A distributed reinforcement learning approach to mission survivability in tactical MANETs

Authors:
Marco Carvalho
Affiliations:
Institute for Human and Machine Cognition, Pensacola, FL
Venue:
Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research: Cyber Security and Information Intelligence Challenges and Strategies
Year:
2009

Citing 2
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Abstract

In this paper we present an ongoing research to develop a distributed reinforcement learning approach for mission survivability that combines two basic strategies for mission resilience: a) mission decomposition and distribution with replication of critical components, and b) differential task allocation based on estimated level of threat. Level of threat is estimated from a locally perceived attack, or the possibility of an attack, based on threat information that is shared between similar nodes.