements exists. Thus, when constructing a new element, it is interesting to use this already known and acquired knowledge. This knowledge does not only contain the discipline's information but also the engineers' experience. Considering this point of view, the design of complex products defines a new generic class of complex problems. In this paper, we address this class of problems using the Self-Adaptive Population Based Reasoning (SAPBR) generic approach. It is based on the Adaptive Multi-Agent System (AMAS) theory that takes advantage from cooperation to design robust and open multi-agent systems. In SAPBR, agents use cooperative self-tuning principles in order to estimate and discover new characteristic values for the design of new elements. The obtained system is compared to the Self-Organising Map (SOM) and the Multilayer Perceptron (MP) algorithms that address similar problems.","PeriodicalId":126067,"journal":{"name":"2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128891783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11