DAML-based policy enforcement for semantic data transformation and filtering in multi-agent systems

Authors:
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
Affiliations:
Institute for Human and Machine Cognition, University of West Florida and Lancaster University;Institute for Human and Machine Cognition, University of West Florida;BBN Technologies;Institute for Human and Machine Cognition, University of West Florida;BBN Technologies;Institute for Human and Machine Cognition, University of West Florida;Institute for Human and Machine Cognition, University of West Florida;BBN Technologies;Institute for Human and Machine Cognition, University of West Florida;Institute for Human and Machine Cognition, University of West Florida;Institute for Human and Machine Cognition, University of West Florida;Institute for Human and Machine Cognition, University of West Florida
Venue:
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
Year:
2003

Citing 6
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Abstract

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.