Abstract
Robot swarms provide a way for a number of simple robots to work together to carry out a task. While swarms have been found to be adaptable, fault-tolerant and widely applicable, designing individual robot algorithms so as to ensure effective and correct swarm behaviour is very difficult. In order to assess swarm effectiveness, either experiments with real robots or computational simulations of the swarm are usually carried out. However, neither of these involve a deep analysis of all possible behaviours. In this paper we will utilise automated formal verification techniques, involving an exhaustive mathematical analysis, in order to assess whether our swarms will indeed behave as required.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Beni, G.: From Swarm Intelligence to Swarm Robotics. In: Şahin, E., Spears, W.M. (eds.) SAB 2004. LNCS, vol. 3342, pp. 1–9. Springer, Heidelberg (2005)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. J. Artificial Societies and Social Simulation 4(1) (2001)
Clarke, E., Grumberg, O., Peled, D.: Model Checking. MIT Press, Cambridge (1999)
Duflot, M., Kwiatkowska, M., Norman, G., Parker, D.: A Formal Analysis of Bluetooth Device Discovery. Int. J. Software Tools Techn. Transfer 8(6), 621–632 (2006)
Hansson, H., Jonsson, B.: A Logic for Reasoning about Time and Reliability. Formal Aspects of Computing 6, 102–111 (1994)
Hinton, A., Kwiatkowska, M., Norman, G., Parker, D.: PRISM: A Tool for Automatic Verification of Probabilistic Systems. In: Hermanns, H., Palsberg, J. (eds.) TACAS 2006. LNCS, vol. 3920, pp. 441–444. Springer, Heidelberg (2006)
Labella, T.H., Dorigo, M., Deneubourg, J.L.: Efficiency and Task Allocation in Prey Retrieval. In: Ijspeert, A.J., Murata, M., Wakamiya, N. (eds.) BioADIT 2004. LNCS, vol. 3141, pp. 274–289. Springer, Heidelberg (2004)
Lerman, K., Galstyan, A.: Mathematical Model of Foraging in a Group of Robots: Effect of Interference. Autonomous Robots 13(2), 127–141 (2002)
Lerman, K., Martinoli, A., Galstyan, A.: A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems. In: Şahin, E., Spears, W.M. (eds.) SAB 2004. LNCS, vol. 3342, pp. 143–152. Springer, Heidelberg (2005)
Liu, W., Winfield, A., Sa, J.: Modelling Swarm Robotic Systems: A Study in Collective Foraging. In: Proc. Towards Autonomous Robotic Systems (TAROS), pp. 25–32 (2007)
Liu, W., Winfield, A., Sa, J., Chen, J., Dou, L.: Strategies for Energy Optimisation in a Swarm of Foraging Robots. In: Şahin, E., Spears, W.M., Winfield, A.F.T. (eds.) SAB 2006 Ws 2007. LNCS, vol. 4433, pp. 14–26. Springer, Heidelberg (2007)
Sahin, E., Winfield, A.F.T.: Special Issue on Swarm Robotics. Swarm Intelligence 2(2-4), 69–72 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Konur, S., Dixon, C., Fisher, M. (2010). Formal Verification of Probabilistic Swarm Behaviours. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2010. Lecture Notes in Computer Science, vol 6234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15461-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-15461-4_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15460-7
Online ISBN: 978-3-642-15461-4
eBook Packages: Computer ScienceComputer Science (R0)