DAML-Based Policy Enforcement for Semantic Data Transformation and Filtering in Multi-agent Systems

机译：基于DAML的多主体系统中语义数据转换和过滤的策略执行

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This paper describes an approach to runtime policy-based control over information exchange that allows a far more fine-grained control of these dynamically discovered agent interactions. The DARPA Agent Markup Language (DAML) is used to represent policies that may either filter messages based on their semantic content or transform the messages to make them suitable to be released. Policy definition, management, and enforcement are realized as part of the KAoS architecture. The solutions presented have been tested in the Coalition Agents Experiment (CoAX) - an experiment involving coalition military operations.

机译：本文介绍了一种基于运行时策略的信息交换控制方法，该方法可对这些动态发现的代理交互进行更细粒度的控制。 DARPA代理标记语言（DAML）用于表示策略，这些策略可以根据消息的语义内容过滤消息，也可以对消息进行转换以使其适合发布。策略定义，管理和执行是KAoS体系结构的一部分。提出的解决方案已在联盟特工实验（CoAX）中进行了测试-该实验涉及联盟军事行动。

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《3rd International Central and Eastern European Conference on Multi-Agent Systems, CEEMAS 2003 Jun 16-18, 2003 Prague, Czech Republic》|2003年|p.122-135|共14页
会议地点 Prague(CZ);Prague(CZ)
作者
Niranjan Suri; Jeffrey M. Bradshaw; Mark Burstein; Andrzej Uszok; Brett Benyo; Maggie Breedy; Marco Carvalho; David Diller; Renia Jeffers; Matt Johnson; Shri Kulkarni; James Lott;
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作者单位

Institute for Human and Machine Cognition, University of West Florida;

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原文格式 PDF
正文语种 eng
中图分类自动化技术、计算机技术;
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3. Learning data-driven decision-making policies in multi-agent environments for autonomous systems [J] . Hook Joosep, El-Sedky Seif, De Silva Varuna, Cognitive Systems Research . 2021,第Jana期

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4. Toward DAML-based policy enforcement for semantic data transformation and filtering in multi-agent systems [C] . Niranjan Suri, Jeffrey Bradshaw, Andrzej Uszok, International joint conference on Autonomous agents and multiagent systems . 2003

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6. Giving meaning to tweets in emergency situations: a semantic approach for filtering and visualizing social data [O] . Teresa Onorati, Paloma Díaz -1

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机译：基于DamL的策略实施，用于多代理系统中的语义数据转换和过滤

DAML-Based Policy Enforcement for Semantic Data Transformation and Filtering in Multi-agent Systems

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