Title
SinCity 2.0: An Environment for Exploring the Effectiveness of Multi-agent Learning Techniques
Abstract
In this paper we present an extensive and practical analysis of Multi-agent learning strategies using our open simulator SinCity 2.0. Sin City has been developed in Net Logo and it can be considered as an extension of the simple predator-prey pursuit problem. In our case, the predators are substituted by police cars, the prey by a thief and the chase is performed in an urban grid environment. Sin City allows to model, in a graphical friendly environment, different strategies for both the police and the thief, also implying coordination and communication among the agent set. We build this model, introducing traffic and more agent's behaviors to have a more realistic and complex scenario. We also present the results of multiple experiments performed, comparing some classical learning strategies and their performance.
Year
DOI
Venue
2010
10.1007/978-3-642-12384-9_24
ADVANCES IN PRACTICAL APPLICATIONS OF AGENTS AND MULTIAGENT SYSTEMS
Field
DocType
Volume
Robot learning,Computer science,NetLogo,Human–computer interaction,Chase,Friendly environment,Artificial intelligence,Error-driven learning,Machine learning,Grid,Proactive learning
Conference
70
ISSN
Citations 
PageRank 
1867-5662
0
0.34
References 
Authors
4
4