Retrieving the Most Probable Solution in a Temporal Interval Algebra Network

Abstract:- In this paper, we propose a probability-based approach to retrieve the most probable solution in a temporal Interval Algebra (IA) network. In our approach, all probable solutions are abstracted as a Markov Chain. Based on this Markov Chain, we utilize Chapman-Kolmogorov functional equation to compute the most probable solution. Furthermore, in order to achieve easy and friendly operations for IA network’s researchers, we attempt to adopt constraint logic programming to implement software which is able to support temporal an IA network. The implementation of the software is based on finite domain non-binary CSPs. In addition, we briefly provide an application of retrieving the most probable solution to show the functions of software. Through experiments, we conclude that the implementation can satisfy the desired goal