dc.contributor |
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics |
dc.contributor |
Alquézar Mancho, René |
dc.contributor.author |
Georgaraki, Chryso |
dc.date |
2012-01-13 |
dc.identifier.uri |
http://hdl.handle.net/2099.1/14193 |
dc.language.iso |
eng |
dc.publisher |
Universitat Politècnica de Catalunya |
dc.rights |
Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.rights |
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject |
Àrees temàtiques de la UPC::Informàtica::Robòtica |
dc.subject |
Mobile robots |
dc.subject |
Artificial intelligence |
dc.subject |
MOMDP (Mixed Observability MDPS) |
dc.subject |
SARSOP (Successive Approximations of the Reachable Space under Optimal Policies)) |
dc.subject |
POMDP (Partially Observable Markovian Decision Processes) |
dc.subject |
Intel·ligència artificial |
dc.subject |
Robots mòbils |
dc.title |
A POMDP approach to the hide and seek game |
dc.type |
info:eu-repo/semantics/masterThesis |
dc.description.abstract |
Projecte final de Màster Oficial fet en col.laboració amb Institut de Robàtica i Informàtica Industrial |
dc.description.abstract |
Partially observable Markov decision processes (POMDPs) provide an elegant
mathematical framework for modeling complex decision and planning problems
in uncertain and dynamic environments. They have been successfully applied to
various robotic tasks. The modeling advantage of POMDPs, however, comes at
a price exact methods for solving them are computationally very expensive and
thus applicable in practice only to simple problems. A major challenge is to scale
up POMDP algorithms for more complex robotic systems. Our goal is to make
an autonomous mobile robot to learn and play the children's game hide and seek
with opponent a human agent. Motion planning in uncertain and dynamic envi-
ronments is an essential capability for autonomous robots. We focus on an e cient
point-based POMDP algorithm, SARSOP, that exploits the notion of optimally
reachable belief spaces to improve computational efficiency. Moreover we explore
the mixed observability MDPs (MOMDPs) model, a special class of POMDPs.
Robotic systems often have mixed observability: even when a robots state is not
fully observable, some components of the state may still be fully observable. Ex-
ploiting this, we use the factored model, proposed in the literature, to represent
separately the fully and partially observable components of a robots state and derive a compact lower dimensional representation of its belief space. We then use
this factored representation in conjunction with the point-based algorithm to com-
pute approximate POMDP solutions. Experiments show that on our problem, the
new algorithm is many times faster than a leading point-based POMDP algorithm
without important losses in the quality of the solution |