Putting the problem solver back in the Driver's seat: Contextual control of the ATMS

Abstract

The Atms is a powerful tool for automated problem solvers and has been used to support several model-based reasoning tasks such as prediction and diagnosis. It provides an efficient mechanism for maintaining consistent sets of beliefs and recording the assumptions underlying them. This enables the problem solver to switch rapidly between contexts and compare them. Such capabilities are central to diagnostic systems, and are also valuable to design and planning systems. Applications to larger problems have been hampered, however, by the problem solver's inability to maintain control over the Atms.

We present a new approach, implemented in a system called Coco, which allows the problem solver to maintain tight control over the contexts explored by the Atms. Coco provides means for expressing local and global control over both normal and nogood consumers. Local control is achieved by attaching guards to individual consumers. These guards express control, rather than logical, knowledge and consist of sets of environments. Global control is achieved by specifying a set of interesting environments. Consumers are fired only when its antecedents are true in some interesting environment. We also successfully apply the same technique to limit label propagation in the Atms. This ensures that the Atms respects the problem solver's wishes and only makes derivations in interesting contexts.

We demonstrate the both the dramatic increases in efficiency which are made possible by these techniques, as well as their tremendous expressive power, in four examples.

This research was supported by Bundesminister fuer Forschung und Technologie, project TEX-B, Itw 8506 E4.

This research was in part supported by a SIEMENS doctoral fellowship, and has in part taken place in the Qualitative Reasoning Group at the Artificial Intelligence Laboratory, The University of Texas at Austin. Research of the Qualitative Reasoning Group is supported in part by NSF grants IRI-8602665, IRI-8905494, and IRI-8904454, by NASA grants NAG 2-507 and NAG 9-200, and by the Texas Advanced Research Program under grant no. 003658175.