Publication Type

Conference Proceeding Article

Publication Date

11-2006

Abstract

Temporal constraint networks are embedded in many planning and scheduling problems. In dynamic problems, a fundamental challenge is to decide whether such a network can be executed as uncertainty is revealed over time. Very little work in this domain has been done in the probabilistic context. In this paper, we propose a Temporal Constraint Network (TCN) model where durations of uncertain activities are represented by random variables. We wish to know whether such a network is robust controllable, i.e. can be executed dynamically within a given failure probability, and if so, how one might find a feasible schedule as the uncertainty variables are revealed dynamically. We present a computationally tractable and efficient approach to solve this problem. Experimentally, we study how the failure probability is affected by various network properties of the underlying TCN, and the relationship of failure rates between robust and weak controllability.

Discipline

Artificial Intelligence and Robotics | Business | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics

Publication

IEEE Conference Tools with Artificial Intelligence (ICTAI) (European Conference on AI (ECAI) 2006 Workshop on Modeling and Solving Problems with Constraints)

First Page

288

Last Page

296

ISSN

1082-3409

Identifier

10.1109/ICTAI.2006.100

Publisher

IEEE

Additional URL

http://dx.doi.org/10.1109/ICTAI.2006.100