Temporal logic for process specification and recognition

Acting intelligently in dynamic environments involves anticipating surrounding processes, for example to foresee a dangerous situation by recognizing a process and inferring respective safety zones. Process recognition is thus key to mastering dynamic environments including surveillance tasks. In this paper, we are concerned with a logic-based approach to process specification, recognition, and interpretation. We demonstrate that linear temporal logic (LTL) provides the formal grounds on which processes can be specified. Recognition can then be approached as a model checking problem. The key feature of this logic-based approach is its seamless integration with logic inference which can sensibly supplement the incomplete observations of the robot. Furthermore, logic allows us to query for process occurrences in a flexible manner and it does not rely on training data. We present a case study with a robotic observer in a warehouse logistics scenario. Our experimental evaluation demonstrates that LTL provides an adequate basis for process recognition.

[1]  Henry A. Kautz,et al.  Learning and inferring transportation routines , 2004, Artif. Intell..

[2]  Martin Gebser,et al.  Conflict-Driven Answer Set Solving , 2007, IJCAI.

[3]  Qiang Yang,et al.  Quantifying information and contradiction in propositional logic through test actions , 2009, IJCAI.

[4]  Ufuk Topcu,et al.  Receding horizon control for temporal logic specifications , 2010, HSCC '10.

[5]  Stefan Edelkamp,et al.  Action Planning for Directed Model Checking of Petri Nets , 2006, MoChArt@CONCUR/SPIN.

[6]  Vladimir Lifschitz,et al.  Answer set programming and plan generation , 2002, Artif. Intell..

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

[8]  Calin Belta,et al.  Temporal Logic Motion Planning and Control With Probabilistic Satisfaction Guarantees , 2012, IEEE Transactions on Robotics.

[9]  Ufuk Topcu,et al.  Correct, Reactive, High-Level Robot Control , 2011, IEEE Robotics & Automation Magazine.

[10]  Fahiem Bacchus,et al.  Using temporal logics to express search control knowledge for planning , 2000, Artif. Intell..

[11]  Bud Mishra,et al.  Discrete event models+temporal logic=supervisory controller: automatic synthesis of locomotion controllers , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[12]  Nils J. Nilsson,et al.  Shakey the Robot , 1984 .

[13]  Fausto Giunchiglia,et al.  Planning via Model Checking: A Decision Procedure for AR , 1997, ECP.

[14]  Ufuk Topcu,et al.  Receding horizon temporal logic planning for dynamical systems , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[15]  Wolfram Burgard,et al.  Learning Motion Patterns of People for Compliant Robot Motion , 2005, Int. J. Robotics Res..

[16]  Philippe Schnoebelen,et al.  The Complexity of Temporal Logic Model Checking , 2002, Advances in Modal Logic.

[17]  Maria-Florina Balcan,et al.  A discriminative model for semi-supervised learning , 2010, J. ACM.

[18]  Fulvio Mastrogiovanni,et al.  Context assessment strategies for Ubiquitous Robots , 2009, 2009 IEEE International Conference on Robotics and Automation.

[19]  G. Brewka.  Principles of Knowledge Representation , 1996 .

[20]  Calin Belta,et al.  LTL Control in Uncertain Environments with Probabilistic Satisfaction Guarantees , 2011, ArXiv.

[21]  Christian Freksa,et al.  On Process Recognition by Logical Inference , 2011, ECMR.

[22]  Klaus Schneider,et al.  Program Sketching via CTL* Model Checking , 2011, SPIN.

[23]  Heribert Vollmer,et al.  The tractability of model checking for LTL: The good, the bad, and the ugly fragments , 2008, TOCL.

[24]  Martin Gebser,et al.  Advances in gringo Series 3 , 2011, LPNMR.

[25]  Andrea Bianco,et al.  Model Checking of Probabalistic and Nondeterministic Systems , 1995, FSTTCS.

[26]  Anthony G. Cohn,et al.  Interleaved Inductive-Abductive Reasoning for Learning Complex Event Models , 2011, ILP.

[27]  Calin Belta,et al.  Optimal path planning under temporal logic constraints , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  V. Lifschitz,et al.  Foundations of Logic Programming , 1997 .

[29]  Michael ten Hompel,et al.  Management of Warehouse Systems , 2007 .

[30]  Amir Pnueli,et al.  The temporal logic of programs , 1977, 18th Annual Symposium on Foundations of Computer Science (sfcs 1977).

[31]  Udo Frese.  An O(log n) algorithm for simultaneous localization and mapping of mobile robots in indoor environments , 2004 .

[32]  Amir Pnueli,et al.  Checking that finite state concurrent programs satisfy their linear specification , 1985, POPL.

[33]  John-Jules Ch. Meyer,et al.  Intelligent agents and common sense reasoning , 2007, Handbook of Modal Logic.

[34]  C. Belta,et al.  LTL Planning for Groups of Robots , 2006, 2006 IEEE International Conference on Networking, Sensing and Control.