{"title":"Comparing rankings of heterogeneous agents","authors":"N. Kuhn","doi":"10.1145/168555.168556","DOIUrl":null,"url":null,"abstract":"A central problem in the study of autonomous cooperating systems is that of how to establish mechanisms for controlling the interactions between different parts (which are called agents) of the system. For heterogeneous agents this aims at, modeling the basics for their decisions. The mechanisms we discuss in this paper are based on the assumption that, the agents can estimate the effects of being attached to a certain set of goals. In the simplest case this is expressed by a single value e.g., the cost that will arise for the accomplishment of these goals. But in general this estimation may be arbitrarily complicated. In addition, we assume that, the agents have a function available to mnk their goals according to the estimated values and they pursue the goals they rank best. Then, these values can be used to resolve various kinds of conflicts in this kind of systems. For example in the task allocation phase the case of multiple applications for the allocation of a goal can be decided by allocating the goal to the agent with the ‘(best estimation”. Another way of using these values is for establishing collaborative actions between a pair (or a set) of agents: If one agent wants to get support in the accomplishment, of a particular goal he will try to persuade another agent to modify his ranking of the goal in such a way that, they both will rank best this “common” goal. Thus, the use of ranking functions provides a general framework for considering cooperative aspects within the study of multi-agent systems. An essential question in this context is how the rankings of different agents can be compared. Therefore, we develop in this paper a formalization of the concept, of ranking functions and discuss mechanisms that establish the comparability of different, rankings. *This work was supported by the German Ministry of Research and Technology under grant ITW9104 COOCS’93 -1 I1931CA, USA o 1993 ACM O-89791 -627 -1 19310010... $1 .50","PeriodicalId":338751,"journal":{"name":"Conference on Organizational Computing Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference on Organizational Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/168555.168556","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
A central problem in the study of autonomous cooperating systems is that of how to establish mechanisms for controlling the interactions between different parts (which are called agents) of the system. For heterogeneous agents this aims at, modeling the basics for their decisions. The mechanisms we discuss in this paper are based on the assumption that, the agents can estimate the effects of being attached to a certain set of goals. In the simplest case this is expressed by a single value e.g., the cost that will arise for the accomplishment of these goals. But in general this estimation may be arbitrarily complicated. In addition, we assume that, the agents have a function available to mnk their goals according to the estimated values and they pursue the goals they rank best. Then, these values can be used to resolve various kinds of conflicts in this kind of systems. For example in the task allocation phase the case of multiple applications for the allocation of a goal can be decided by allocating the goal to the agent with the ‘(best estimation”. Another way of using these values is for establishing collaborative actions between a pair (or a set) of agents: If one agent wants to get support in the accomplishment, of a particular goal he will try to persuade another agent to modify his ranking of the goal in such a way that, they both will rank best this “common” goal. Thus, the use of ranking functions provides a general framework for considering cooperative aspects within the study of multi-agent systems. An essential question in this context is how the rankings of different agents can be compared. Therefore, we develop in this paper a formalization of the concept, of ranking functions and discuss mechanisms that establish the comparability of different, rankings. *This work was supported by the German Ministry of Research and Technology under grant ITW9104 COOCS’93 -1 I1931CA, USA o 1993 ACM O-89791 -627 -1 19310010... $1 .50
自主协作系统研究的一个核心问题是如何建立机制来控制系统中不同部分(称为agent)之间的相互作用。对于异构代理,其目标是为其决策建模基础。我们在本文中讨论的机制是基于这样的假设,即代理可以估计依附于某一组目标的影响。在最简单的情况下,这可以用一个值来表示,例如,为实现这些目标而产生的成本。但一般来说,这种估计可能是任意复杂的。此外,我们假设agent有一个函数可以根据估计值来标记它们的目标,并且它们追求它们排名最好的目标。然后,这些值可以用来解决这类系统中的各种冲突。例如,在任务分配阶段,可以通过将目标分配给具有“最佳估计”的代理来决定多个应用程序分配目标的情况。使用这些值的另一种方法是在一对(或一组)智能体之间建立协作行为:如果一个智能体想要在完成特定目标时获得支持,他将试图说服另一个智能体修改他对目标的排名,这样他们都将在这个“共同”目标中排名最高。因此,排序函数的使用为考虑多智能体系统研究中的合作方面提供了一个通用框架。在这种情况下,一个重要的问题是如何比较不同代理的排名。因此,我们在本文中发展了排名函数概念的形式化,并讨论了建立不同排名的可比性的机制。*本工作由德国研究技术部资助,ITW9104 COOCS ' 93 -1 I1931CA, USA o 1993 ACM o -89791 -627 -1 19310010…1美元50