DocumentCode :
2222575
Title :
A multiobjective optimisation approach for the dynamic inference and refinement of agent-based model specifications
Author :
Adra, Salem F. ; Kiran, Mariam ; McMinn, Phil ; Walkinshaw, Neil
Author_Institution :
STC, Microsoft, London, UK
fYear :
2011
fDate :
5-8 June 2011
Firstpage :
2237
Lastpage :
2244
Abstract :
Despite their increasing popularity, agent-based models are hard to test, and so far no established testing technique has been devised for this kind of software applications. Reverse engineering an agent-based model specification from model simulations can help establish a confidence level about the implemented model and in some cases reveal discrepancies between observed and normal or expected behaviour. In this study, a multiobjective optimisation technique based on a simple random search algorithm is deployed to dynamically infer and refine the specification of three agent-based models from their simulations. The multiobjective optimisation technique also incorporates a dynamic invariant detection technique which serves to guide the search towards uncovering new model behaviour that better captures the model specification. The Non-dominated Sorting Genetic Algorithm (NSGA-II) was also deployed to replace the random search algorithm, and the results from both approaches were compared. While both algorithms revealed good potential in capturing the model specifications, the pure exploratory nature of random search was found more suitable for the application at hand, compared to the balanced exploitation/exploration nature of genetic algorithms in general.
Keywords :
genetic algorithms; inference mechanisms; multi-agent systems; search problems; agent based model specifications; dynamic inference; dynamic invariant detection technique; multiobjective optimisation approach; nondominated sorting genetic algorithm; random search algorithm; reverse engineering; Biological system modeling; Computational modeling; Economics; Predator prey systems; Rabbits; Search problems; Skin;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location :
New Orleans, LA
ISSN :
Pending
Print_ISBN :
978-1-4244-7834-7
Type :
conf
DOI :
10.1109/CEC.2011.5949892
Filename :
5949892
Link To Document :
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