Autonomous Robots manuscript No. (will be inserted by the editor) Top–Down vs Bottom–up Methodologies in Multi–Agent System Design

Abstract Traditionally, two alternative design approaches have been available to engineers: top-down and bottom-up. In the top-down approach, the design process starts with specifying the global system state and assuming that each component has global knowledge of the system, as in a centralized approach. The solution is then decentralized by replacing global knowledge with communication. In the bottom-up approach, on the other hand, the design starts with specifying requirements and capabilities of individual components, and the global behavior is said to emerge out of interactions among constituent components and between components and the environment. In this paper we present a comparative study of both approaches with particular emphasis on applications to multi-agent system engineering and robotics. We outline the generic characteristics of both approaches from the MAS perspective, and identify three elements that we believe should serve as criteria for how and when to apply either of the approaches. We demonstrate our analysis on a specific example of load balancing problem in robotics. We also show that under certain assumptions on the communication and the external environment, both bottom-up and top-down methodologies produce very similar solutions.

[1]  Kristina Lerman,et al.  Macroscopic analysis of adaptive task allocation in robots , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[2]  Jean Oh,et al.  Electric Elves: Applying Agent Technology to Support Human Organizations , 2001, IAAI.

[3]  Paul Levi,et al.  Generation of Desired Emergent Behavior in Swarm of Micro-Robots , 2004, ECAI.

[4]  Maja J. Mataric,et al.  From local to global behavior in intelligent self-assembly , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[5]  Clare Dixon,et al.  On Formal Specification of Emergent Behaviours in Swarm Robotic Systems , 2005 .

[6]  Luca Maria Gambardella,et al.  Collaboration Through the Exploitation of Local Interactions in Autonomous Collective Robotics: The Stick Pulling Experiment , 2001, Auton. Robots.

[7]  Chris Melhuish,et al.  Stigmergy, Self-Organization, and Sorting in Collective Robotics , 1999, Artificial Life.

[8]  Karl Sims,et al.  Evolving 3D Morphology and Behavior by Competition , 1994, Artificial Life.

[9]  Niklaus Wirth,et al.  Program development by stepwise refinement , 1971, CACM.

[10]  Arnold Kamis,et al.  Reconciling top-down and bottom-up design approaches in RMM , 1998, DATB.

[11]  Clare Dixon,et al.  On the Formal Specification of Emergent Behaviours of Swarm Robotics Systems , 2005 .

[12]  Hong Zhang,et al.  The use of perceptual cues in multi-robot box-pushing , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[13]  Kristina Lerman,et al.  A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems , 2004, Swarm Robotics.

[14]  George Cybenko,et al.  Performance Analysis of Mobile Agents for Filtering Data Streams on Wireless Networks , 2000, MSWIM '00.

[15]  Deborah Estrin,et al.  Directed diffusion: a scalable and robust communication paradigm for sensor networks , 2000, MobiCom '00.

[16]  Gregory McFarland The benefits of bottom-up design , 1986, SOEN.

[17]  George Cybenko,et al.  Decentralized algorithms for sensor registration , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[18]  John N. Tsitsiklis,et al.  Gradient Convergence in Gradient methods with Errors , 1999, SIAM J. Optim..

[19]  D. Bertsekas Gradient convergence in gradient methods , 1997 .

[20]  Ronald C. Arkin,et al.  Cooperative multiagent robotic systems , 1998 .

[21]  Markus Pizka,et al.  A brief top-down and bottom-up philosophy on software evolution , 2004, Proceedings. 7th International Workshop on Principles of Software Evolution, 2004..

[22]  Alan F. T. Winfield,et al.  A methodology for provably stable behaviour-based intelligent control , 2006, Robotics Auton. Syst..

[23]  Kristina Lerman,et al.  Mathematical Model of Foraging in a Group of Robots: Effect of Interference , 2002, Auton. Robots.

[24]  A. Ijspeert,et al.  A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems , 2002, Artificial Life.

[25]  Alcherio Martinoli,et al.  Modeling Swarm Robotic Systems: a Case Study in Collaborative Distributed Manipulation , 2004, Int. J. Robotics Res..

[26]  George Cybenko,et al.  Decentralized control for coordinated flow of multi-agent systems , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[27]  George Cybenko,et al.  Agent-Based Systems Engineering and Intelligent Vehicles and Road Systems , 2001 .

[28]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[29]  George Cybenko,et al.  Agent-Based Systems Engineering , 2005 .

[30]  Luca Maria Gambardella,et al.  A Probabilistic Model for Understanding and Comparing Collective Aggregation Mechansims , 1999, ECAL.

[31]  Eric Klavins,et al.  A Grammatical Approach to Cooperative Control , 2007 .

[32]  Kristina Lerman,et al.  Analysis of Dynamic Task Allocation in Multi-Robot Systems , 2006, Int. J. Robotics Res..