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Parallelized path-based search for constraint satisfaction in autonomous cognitive agents

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Abstract

Cognitive agents are typically utilized in autonomous systems for automated decision making. With the widespread use of autonomous systems in complex environments, the need for real-time cognitive agents is essential. Cognitive agents are more capable when they are able to process larger amounts of information to make more informed and intelligent decisions. The solution search space for cognitive agents increases exponentially with large volumes of varied data. In this paper, we present the parallelization of the knowledge-mining component of a cognitive agent that can be programmed to reason like humans. This study examined a novel high-performance path-based forward checking algorithm on 128 compute nodes at the Ohio Supercomputing Center (768 cores) to achieve a speedup of over 200 times compared to a serial implementation of our algorithm. The serial implementation is around 10–25 times faster than a conventional Java-based constraint solver at generating the first solution.

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Acknowledgements

This work is supported through funds from AFRL and AFOSR.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Dayton, Dayton, OH, 45469, USA

    Tanvir Atahary & Tarek M. Taha

  2. Air Force Research Laboratory, 711 Human Performance Wing, Wright-Patterson AFB, OH, 45433-7955, USA

    Scott Douglass

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  1. Tanvir Atahary
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  3. Scott Douglass
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Correspondence to Tanvir Atahary.

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Atahary, T., Taha, T.M. & Douglass, S. Parallelized path-based search for constraint satisfaction in autonomous cognitive agents. J Supercomput 77, 1667–1692 (2021). https://doi.org/10.1007/s11227-020-03339-2

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