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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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References

  1. Wray R, Chong R, Phillips J, Rogers S, Walsh B (1994) A survey of cognitive and agent architectures. Retrieved 28 Jan 2007 from http://ai.eecs.umich.edu/cogarch0/

  2. Chong HQ, Tan AH, Ng GW (2007) Integrated cognitive architectures: a survey. Artif Intell Rev 28:103–130

    Article  Google Scholar 

  3. Laird JE (2012) The soar cognitive architecture. MIT Press, Cambridge

    Book  Google Scholar 

  4. Anderson JR (1983) The architecture of cognition. Harvard University Press, Harvard

    Google Scholar 

  5. Anderson JR (1997) How can the human mind exist in the physical universe?. Oxford University Press, Oxford

    Google Scholar 

  6. Anderson JR, Bothell D, Byrne MD, Douglass S, Lebiere C, Qin Y (2004) An integrated theory of the mind. Psychol Rev 111(4):1036–1060. https://doi.org/10.1037/0033-295X.111.4.1036

    Article  Google Scholar 

  7. Atahary T, Taha T, Douglass S (2016) Parallelized mining of domain knowledge on GPGPU and Xeon Phi clusters. J Supercomput 72:2132. https://doi.org/10.1007/s11227-016-1712-0

    Article  Google Scholar 

  8. Luckham D (2008) The power of events: an introduction to complex event processing in distributed enterprise systems. Springer, Berlin

    Google Scholar 

  9. Douglass S, Myers C (2010) Concurrent knowledge activation calculation in large declarative memories. In: Proceedings of the 10th International Conference on Cognitive Modeling, pp 55–60

  10. EsperTech-Esper. http://www.espertech.com/esper/

  11. Russell S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  12. Brailsford SC, Potts CN, Smith BM (1999) Constraint satisfaction problems: algorithms and applications. Eur J Oper Res 119(3):557–581. https://doi.org/10.1016/S0377-2217(98)00364-6

    Article  MATH  Google Scholar 

  13. Kumar V (1992) Algorithms for constraint satisfaction problems: a survey. AI Mag 13(1):32–44

    Google Scholar 

  14. Atahary T, Taha T, Webber F, Douglass S (2015) Knowledge mining for cognitive agents through path based forward checking. In: International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp 5–12

  15. Atahary T, Taha T, Douglass S (2017) Knowledge mining with multiple optimizations on a GPGPU for an autonomous cognitive agent. In: IEEE-2017 SAI Computing Conference (SAI), UK, July 18–20

  16. Zeigler BP, Hammonds PE (2000) Modeling & simulation-based data engineering: introducing pragmatics into ontologies for net-centric information exchange, 1st edn. Academic Press, London

    Google Scholar 

  17. Benavides D, Segura S, Ruiz-Cortés A (2010) Automated analysis of feature models 20 years later: a literature review. Inf Syst 35(6):615–636

    Article  Google Scholar 

  18. Gent IP, Jefferson C, Miguel I, Moore NCA, Nightingale P, Prosser P, Unsworth C (2011) A preliminary review of literature on parallel constraint solving. In: Workshop on Parallel Methods for Constraint Solving (PMCS’11)

  19. Gent I, Miguel I, Nightingale P, Mccreesh C, Prosser P, Moore N, Unsworth C (2018) A review of literature on parallel constraint solving. Theory Pract Logic Program 18(5–6):725–758. https://doi.org/10.1017/s1471068418000340

    Article  MathSciNet  MATH  Google Scholar 

  20. Schulte C (2000) Parallel search made simple. In: Proceedings of TRICS: Techniques for Implementing Constraint programming Systems, a Post-Conference Workshop of CP

  21. Bordeaux L, Hamadi Y, Samulowitz H (2009) Experiments with massively parallel constraint solving. In: Boutilier C (ed) IJCAI, pp 443–448

  22. Xie F, Davenport A (2010) Massively parallel constraint programming for supercomputers: challenges and initial results. In: Lodi A, Milano M, Toth P (eds) LNCS, vol 6140. Springer, Heidelberg, pp 334–338

    Google Scholar 

  23. Régin JC, Rezgui M, Malapert A (2014) Improvement of the embarrassingly parallel search for data centers. In: Principles and Practice of Constraint Programming, Lecture Notes in Computer Science, pp 622–635; ISBN 319-10428-7

  24. Caniou Y, Codognet P, Richoux F, Diaz D, Abreu S (2014) Large-scale parallelism for constraint-based local search: the costas array case study. Constraints 20(1):30–56

    Article  Google Scholar 

  25. Diaz D, Abreu S, Codognet P (2012) Targeting the cell broadband engine for constraint-based local search. https://doi.org/10.1002/cpe.20oct2012

  26. Diaz D, Abreu S, Codognet P (2010) Parallel constraint-based local search on the Cell/BE multicore architecture. In: Essaaidi M, Malgeri M, Badica C (eds) Intelligent Distributed Computing IV. Studies in Computational Intelligence, vol 315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15211-5_28

  27. Luong TV, Melab N, Talbi EG (2010) Local search algorithms on graphics processing units, lecture notes in computer science, vol. 6022. Springer, Berlin, pp 264–275

    Google Scholar 

  28. Codognet P, Munera D, Diaz D, Abreu S (2018) Parallel local search. In: Hamadi Y, Sais L (eds) Handbook of parallel constraint reasoning. Springer, Cham

    Google Scholar 

  29. Qiao WB, Créput JC (2019) Massive 2-opt and 3-opt moves with high performance GPU local search to large-scale traveling salesman problem. In: Battiti R, Brunato M, Kotsireas I, Pardalos P (eds) Learning and Intelligent Optimization (LION 12 2018), Lecture Notes in Computer Science, vol 11353. Springer, Cham

  30. Liu K, Löffler S, Hofstedt P (2019) Parallel stochastic portfolio search for constraint solving. In: 2019 IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), Xiamen, China, pp 697–704

  31. Archibald B, Maier P, Stewart R, Trinder P (2020) YewPar: skeletons for exact combinatorial search. In: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP’20). Association for Computing Machinery, New York, pp 292–307. https://doi.org/10.1145/3332466.3374537

  32. Chu G, Schulte C, Stuckey PJ (2009) Confidence-based work stealing in parallel constraint programming. In: Gent IP (ed) LNCS, vol 5732. Springer, Heidelberg, pp 226–241

    Google Scholar 

  33. Rolf C (2011) Parallelism in constraint programing, PhD Thesis

  34. Rolf CC, Kuchcinski K (2008) State-copying and recomputation in parallel constraint programming with global constraints. In: Parallel, Distributed and Network-Based Processing. IEEE Computer Society, Washington, DC, USA, pp 311–317

  35. GPU AI for Board Games. http://developer.nvidia.com/gpu-ai-board-games. Accessed 10 July 2012

  36. Gianpaolo C, Alessandro M (2012) Complex event processing with T-REX. J Syst Softw 85:1709–1728. https://doi.org/10.1016/j.jss.2012.03.056

    Article  Google Scholar 

  37. Huang H, Zhao L, Huang H, Guo S (2019) Machine fault detection for intelligent self-driving networks. IEEE Commun Mag. https://doi.org/10.1109/MCOM.001.1900283

    Article  Google Scholar 

  38. Habbas Z, Krajecki M, Singer D (2000) Parallel resolution of CSP with OpenMP. In: Proceedings of the Second European Workshop on OpenMP, Edinburgh, Scotland, pp 1–8

  39. Habbas Z, Krajecki M, Singer D (2001) Shared memory im-plementation of Constraint satisfaction problem resolution. Parallel Process Lett 11(4):487–501

    Article  Google Scholar 

  40. Farhang Y, Meybodi MR, Hatamlou AR (2008) Improving the efficiency of forward checking algorithm for solving constraint satisfaction problems. In: Eighth International Conference on Intelligent Systems Design and Applications

  41. Kimmig R, Meyerhenke H, Strash D (2017) Shared memory parallel subgraph enumeration. In: 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Lake Buena Vista, FL, 2017, pp 519–529. https://doi.org/10.1109/ipdpsw.2017.133

  42. Bell 47 helicopter https://en.wikipedia.org/wiki/Bell_47

  43. https://en.wikipedia.org/wiki/Sikorsky_HH-60_Pave_Hawk

  44. https://en.wikipedia.org/wiki/Raytheon_Sentinel

  45. Narendra J, Guillaume R, Xavier L (2008) Choco: an open source java constraint programming library In: Workshop on Open-Source Software for Integer and Constraint Programming

  46. Oakley cluster at Ohio Supercomputer center https://www.osc.edu/supercomputing/computing/oakley

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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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  2. Tarek M. Taha
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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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