Abstract
This paper studies a sequential adversarial incomplete information game, the attack-defense game, with multiple defenders against one attacker. The attacker has limited information on game configurations and makes guesses of the correct configuration based on observations of defenders’ actions. Challenges for multi-agent incomplete information games include scalability in terms of agents’ joint state and action space, and high dimensionality due to sequential actions. We tackle this problem by introducing deceptive actions for the defenders to mislead the attacker’s belief of correct game configuration. We propose a k-step deception strategy for the defender team that forward simulates the attacker and defenders’ actions within k steps and computes the locally optimal action. We present results based on comparisons of different parameters in our deceptive strategy. Experiments show that our approach outperforms Bayesian Nash Equilibrium strategy, a strategy commonly used for adversarial incomplete information games, with higher expected rewards and less computation time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brown, N., Sandholm, T.: Superhuman AI for multiplayer poker. Science 365(6456), 885–890 (2019)
Carroll, T.E., Grosu, D.: A game theoretic investigation of deception in network security. Secur. Commun. Netw. 4(10), 1162–1172 (2011)
Chaslot, G., Bakkes, S., Szita, I., Spronck, P.: Monte-Carlo tree search: a new framework for game AI. In: AIIDE (2008)
Deng, Z., Kong, Z.: Multi-agent cooperative pursuit-defense strategy against one single attacker. IEEE Robot. Autom. Lett. 5(4), 5772–5778 (2020)
Durkota, K., Lisỳ, V., Bošanskỳ, B., Kiekintveld, C.: Optimal network security hardening using attack graph games. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)
Gilpin, A., Sandholm, T.: Solving two-person zero-sum repeated games of incomplete information. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 2, pp. 903–910 (2008)
Gmytrasiewicz, P.J., Doshi, P.: A framework for sequential planning in multi-agent settings. J. Artif. Intell. Res. 24, 49–79 (2005)
Harsanyi, J.C.: Games with incomplete information played by “Bayesian” players, i–iii part i. the basic model. Manag. Sci. 14(3), 159–182 (1967)
Horák, K., Zhu, Q., Bošanskỳ, B.: Manipulating adversary’s belief: a dynamic game approach to deception by design for proactive network security. In: Rass, S., An, B., Kiekintveld, C., Fang, F., Schauer, S. (eds.) GameSec 2017. LNCS, vol. 10575, pp. 273–294. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68711-7_15
Li, L., Shamma, J.: LP formulation of asymmetric zero-sum stochastic games. In: 53rd IEEE Conference on Decision and Control, pp. 1930–1935. IEEE (2014)
Lisỳ, V., Zivan, R., Sycara, K., Pěchouček, M.: Deception in networks of mobile sensing agents. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 1031–1038 (2010)
Nash, J.: Non-cooperative games. Ann. Math. 54, 286–295 (1951)
Nguyen, T.H., Wang, Y., Sinha, A., Wellman, M.P.: Deception in finitely repeated security games. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 2133–2140 (2019)
Píbil, R., Lisý, V., Kiekintveld, C., Bošanský, B., Pěchouček, M.: Game theoretic model of strategic honeypot selection in computer networks. In: Grossklags, J., Walrand, J. (eds.) GameSec 2012. LNCS, vol. 7638, pp. 201–220. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34266-0_12
Sengupta, S., Kambhampati, S.: Multi-agent reinforcement learning in Bayesian Stackelberg Markov games for adaptive moving target defense. arXiv preprint arXiv:2007.10457 (2020)
Shapley, L.S.: Stochastic games. Proc. Natl. Acad. Sci. 39(10), 1095–1100 (1953)
Sincák, D.: Multi-robot control system for pursuit-evasion problem. J. Electr. Eng. 60(3), 143–148 (2009)
Torreño, A., Onaindia, E., Sapena, Ó.: A flexible coupling approach to multi-agent planning under incomplete information. Knowl. Inf. Syst. 38(1), 141–178 (2014)
Vidal, R., Rashid, S., Sharp, C., Shakernia, O., Kim, J., Sastry, S.: Pursuit-evasion games with unmanned ground and aerial vehicles. In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164), vol. 3, pp. 2948–2955. IEEE (2001)
Yi, S., Nam, C., Sycara, K.: Indoor pursuit-evasion with hybrid hierarchical partially observable Markov decision processes for multi-robot systems. In: Correll, N., Schwager, M., Otte, M. (eds.) Distributed Autonomous Robotic Systems. SPAR, vol. 9, pp. 251–264. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05816-6_18
Zhuang, J., Bier, V.M., Alagoz, O.: Modeling secrecy and deception in a multiple-period attacker-defender signaling game. Eur. J. Oper. Res. 203(2), 409–418 (2010)
Acknowledgments
This work has been supported in part by AFOSR Award FA9550-18-1-0097 and AFOSR/AFRL award FA9550-18-1-0251.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, X., Yi, S., Sycara, K. (2022). Multi-agent Deception in Attack-Defense Stochastic Game. In: Matsuno, F., Azuma, Si., Yamamoto, M. (eds) Distributed Autonomous Robotic Systems. DARS 2021. Springer Proceedings in Advanced Robotics, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-92790-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-92790-5_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92789-9
Online ISBN: 978-3-030-92790-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)