Spatio-Temporal Stream Reasoning with Incomplete Spatial Information

Reasoning about time and space is essential for many applications, especially for robots and other autonomous systems that act in the real world and need to reason about it. In this paper we present a pragmatic approach to spatio-temporal stream reasoning integrated in the Robot Operating System through the DyKnow framework. The temporal reasoning is done in Metric Temporal Logic and the spatial reasoning in the Region Connection Calculus RCC-8. Progression is used to evaluate spatio-temporal formulas over incrementally available streams of states. To handle incomplete information the underlying first-order logic is extended to a three-valued logic. When incomplete spatial information is received, the algebraic closure of the known information is computed. Since the algebraic closure might have to be re-computed every time step, we separate the spatial variables into static and dynamic variables and reuse the algebraic closure of the static variables, which reduces the time to compute the full algebraic closure. The end result is an efficient and useful approach to spatio-temporal reasoning over streaming information with incomplete spatial information.

[1]  Patrick Doherty,et al.  DyKnow: An approach to middleware for knowledge processing , 2004, J. Intell. Fuzzy Syst..

[2]  Bernhard Nebel,et al.  Qualitative Spatio-Temporal Reasoning with RCC-8 and Allen's Interval Calculus: Computational Complexity , 2002, ECAI.

[3]  Fredrik Heintz,et al.  DyKnow : A Stream-Based Knowledge Processing Middleware Framework , 2009 .

[4]  Patrick Doherty,et al.  Stream-Based Reasoning Support for Autonomous Systems , 2010, ECAI.

[5]  Patrick Doherty,et al.  A temporal logic-based planning and execution monitoring framework for unmanned aircraft systems , 2009, Autonomous Agents and Multi-Agent Systems.

[6]  Bernhard Nebel,et al.  Efficient Methods for Qualitative Spatial Reasoning , 2001, J. Artif. Intell. Res..

[7]  Fredrik Heintz,et al.  Semantically grounded stream reasoning integrated with ROS , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Frank Wolter,et al.  Spatio-temporal representation and reasoning based on RCC-8 , 2000, International Conference on Principles of Knowledge Representation and Reasoning.

[9]  Fredrik Heintz,et al.  Semantic information integration for stream reasoning , 2012, 2012 15th International Conference on Information Fusion.

[10]  KoymansRon Specifying real-time properties with metric temporal logic , 1990 .

[11]  Fredrik Heintz,et al.  Semantic information integration with transformations for stream reasoning , 2013, Proceedings of the 16th International Conference on Information Fusion.

[12]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[13]  Anthony G. Cohn,et al.  A Spatial Logic based on Regions and Connection , 1992, KR.

[14]  James F. Allen Maintaining knowledge about temporal intervals , 1983, CACM.

[15]  Anthony G. Cohn,et al.  Multi-Dimensional Modal Logic as a Framework for Spatio-Temporal Reasoning , 2002, Applied Intelligence.

[16]  Stephen Cole Kleene,et al.  On notation for ordinal numbers , 1938, Journal of Symbolic Logic.

[17]  Anthony G. Cohn,et al.  Qualitative Spatial Representation and Reasoning , 2008, Handbook of Knowledge Representation.

[18]  Patrick Doherty,et al.  Bridging the sense-reasoning gap: DyKnow - Stream-based middleware for knowledge processing , 2010, Adv. Eng. Informatics.

[19]  Ron Koymans,et al.  Specifying real-time properties with metric temporal logic , 1990, Real-Time Systems.

[20]  S. Wölfl,et al.  GQR – A Fast Reasoner for Binary Qualitative Constraint Calculi , 2008 .