dc.contributor.author |
Rens, G
|
|
dc.contributor.author |
Ferrein, A
|
|
dc.contributor.author |
van der Poel, E
|
|
dc.date.accessioned |
2009-02-04T10:25:33Z |
|
dc.date.available |
2009-02-04T10:25:33Z |
|
dc.date.issued |
2008-11 |
|
dc.identifier.citation |
Rens, G, Ferrein, A and van der Poel, E. 2008. Extending DTGolog to deal with POMDPs. 19th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA 2008), Cape Town, South Africa, 27-28 November 2008, pp 49-54 |
en |
dc.identifier.isbn |
9780799223507 |
|
dc.identifier.uri |
http://hdl.handle.net/10204/2972
|
|
dc.description |
19th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA 2008) |
en |
dc.description.abstract |
For sophisticated robots, it may be best to accept and reason with noisy sensor data, instead of assuming complete observation and then dealing with the effects of making the assumption. We shall model uncertainties with a formalism called the partially observable Markov decision process (POMDP). The planner developed in this paper will be implemented in Golog; a theoretically and practically 'proven' agent programming language. There exists a working implementation of our POMDP-planner |
en |
dc.language.iso |
en |
en |
dc.publisher |
PRASA |
en |
dc.subject |
Partially observable Markov decision process |
en |
dc.subject |
POMDP |
en |
dc.subject |
Golog |
en |
dc.title |
Extending DTGolog to deal with POMDPs |
en |
dc.type |
Conference Presentation |
en |
dc.identifier.apacitation |
Rens, G., Ferrein, A., & van der Poel, E. (2008). Extending DTGolog to deal with POMDPs. PRASA. http://hdl.handle.net/10204/2972 |
en_ZA |
dc.identifier.chicagocitation |
Rens, G, A Ferrein, and E van der Poel. "Extending DTGolog to deal with POMDPs." (2008): http://hdl.handle.net/10204/2972 |
en_ZA |
dc.identifier.vancouvercitation |
Rens G, Ferrein A, van der Poel E, Extending DTGolog to deal with POMDPs; PRASA; 2008. http://hdl.handle.net/10204/2972 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Rens, G
AU - Ferrein, A
AU - van der Poel, E
AB - For sophisticated robots, it may be best to accept and reason with noisy sensor data, instead of assuming complete observation and then dealing with the effects of making the assumption. We shall model uncertainties with a formalism called the partially observable Markov decision process (POMDP). The planner developed in this paper will be implemented in Golog; a theoretically and practically 'proven' agent programming language. There exists a working implementation of our POMDP-planner
DA - 2008-11
DB - ResearchSpace
DP - CSIR
KW - Partially observable Markov decision process
KW - POMDP
KW - Golog
LK - https://researchspace.csir.co.za
PY - 2008
SM - 9780799223507
T1 - Extending DTGolog to deal with POMDPs
TI - Extending DTGolog to deal with POMDPs
UR - http://hdl.handle.net/10204/2972
ER -
|
en_ZA |