Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-32729-2-2
Title: Probabilistic model checking multi-agent behaviors in dispersion games using counter abstraction
Authors: Hao, J.
Song, S.
Liu, Y. 
Sun, J.
Gui, L.
Dong, J.S. 
Leung, H.-F.
Issue Date: 2012
Citation: Hao, J.,Song, S.,Liu, Y.,Sun, J.,Gui, L.,Dong, J.S.,Leung, H.-F. (2012). Probabilistic model checking multi-agent behaviors in dispersion games using counter abstraction. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7455 LNAI : 16-30. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-32729-2-2
Abstract: Accurate analysis of the stochastic dynamics of multi-agent system is important but challenging. Probabilistic model checking, a formal technique for analysing a system which exhibits stochastic behaviors, can be a natural solution to analyse multi-agent systems. In this paper, we investigate this problem in the context of dispersion games focusing on two strategies: basic simple strategy (BSS) and extended simple strategies (ESS). We model the system using discrete-time Markov chain (DTMC) and reduce the state space of the models by applying counter abstraction technique. Two important properties of the system are considered: convergence and convergence rate. We show that these kinds of properties can be automatically analysed and verified using probabilistic model checking techniques. Better understanding of the dynamics of the strategies can be obtained compared with empirical evaluations in previous work. Through the analysis, we are able to demonstrate that probabilistic model checking technique is applicable, and indeed useful for automatic analysis and verification of multi-agent dynamics. © Springer-Verlag Berlin Heidelberg 2012.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/78297
ISBN: 9783642327285
ISSN: 03029743
DOI: 10.1007/978-3-642-32729-2-2
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

5
checked on Nov 16, 2018

Page view(s)

39
checked on Nov 9, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.