The coordination of multiple autonomous systems using information theoretic political science voting models

In this paper we introduce an approach to solving a coordination problem involving multiple heterogenous autonomous systems operating in a multi-objective mission domain. The distributed coordination mechanism presented applies an information theoretic political science voting model to the dynamic weighted aggregation method, thereby allowing the decision makers (the autonomous systems), to influence the weights assigned to each mission objective. Results from a simulated instantiation of a multiple objective mission are presented, demonstrating the use of the voting mechanism to coordinate actions undertaken by the autonomous systems. The results show a level of overall mission success comparable to that obtained in an 'ideal' centralised mechanism

[1]  P. Ordeshook The Spatial Analysis of Elections and Committees: Four Decades of Research , 1993 .

[2]  P. Stadler,et al.  The dynamics of locally adaptive parties under spatial voting , 1998 .

[3]  Bärbel M. R. Stadler,et al.  Adaptive platform dynamics in multi-party spatial voting , 1999, Adv. Complex Syst..

[4]  Maja J. Mataric,et al.  Multi-robot target acquisition using multiple objective behavior coordination , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[5]  Yaochu Jin,et al.  Dynamic Weighted Aggregation for evolutionary multi-objective optimization: why does it work and how? , 2001 .

[6]  Ben Grocholsky,et al.  Information-Theoretic Control of Multiple Sensor Platforms , 2002 .

[7]  Alexei Makarenko,et al.  Information-theoretic coordinated control of multiple sensor platforms , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[8]  Anthony Stentz,et al.  Market-Based Multirobot Coordination Using Task Abstraction , 2003, FSR.

[9]  L. Jain,et al.  Evolutionary multiobjective optimization : theoretical advances and applications , 2005 .

[10]  B. Upcroft,et al.  A Comparison of Probabilistic Representations for Decentralised Data Fusion , 2005, 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[11]  Carlos A. Coello Coello,et al.  Recent Trends in Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[12]  Illah R. Nourbakhsh,et al.  Heterogeneous Multirobot Coordination with Spatial and Temporal Constraints , 2005, AAAI.

[13]  J. Fowler,et al.  Policy-Motivated Parties in Dynamic Political Competition , 2007 .

[14]  J. Rosenblatt Utility Theoretic Planning in a Behavior-Based System , 2007 .