Thumbnail
Access Restriction
Open

Author Kurien, James ♦ Nayak, P. Pandurang
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Likely State ♦ Observ-able Markov Decision ♦ Autonomous Controller ♦ Sum-marizing Segment ♦ Likely Trajectory ♦ Physical Process ♦ Re-visit Past Assumption ♦ Process Evolution ♦ Large State Space ♦ Relevant Class ♦ Consistency-based Trajectory Tracking ♦ Approximate Belief State ♦ Space-craft Control ♦ Belief State ♦ Partial Be-lief State
Description Given a model of a physical process and a sequence of com-mands and observations received over time, the task of an autonomous controller is to determine the likely states of the process and the actions required to move the process to a desired configuration. We introduce a representation and algorithms for incrementally generating approximate belief states for a restricted but relevant class of partially observ-able Markov decision processes with very large state spaces. The algorithm incrementally generates, rather than revises, an approximate belief state at any point by abstracting and sum-marizing segments of the likely trajectories of the process. This enables applications to efficiently maintain a partial be-lief state when it remains consistent with observations and re-visit past assumptions about the process’s evolution when the belief state is ruled out. The system presented has been im-plemented and results on examples from the domain of space-craft control are presented.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Institution Proceedings of the National Conference on Artificial Intelligence. Menlo Park, CA: AAAI