Modelling Dynamic Causal Interactions with Bayesian Networks: Temporal Noisy Gates

The usual way of applying Bayesian n etworks to the modelling of temporal processes consists in d iscretizing time a nd creating an instance of each random variable for each point in time. This method leads to large a nd complex networks. We present a new approach called Net of Irreversible Events in Discrete Time (NIEDT), for temporal reasoning in domains involving irreversible events. Under this approach, time is discretized, nodes are associated to events, and each value of a node represents the occurrence of an event at a particular instant; t his leads to more simple networks. We a lso d efine several t ypes of Temporal Noisy Gates, which facilitate the ac quisition and representation o f uncertain temporal knowledge.