Automatic synthesis of cooperative multi-agent systems

In this paper, the cooperative tasking and automatic controller synthesis problem for multi-agent systems are investigated. It is assumed that the global specification is given as regular languages while the multi-agent system is modeled as a concurrent discrete-event system defined by a collection of finite automata that interact with each other. A top-down and iterative design approach is pursued and the basic idea is to divide-and-conquer. First, the global specification is decomposed into subtasks with respect to each individual agents event sets. Then, a local supervisor is automatically synthesized for each agent respectively using learning based approaches. Thirdly, we use the assume-guarantee reasoning to check whether the collective behaviors of the local controlled agents can satisfy the global specification. Once the checking fails, a counterexample is generated and used to modify the task decomposition and the automatic synthesis process repeats. It is proved that the iterative process converges and a correctness of the design process is guaranteed. Finally, the design process is illustrated through a robot cooperative tasking example.

[1]  Yushan Chen,et al.  Automatic Deployment of Robotic Teams , 2011, IEEE Robotics & Automation Magazine.

[2]  Hai Lin,et al.  Guaranteed global performance through local coordinations , 2011, Autom..

[3]  Howard Barringer,et al.  Learning to divide and conquer: applying the L* algorithm to automate assume-guarantee reasoning , 2008, Formal Methods Syst. Des..

[4]  Hadas Kress-Gazit,et al.  Where's Waldo? Sensor-Based Temporal Logic Motion Planning , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[5]  P. Ramadge,et al.  Supervisory control of a class of discrete event processes , 1987 .

[6]  Hai Lin,et al.  Assume-guarantee cooperative satisfaction of multi-agent systems , 2014, 2014 American Control Conference.

[7]  Jana Kosecka,et al.  Control of Discrete Event Systems , 1992 .

[8]  Makoto Yokoo,et al.  Coordination Planning: Applying Control Synthesis Methods for a Class of Distributed Agents , 2009, IEEE Transactions on Control Systems Technology.

[9]  AngluinDana Learning regular sets from queries and counterexamples , 1987 .

[10]  Hervé Marchand,et al.  Supervisory control of concurrent discrete event systems , 2004 .

[11]  Hai Lin,et al.  Fault-tolerant cooperative tasking for multi-agent systems , 2011, Int. J. Control.

[12]  Shengbing Jiang,et al.  Decentralized control of discrete event systems with specializations to local control and concurrent systems , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[13]  H. Marchand,et al.  Modular supervisory control of a class of concurrent discrete event systems , 2004 .

[14]  Vijay K. Garg,et al.  Modeling and Control of Logical Discrete Event Systems , 1994 .

[15]  Rajeev Alur,et al.  Symbolic Compositional Verification by Learning Assumptions , 2005, CAV.

[16]  Howie Choset,et al.  Principles of Robot Motion: Theory, Algorithms, and Implementation ERRATA!!!! 1 , 2007 .

[17]  Calin Belta,et al.  Automatic Deployment of Distributed Teams of Robots From Temporal Logic Motion Specifications , 2010, IEEE Transactions on Robotics.

[18]  Hai Lin,et al.  Decentralized supervisory control of discrete event systems with unknown plants: A learning-based synthesis approach , 2014, 11th IEEE International Conference on Control & Automation (ICCA).

[19]  Christos G. Cassandras,et al.  Introduction to Discrete Event Systems , 1999, The Kluwer International Series on Discrete Event Dynamic Systems.

[20]  Hadas Kress-Gazit,et al.  Temporal-Logic-Based Reactive Mission and Motion Planning , 2009, IEEE Transactions on Robotics.

[21]  Jin Dai,et al.  A Learning-based Synthesis Approach to Decentralized Supervisory Control of Discrete Event Systems with Unknown Plants , 2014 .

[22]  Dana Angluin,et al.  Learning Regular Sets from Queries and Counterexamples , 1987, Inf. Comput..