Reinforcement Learning for Modeling Large-Scale Cognitive Reasoning

Ying Zhao, Emily Mooren, Nate Derbinsky

Abstract

Accurate, relevant, and timely combat identification (CID) enables warfighters to locate and identify critical airborne targets with high precision. The current CID processes included a wide combination of platforms, sensors, networks, and decision makers. There are diversified doctrines, rules of engagements, knowledge databases, and expert systems used in the current process to make the decision making very complex. Furthermore, the CID decision process is still very manual. Decision makers are constantly overwhelmed with the cognitive reasoning required. Soar is a cognitive architecture that can be used to model complex reasoning, cognitive functions, and decision making for warfighting processes like the ones in a kill chain. In this paper, we present a feasibility study of Soar, and in particular the reinforcement learning (RL) module, for optimal decision making using existing expert systems and smart data. The system has the potential to scale up and automate CID decision-making to reduce the cognitive load of human operators.

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Paper Citation


in Harvard Style

Zhao Y., Mooren E. and Derbinsky N. (2017). Reinforcement Learning for Modeling Large-Scale Cognitive Reasoning.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, ISBN 978-989-758-272-1, pages 233-238. DOI: 10.5220/0006508702330238


in Bibtex Style

@conference{keod17,
author={Ying Zhao and Emily Mooren and Nate Derbinsky},
title={Reinforcement Learning for Modeling Large-Scale Cognitive Reasoning},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,},
year={2017},
pages={233-238},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006508702330238},
isbn={978-989-758-272-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,
TI - Reinforcement Learning for Modeling Large-Scale Cognitive Reasoning
SN - 978-989-758-272-1
AU - Zhao Y.
AU - Mooren E.
AU - Derbinsky N.
PY - 2017
SP - 233
EP - 238
DO - 10.5220/0006508702330238