This paper proposes a continuous-time machine learning model that learns the chronological relationships and the intervals between events, stores and organises the learnt knowledge in different levels of abstraction in a network, and makes predictions about future events. The acquired knowledge is represented in a categorisation-like manner, in which events are categorised into categories of different levels. This inherently facilitates the categorisation of static items and leads to a general approach to both spatial and temporal perception. The paper presents the approach and a demonstration showing how it works.
CITATION STYLE
Nan, J., & Costello, F. (2010). An artificial intelligence model that combines spatial and temporal perception. In Artificial General Intelligence - Proceedings of the Third Conference on Artificial General Intelligence, AGI 2010 (pp. 109–114). Atlantis Press. https://doi.org/10.2991/agi.2010.38
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