nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

Multi-agent Simulation for AI Behaviour Discovery in Operations Research

verfasst von : Michael Papasimeon, Lyndon Benke

Erschienen in: Multi-Agent-Based Simulation XXII

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

We describe ACE0, a lightweight platform for evaluating the suitability and viability of AI methods for behaviour discovery in multi-agent simulations. Specifically, ACE0 was designed to explore AI methods for multi-agent simulations used in operations research studies related to new technologies such as autonomous aircraft. Simulation environments used in production are often high-fidelity, complex, require significant domain knowledge and as a result have high R&D costs. Minimal and lightweight simulation environments can help researchers and engineers evaluate the viability of new AI technologies for behaviour discovery in a more agile and potentially cost effective manner. In this paper we describe the motivation for the development of ACE0. We provide a technical overview of the system architecture, describe a case study of behaviour discovery in the aerospace domain, and provide a qualitative evaluation of the system. The evaluation includes a brief description of collaborative research projects with academic partners, exploring different AI behaviour discovery methods.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel On the Same Wavelengths: Emergence of Multiple Synchronies Among Multiple Agents

Nächstes Kapitel Using Agent-Based Modelling to Understand Advantageous Behaviours Against COVID-19 Transmission in the Built Environment

This is a simplified view of the situation, as one can consider higher order features such as turn rates and other time derivatives of the basic state space variables.

Austin, F., Carbone, G., Lewis, M.: Automated maneuvering decisions for air-to-air combat. In: In Proceedings of the Military Computing Conference. Anaheim, California, May 1987

Burgin, G.H., Sidor, L.B.: Rule-Based Air Combat Simulation. Technical Report, Contractor Report 4160, National Aeronautics and Space Administration (NASA) (1988)

Colledanchise, M., Ögren, P.: How behavior trees modularize hybrid control systems and generalize sequential behavior compositions, the subsumption architecture, and decision trees. IEEE Trans. Rob. 33(2), 372–389 (2017). https://doi.org/10.1109/TRO.2016.2633567CrossRef

Evertsz, R., Thangarajah, J., Papasimeon, M.: The conceptual modelling of dynamic teams for autonomous systems. In: Mayr, H.C., Guizzardi, G., Ma, H., Pastor, O. (eds.) ER 2017. LNCS, vol. 10650, pp. 311–324. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69904-2_25CrossRef

Heinze, C., Papasimeon, M., Goss, S., Cross, M., Connell, R.: Simulating fighter pilots. In: Pěchouček, M., Thompson, S.G., Voos, H. (eds.) Defence Industry Applications of Autonomous Agents and Multi-Agent Systems. WSSAT, pp. 113–130. Birkhäuser Basel, Basel (2008). http://dx.doi.org/10.1007/978-3-7643-8571-2_7

Hossam, M., Le, T., Huynh, V., Papasimeon, M., Phung, D.Q.: OptiGAN: generative adversarial networks for goal optimized sequence generation. In: International Joint Conference on Neural Networks (IJCNN). Glasgow, Scotland, UK, July 2020

Hossam, M., Le, T., Papasimeon, M., Huynh, V., Phung, D.: Text generation with deep variational GAN. In: NeurIPS 3rd Workshop on Bayesian Deep Learning. Montreal, Canada, December 2018. http://bayesiandeeplearning.org/2018/papers/157.pdf

Jones, R.M., Wray, R., van Lent, M., Laird, J.E.: Planning in the Tactical Air Domain. Technical Report, aAAI Technical Report FS-94-01, AAAI (1994)

Kluyver, T., et al.: Jupyter Notebooks - a publishing format for reproducible computational workflows. In: Positioning and Power in Academic Publishing: Players, Agents and Agendas, pp. 87–90. IOS Press (2016). https://doi.org/10.3233/978-1-61499-649-1-87

10.

Kurniawan, B., Vamplew, P., Papasimeon, M., Dazeley, R., Foale, C.: An empirical study of reward structures for actor-critic reinforcement learning in air combat manoeuvring simulation. In: Liu, J., Bailey, J. (eds.) AI 2019. LNCS (LNAI), vol. 11919, pp. 54–65. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35288-2_5CrossRef

11.

Kurniawan, B., Vamplew, P., Papasimeon, M., Dazeley, R., Foale, C.: Discrete-to-Deep supervised policy learning: an effective training method for neural reinforcement learning. In: ALA 2020: Adaptive Learning Agents Workshop at AAMAS 2020. Auckland, New Zealand (2020)

12.

Lam, C.P., Masek, M., Kelly, L., Papasimeon, M., Benke, L.: A simheuristic approach for evolving agent behaviour in the exploration for novel combat tactics. Oper. Res. Perspect. 6, 100123 (2019). https://doi.org/10.1016/j.orp.2019.100123MathSciNetCrossRef

13.

Lipovetzky, N., Geffner, H.: Width and serialization of classical planning problems. In: ECAI, pp. 540–545 (2012)

14.

Martzinotto, A., Colledanchise, M., Smith, C., Ögren, P.: Towards a unified behavior trees framework for robot control. In: In proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), pp. 5420–5427 (2014). https://doi.org/10.1109/icra.2014.6907656

15.

Masek, M., Lam, C.P., Benke, L., Kelly, L., Papasimeon, M.: Discovering emergent agent behaviour with evolutionary finite state machines. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds.) PRIMA 2018. LNCS (LNAI), vol. 11224, pp. 19–34. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03098-8_2CrossRef

16.

Masek, M., Lam, C.P., Kelly, L., Benke, L., Papasimeon, M.: A genetic programming framework for novel behaviour discovery in air combat scenarios. In: Ernst, A.T., Dunstall, S., García-Flores, R., Grobler, M., Marlow, D. (eds.) Data and Decision Sciences in Action 2. LNMIE, pp. 263–277. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-60135-5_19CrossRef

17.

McGrew, J.S., How, J.P.: Air combat strategy using approximate dynamic programming. J. Guidance Control Dyn. 33, 1641–1654 (2010)CrossRef

18.

Papasimeon, M., Pearce, A., Goss, S.: The human agent virtual environment. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems. AAMAS 2007. Association for Computing Machinery, New York, NY, USA (2007). https://doi.org/10.1145/1329125.1329463

19.

Park, H., Lee, B.Y., Tahk, M.J., Yoo, D.W.: Differential game based air combat maneuver generation using scoring function matrix. Int. J. Aeronaut. Space Sci. 17(2), 204–213 (2016)CrossRef

20.

Pérez, F., Granger, B.E.: IPython: a system for interactive scientific computing. Comput. Sci. Eng. 9(3), 21–29 (2007)CrossRef

21.

Ramirez, M., Papasimeon, M., Benke, L., Lipovetzky, N., Miller, T., Pearce, A.R.: Real-Time UAV maneuvering via automated planning in simulations. In: 26th International Joint Conference on Artificial Intelligence (IJCAI), pp. 5243–5245. Melbourne, Australia, August 2017

22.

Ramirez, M., et al.: Integrated hybrid planning and programmed control for real time UAV maneuvering. In: 17th Int. Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1318–1326. Stockholm, Sweden, July 2018

23.

Shaw, R.L.: Fighter Combat: Tactics and Maneuvering. Naval Institute Press, Annapolis (1985)

24.

Smith, R., Dike, B., Ravichandran, B., El-Fallah, A., Mehra, K.: Discovering novel fighter combat maneuvers: simulating test pilot creativity. In: Bentley, P.J., Corne, D.W. (eds.) Creative Evolutionary Systems, p. 467 - VIII. The Morgan Kaufmann Series in Artificial Intelligence, Morgan Kaufmann, San Francisco (2002). https://doi.org/10.1016/B978-155860673-9/50059-8

25.

Tidhar, G., Heinze, C., Selvestrel, M.: Flying together: modelling air mission teams. Appl. Intell. 8(3), 195–218 (1998)CrossRef

26.

Toubman, A.: Calculated Moves: Generating Air Combat Behaviour. Ph.D. thesis, Leiden University, The Netherlands (2020)

27.

Vered, M., Kaminka, G.A.: Heuristic online goal recognition in continuous domains. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 4447–4454. Melbourne, Australia (2017)

28.

Zhang, L.A., et al.: Air Dominance Through Machine Learning: A Preliminary Exploration of Artificial Intelligence-Assisted Mission Planning. Technical Report, RR4311, RAND Corporation, Santa Monica, CA, USA (2020). https://www.rand.org/pubs/research_reports/RR4311.html

Titel: Multi-agent Simulation for AI Behaviour Discovery in Operations Research
verfasst von: Michael Papasimeon
Lyndon Benke
Verlag: Springer International Publishing
Buch: Multi-Agent-Based Simulation XXII
Print ISBN: 978-3-030-94547-3

Electronic ISBN: 978-3-030-94548-0

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-94548-0_6

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"