Learning Sets of Sub-Models for Spatio-Temporal Prediction

In this paper we describe a novel technique which implements a spatiotemporal model as a set of sub-models based on first order logic. These sub-models model different, typically independent, parts of the dataset; for example different spatio or temporal contexts. To decide which submodels to use in different situations a context chooser is used. By separating the sub-models from where they are applied allows greater flexibility for the overall model. The sub-models are learnt using an evolutionary technique called Genetic Programming. The method has been applied to spatio-temporal data. This includes learning the rules of snap by observation, learning the rules of a traffic light sequence, and finally predicting a person’s course through a network of CCTV cameras.

• Type: Conference
• Year: 2007
• Where: SGAI
• Authors: Andrew Bennett, Derek R. Magee
• Keywords: Example Different Spatio, SGAI 2007, Sub-models Model, first Order Logic