Publication:
Reinforcement Learning for Modeling Large-Scale Cognitive Reasoning

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Authors
Zhao, Ying
Mooren, Emily
Derbinsky, Nate
Subjects
Reinforcement Learning
Combat Identification
Soar
Cognitive Functions
Decision Making
Machine Learning
Advisors
Date of Issue
2017
Date
Publisher
SCITEPRESS
Language
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.
Type
Conference Paper
Description
KEOD 2017 - 9th International Conference on Knowledge Engineering and Ontology Development
Series/Report No
Department
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Naval Postgraduate School
Funder
Format
6 p.
Citation
Zhao, Ying, Emily Mooren, and Nate Derbinsky. "Reinforcement Learning for Modeling Large-Scale Cognitive Reasoning." KEOD. 2017.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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