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A Geometric Dynamic Temporal Reasoning Method with Tags for Cognitive Systems
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Author(s): Rui Xu (Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration, Ministry of Industry and Information Technology & School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China), Zhaoyu Li (Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration, Ministry of Industry and Information Technology & School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China), Pingyuan Cui (Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration, Ministry of Industry and Information Technology & School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China), Shengying Zhu (Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration, Ministry of Industry and Information Technology & School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China)and Ai Gao (Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration, Ministry of Industry and Information Technology & School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China)
Copyright: 2016
Volume: 8
Issue: 4
Pages: 17
Source title:
International Journal of Software Science and Computational Intelligence (IJSSCI)
Editor(s)-in-Chief: Brij Gupta (Asia University, Taichung City, Taiwan)and Andrew W.H. Ip (University of Saskatchewan, Canada)
DOI: 10.4018/IJSSCI.2016100103
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Abstract
Temporal reasoning is one of the cognitive capabilities humans involve in communicating with others and everything appears related because of temporal reference. Therefore, in this paper a geometric dynamic temporal reasoning algorithm is proposed to solve the temporal reasoning problem, especially in autonomous planning and scheduling. This method is based on the representation of actions in a two dimensional coordination system. The main advantage of this method over others is that it uses tags to mark new constraints added into the constraint network, which leads the algorithm to deal with pending constraints rather than all constraints. This characteristic makes the algorithm suitable for temporal reasoning, where variables and constraints are always added dynamically. This algorithm can be used not only in intelligent planning, but also computational intelligence, real-time systems, and etc. The results show the efficiency of our algorithm from four cases simulating the planning and scheduling process.
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