Learning interactions among objects through spatio-temporal reasoning

In this study, we present how interactions among objects are learned from a given set of actions without any intermediate information about the states of objects. We have used The Incredible Machine game as a suitable test bed to analyze these types of interactions. When a knowledge base about relations among objects is provided, the interactions to devise new plans are learned to a desired extent. Moreover, using spatial information of objects or temporal information of actions makes it feasible to learn the effects of objects on each other. Integrating spatial and temporal data in a spatio-temporal learning approach gives closer results to that of the knowledge-based approach. This is promising because gathering spatio-temporal information does not require great amount of knowledge.