Abstract
In order for an agent or a group of agents (such as a team) to achieve a goal, a sequence of actions have to be performed. These actions bring about state transitions that constitute a plan. Multiple ways of achieving the goal may exist. In some situations, one may want to prevent or delay an agent or group of agents from achieving a goal. We argue that plans can be disrupted by preventing particular state transitions from happening. We propose four algorithms to identify which state transitions should be thwarted such that the achievement of the goal is prevented (total disruption) or delayed (partial disruption). In order to evaluate the performance of our algorithms we define disruption (partial and total) and also provide metrics for its measurement. We do acknowledge that the disruptor may not always have an accurate representation of the disruptee’s plans. Thus, we perform an experimental analysis to examine the performance of the algorithms when some of the state transitions available to the disruptee are unknown to the disruptor.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Grosz, B.J., Hunsberger, L., Kraus, S.: Planning and acting together. AI Mag. 20(4), 23–34 (1999)
Georgeff, M.P., Lansky, A.L.: Reactive reasoning and planning. In: Proceedings of the 6th National Conference on Artificial Intelligence, pp. 677–682 (1987)
d’Inverno, M., Kinny, D., Luck, M., Wooldridge, M.: A formal specification of dMARS. In: Rao, A., Singh, M.P., Wooldridge, M.J. (eds.) ATAL 1997. LNCS, vol. 1365, pp. 155–176. Springer, Heidelberg (1998)
Voinitchi, A., Black, E., Luck, M.: Introduction to team disruption mechanisms. In: Proceedings of the 2012 Imperial College Computing Student Workshop, pp. 149–155 (2012)
McDermott, D.: Robot planning. AI Mag. 13(2), 55–79 (1992)
Hanks, S., McDermott, D.: Modeling a dynamic and uncertain world: symbolic and probabilistic reasoning about change. Artif. Intell. 66(1), 1–55 (1994)
Porteous, J., Sebastia, L.: Extracting and ordering landmarks for planning. J. Artif. Intell. Res. 22(1), 215–278 (2004)
Schneier, B.: Attack trees: modeling security threats. Dr. Dobbs J. Softw. Tools 24(12), 21–29 (1999)
Mauw, S., Oostdijk, M.: Foundations of attack trees. In: Won, D.H., Kim, S. (eds.) ICISC 2005. LNCS, vol. 3935, pp. 186–198. Springer, Heidelberg (2006)
Kordy, B., Mauw, S., Radomirović, S., Schweitzer, P.: Foundations of attack–defense trees. In: Degano, P., Etalle, S., Guttman, J. (eds.) FAST 2010. LNCS, vol. 6561, pp. 80–95. Springer, Heidelberg (2011)
Kordy, B., Kordy, P., Mauw, S., Schweitzer, P.: ADTool: security analysis with attack–defense trees. In: Joshi, K., Siegle, M., Stoelinga, M., D’Argenio, P.R. (eds.) QEST 2013. LNCS, vol. 8054, pp. 173–176. Springer, Heidelberg (2013)
Kordy, B., Mauw, S., Schweitzer, P.: Quantitative questions on attack–defense trees. In: Kwon, T., Lee, M.-K., Kwon, D. (eds.) ICISC 2012. LNCS, vol. 7839, pp. 49–64. Springer, Heidelberg (2013)
TRESsPASS: The tresspass project (2015)
Carbonell, J.G.: Counterplanning: a strategy-based model of adversary planning in real-world situations. Artif. Intell. 16(3), 295–329 (1981)
de Cote, E.M., Chapman, A., Sykulski, A.M., Jennings, N.: Automated planning in repeated adversarial games. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, pp. 376–383 (2010)
Willmott, S., Richardson, J., Bundy, A., Levine, J.: Applying adversarial planning techniques to go. Theor. Comput. Sci. 252(12), 45–82 (2001)
Huang, H., Ding, J., Zhang, W., Tomlin, C.J.: A differential game approach to planning in adversarial scenarios: a case study on capture the flag. In: Proceedings of the 2011 IEEE International Conference on Robotics and Automation, pp. 1451–1456 (2011)
Tambe, M.: Towards flexible teamwork. J. Artif. Intell. Res. 7(1), 83–124 (1997)
Pynadath, D.V., Tambe, M.: Multiagent teamwork: analyzing the optimality and complexity of key theories and models. In: Proceedings of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 873–880 (2002)
Jennings, N.R.: Controlling cooperative problem solving in industrial multi-agent systems using joint intentions. Artif. Intell. 75(2), 195–240 (1995)
Jennings, N.R., Mamdani, E.H.: Using joint responsibility to coordinate collaborative problem solving in dynamic environments. In: Proceedings of the 10th National Conference on Artificial Intelligence, pp. 269–275 (1992)
Fulkerson, D.R., Ford, L.R.: Maximal flow through a network. Can. J. Math. 8(1), 399–404 (1956)
Goldberg, A.V., Tarjan, R.E.: A new approach to the maximum-flow problem. J. ACM 25(4), 921–940 (1988)
Stoer, M., Wagner, F.: A simple min-cut algorithm. J. ACM 44(4), 585–591 (1997)
Karger, D.R., Stein, C.: A new approach to the minimum cut problem. J. ACM 43(4), 601–640 (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Voinitchi, A., Black, E., Luck, M. (2015). Towards the Disruption of Plans. In: Ghose, A., Oren, N., Telang, P., Thangarajah, J. (eds) Coordination, Organizations, Institutions, and Norms in Agent Systems X. COIN 2014. Lecture Notes in Computer Science(), vol 9372. Springer, Cham. https://doi.org/10.1007/978-3-319-25420-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-25420-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25419-7
Online ISBN: 978-3-319-25420-3
eBook Packages: Computer ScienceComputer Science (R0)