Abstract
Applications of multi-agent system (MAS) are versatile. In this chapter we focus on a specific application domain—agent-oriented programming for distributed constraint reasoning (DCR ). The field of DCR deals with constraints -based problems that are distributed among multiple agents. The agents need to arrive at an optimal solution to the global combinatorial problem, and in order to do so, they run a distributed search algorithm . Another important aspect of MAS software development is MAS simulation . In this regard, this chapter introduces a new agent-based research tool for designing and testing DCR algorithms. The new tool—AgentZero—is specifically designed for the specification, implementation, and evaluation of DCR search algorithms. AgentZero provides full support to researchers of distributed constraints algorithms in the form of an extensive agent-based environment for algorithmic research that includes a distributed run-time environment, built-in performance measures that are automatically used by all algorithms, and visualization tools that help design and understand the behavior of complex distributed search algorithm s. The API of the AgentZero simulator is described in detail and important architectural decisions that enable analysis and smooth implementation of a variety of algorithms are explained and described. In the context of AOSE, this chapter exemplifies two aspects: agent-based simulation environment and tools, and a variety of development and runtime aids for agent-based systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The complete AgentZero software package is available to DCR researchers and students at our DCR homepage http://www.cs.bgu.ac.il/~dcr. It is routinely used by all members of the DCR research group at BGU. In addition, it is the main tool by which graduate students studying distributed constraints algorithms implement their final projects.
References
Waze. http://www.waze.com.
Meisels A (2011) Distributed search by constrained agents. IDC 2011: 5–9
Silaghi M, Yokoo M (2009) Distributed constraint reasoning. Encyclopedia of Artificial Intelligence, Information Science Reference (2008) [ISBN 978-1-59904-849-9]
Gershman A, Meisels A, Zivan R (2009) Asynchronous forward bounding. J Artif Intell Res 25–46(34)
Yeoh W, Felner A, Koenig S (2010) BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm. J Artif Intell Res 38:85–133
Yokoo M, Durfee HE, Ishida T, Kuwabara K (1998) The distributed constraint satisfaction problem: formalization and algorithms. IEEE Trans Knowl Data Eng 10:673–685
Meisels A (2007) Distributed search by constrained agents: algorithms, performance, communication. Springer, London
Zivan R, Meisels A (2003) Synchronous vs asynchronous search on discsps. Proceedings of the 1st European workshop on multiagent system, EUMAS
Ezzahir R, Bessiere C, Belaissaoui M, Bouyakhf EH (2007) DisChoco: a platform for distributed constraint programming. Retrieved from http://www2.lirmm.fr/coconut/dischoco/
Léauté T, Ottens B, Szymanek R (2012) FRODO: a FRamework for Open/Distributed Optimization. Retrieved from http://frodo2.sourceforge.net/index.php/research
Iványi M, Gulyás L, Bocsi R, Kozma V, Legendi R (2007) The multi-agent simulation suite. In: Emergent Agents and Socialities: Social and Organizational Aspects of Intelligence, pp 57–64
Grubshtein A, Meisels A (2012) Finding a nash equilibrium by asynchronous backtracking. CP 2012: 925–940
Peri O, Meisels A (2013) Synchronizing for performance-DCOP algorithms. In: Proceedings of the 5th International Conference on Agents and Artificial Intelligence (ICAART-2013), pp 5–14, Barcelona, February 2013
Bessiere C, Maestre A, Meseguer P (2001) Distributed dynamic backtracking. In: Proceedings of the 7th international conference on principles and practice of constraint programming. Springer, London, pp 772–
Jay Modi P, Shen W-M, Tambe M, Yokoo M (2005) ADOPT: asynchronous distributed constraints optimization with quality guarantees. Artif Intell 161(2):149–180
Petcu A, Faltings B (2005) A scalable method for multiagent constraint optimization, Proceedings of the 19th international joint conference on artificial intelligence. Edinburgh, Scotland, pp 266–271
Zivan R, Meisels A (2006) Concurrent search for distributed CSPs. Artif Intell 170(4–5):440–461
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Lutati, B., Gontmakher, I., Lando, M., Netzer, A., Meisels, A., Grubshtein, A. (2014). AgentZero: A Framework for Simulating and Evaluating Multi-agent Algorithms. In: Shehory, O., Sturm, A. (eds) Agent-Oriented Software Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54432-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-54432-3_16
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54431-6
Online ISBN: 978-3-642-54432-3
eBook Packages: Computer ScienceComputer Science (R0)