Unifying Time and Uncertainty: The Probabilistic Temporal Network

摘要

Complex real-world systems consist of collections of interactingprocesses/events. These processes change over time in response to both internal and external stimuli as well as to the passage of time. Many domains such as real-time systems diagnosis, (mechanized) story understanding, planning and scheduling, and financial forecasting require the capability to model complex systems under a unified framework to deal with both time and uncertainty. Existing uncertainty representations and existing temporal models already provide rich languages for capturing uncertainty and temporal information, respectively. Unfortunately, these partial solutions have made it extremely difficult to unify time and uncertainty in a way that cleanly and adequately models the problem domains at hand. This difficulty is compounded by the practical necessity for effective and efficient knowledge engineering under such a unified framework. Existing approaches for integrating time and uncertainty exhibit serious compromises in their representations of either time, uncertainty, or both. This thesis investigates a new model, the Probabilistic Temporal Network, that represents temporal information while fully embracing probabilistic semantics. The model allows representation of time constrained causality, of when and if events occur, and of the periodic and recurrent nature of processes.