Temporal Reasoning and Bayesian Networks

Abstract

This work examines important issues in probabilistic temporal representation and reasoning using Bayesian networks (also known as belief networks). The representation proposed here utilizes temporal (or dynamic) probabilities to represent facts, events, and the effects of events. The architecture of a belief network may change with time to indicate a different causal context. Probability variations with time capture temporal properties such as persistence and causation. They also capture event interaction, and when the interaction between events follows known models such as the competing risks model, the additive model, or the dominating event model, the net effect of many interacting events on the temporal probabilities can be calculated efficiently. This representation of reasoning also exploits the notion of temporal degeneration of relevance due to information obsolescence to improve the efficiency.

Methods for model-based reasoning within agent-based Ambient Intelligence applications

“…Finally, Bayesian network-based solutions (introduced by [44]) offer the possibility to deal with uncertainty and temporality [55] when reasoning about (causal) models. However, our approaches deal with temporality in a human-like manner, in contrast with the methods described in [55].…”

“…However, our approaches deal with temporality in a human-like manner, in contrast with the methods described in [55]. In addition, Bayesian network solutions in general do not offer a selection mechanism like our focus mechanism.…”

Temporal uncertainty reasoning networks for evidence fusion with applications to object detection and tracking

“…The approach presented in this paper extends the notion of transfer functions [18], using absolute time of observations as well as time intervals for conditional probability updating. In our model, henceforth referred to as Temporal Uncertainty Reasoning Network (TURN), each node denotes a hypothesis (optionally annotated with a timestamp to indicate a specific temporal instantiation of the network), and each edge represents a function that describes a conditional relationship characterized by its potential variation with time.…”

Collaborative Software for Information Fusion

“…Extensions, such as Dynamic Bayesian Networks (DBNs) [41] attempt to handle time-changing problems by instantiating and connecting sequences of entire Bayes networks, each representing a possible situation at a snapshot in time [45], by adding additional nodes to the graphical model representing each temporal interval of interest as a random variable [46], or by using temporal (or dynamic) probabilities to represent the changing state of the observed environment [47]. Such approaches are intractable beyond very simple settings and temporal quantifications.…”