Propagation of Temporally Indexed Values in Multiple Contexts

Abstract

Modelling multiple views of a dynamic system requires both an efficient technique for dealing with multiple contexts and a mechanism for explicit reasoning about time. Assumption-based truth maintenance systems simultaneously handle multiple, possibly contradictory views, which may be characterized by different sets of assumptions about correctness of components, particular operating modes, etc.. This is mainly achieved by recording inference steps in the form of explicit dependencies and propagating sets of assumptions along these dependencies. Constraint systems explicitly reasoning about time use value-interval pairs, so-called episodes, as parameter values.

We argue that straightforward combinations of assumption-based and episode-based systems may raise serious complexity problems. In order to overcome these problems we use the basic ideas of assumption-based systems, i.e. caching of inference steps and label propagation, and apply them to both assumption sets and temporal information.