loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Ying Zhao 1 ; Emily Mooren 1 and Nate Derbinsky 2

Affiliations: 1 Naval Postgraduate School, United States ; 2 Northeastern University, United States

Keyword(s): Reinforcement Learning, Combat Identification, Soar, Cognitive Functions, Decision Making, Machine Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Expert Systems ; Health Information Systems ; Human-Machine Cooperation ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Ontology Matching and Alignment ; Symbolic Systems

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-mak ing to reduce the cognitive load of human operators. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.214.106.184

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 (IC3K 2017) - KEOD; ISBN 978-989-758-272-1; ISSN 2184-3228, SciTePress, pages 233-238. DOI: 10.5220/0006508702330238

@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 (IC3K 2017) - KEOD},
year={2017},
pages={233-238},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006508702330238},
isbn={978-989-758-272-1},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017) - KEOD
TI - Reinforcement Learning for Modeling Large-Scale Cognitive Reasoning
SN - 978-989-758-272-1
IS - 2184-3228
AU - Zhao, Y.
AU - Mooren, E.
AU - Derbinsky, N.
PY - 2017
SP - 233
EP - 238
DO - 10.5220/0006508702330238
PB - SciTePress