Automated Verification of Resource Requirements in Multi-Agent Systems Using Abstraction

We describe a framework for the automated verification of multi-agent systems which do distributed problem solving, e.g., query answering. Each reasoner uses facts, messages and Horn clause rules to derive new information. We show how to verify correctness of distributed problem solving under resource constraints, such as the time required to answer queries and the number of messages exchanged by the agents. The framework allows the use of abstract specifications consisting of Linear Time Temporal Logic (LTL) formulas to specify some of the agents in the system. We illustrate the use of the framework on a simple example.

[1]  Rafael H. Bordini,et al.  Jason and the Golden Fleece of Agent-Oriented Programming , 2005, Multi-Agent Programming.

[2]  Thomas A. Henzinger,et al.  MOCHA: Modularity in Model Checking , 1998, CAV.

[3]  Jeng-Rung Chen,et al.  Predicting the response time of OPS5-style production systems , 1995, Proceedings the 11th Conference on Artificial Intelligence for Applications.

[4]  Abdur Rakib,et al.  Verifying Time and Communication Costs of Rule-Based Reasoners , 2008, MoChArt.

[5]  Jürgen Dix,et al.  Multi-Agent Programming: Languages, Tools and Applications , 2009 .

[6]  Brian Logan,et al.  Modal Logics for Communicating Rule-Based Agents , 2006, ECAI.

[7]  Michael Wooldridge,et al.  State-space reduction techniques in agent verification , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[8]  John J. McCarthy,et al.  The Rule Engine for the Java Platform , 2008 .

[9]  M. Clavel,et al.  Principles of Maude , 1996, WRLA.

[10]  Yehoshua Sagiv,et al.  On Termination of Datalog Programs , 1989, DOOD.

[11]  Stefan Edelkamp,et al.  Model Checking and Artificial Intelligence, 4th Workshop, MoChArt IV, Riva del Garda, Italy, August 29, 2006, Revised Selected and Invited Papers , 2007, MoChArt.

[12]  Patrick Cousot,et al.  Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints , 1977, POPL.

[13]  Gerhard Lakemeyer,et al.  Towards an Integration of Golog and Planning , 2007, IJCAI.

[14]  Spyros G. Tzafestas Knowledge-Based System Diagnosis, Supervision, and Control , 1988 .

[15]  François Goasdoué,et al.  Distributed Reasoning in a Peer-to-Peer Setting , 2004, ECAI.

[16]  Yehoshua Sagiv,et al.  On Termination of Datalog Programs*: Extended Abstract , 1990 .

[17]  Gerard J. Holzmann,et al.  On-the-fly model checking , 1996, CSUR.

[18]  Edmund M. Clarke,et al.  Model checking and abstraction , 1994, TOPL.

[19]  María Alpuente,et al.  Defining Datalog in Rewriting Logic , 2009, LOPSTR.

[20]  Albert Mo Kim Cheng,et al.  A graph-based approach for timing analysis and refinement of OPS5 knowledge-based systems , 2004, IEEE Transactions on Knowledge and Data Engineering.