Sciweavers

NIPS
2003
14 years 27 days ago
Approximate Planning in POMDPs with Macro-Actions
Recent research has demonstrated that useful POMDP solutions do not require consideration of the entire belief space. We extend this idea with the notion of temporal abstraction. ...
Georgios Theocharous, Leslie Pack Kaelbling
AAAI
2004
14 years 28 days ago
Distance Estimates for Planning in the Discrete Belief Space
We present a general framework for studying heuristics for planning in the belief space. Earlier work has focused on giving implementations of heuristics that work well on benchma...
Jussi Rintanen
NIPS
2007
14 years 28 days ago
What makes some POMDP problems easy to approximate?
Point-based algorithms have been surprisingly successful in computing approximately optimal solutions for partially observable Markov decision processes (POMDPs) in high dimension...
David Hsu, Wee Sun Lee, Nan Rong
AIPS
2008
14 years 1 months ago
Exact Dynamic Programming for Decentralized POMDPs with Lossless Policy Compression
High dimensionality of belief space in DEC-POMDPs is one of the major causes that makes the optimal joint policy computation intractable. The belief state for a given agent is a p...
Abdeslam Boularias, Brahim Chaib-draa
ISIPTA
2003
IEEE
14 years 4 months ago
Geometry of Upper Probabilities
In this paper we adopt the geometric approach to the theory of evidence to study the geometric counterparts of the plausibility functions, or upper probabilities. The computation ...
Fabio Cuzzolin
CDC
2008
IEEE
118views Control Systems» more  CDC 2008»
14 years 6 months ago
A density projection approach to dimension reduction for continuous-state POMDPs
Abstract— Research on numerical solution methods for partially observable Markov decision processes (POMDPs) has primarily focused on discrete-state models, and these algorithms ...
Enlu Zhou, Michael C. Fu, Steven I. Marcus