Planning for operational control systems with predictable exogenous events

Ronen I. Brafman, Carmel Domshlak, Yagil Engel, Zohar Feldman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Various operational control systems (OCS) are naturally modeled as Markov Decision Processes. OCS often enjoy access to predictions of future events that have substantial impact on their operations. For example, reliable forecasts of extreme weather conditions are widely available, and such events can affect typical request patterns for customer response management systems, the flight and service time of airplanes, or the supply and demand patterns for electricity. The space of exogenous events impacting OCS can be very large, prohibiting their modeling within the MDP; moreover, for many of these exogenous events there is no useful predictive, probabilistic model. Realtime predictions, however, possibly with a short lead-time, are often available. In this work we motivate a model which combines offline MDP infinite horizon planning with realtime adjustments given specific predictions of future exogenous events, and suggest a framework in which such predictions are captured and trigger real-time planning problems. We propose a number of variants of existing MDP solution algorithms, adapted to this context, and evaluate them empirically.

Original languageEnglish
Title of host publicationAAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference
Pages940-945
Number of pages6
StatePublished - 2 Nov 2011
Event25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11 - San Francisco, CA, United States
Duration: 7 Aug 201111 Aug 2011

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Conference

Conference25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11
Country/TerritoryUnited States
CitySan Francisco, CA
Period7/08/1111/08/11

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