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Extending DTGolog to deal with POMDPs

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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


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