Approximating Value Equivalence in Interactive Dynamic Influence Diagrams Using Behavioral Coverage

Hdl Handle:
http://hdl.handle.net/10149/608516
Title:
Approximating Value Equivalence in Interactive Dynamic Influence Diagrams Using Behavioral Coverage
Authors:
Conroy, R. (Ross); Zeng, Y. (Yifeng); Tang, J. (Jing)
Affiliation:
Teesside University. Digital Futures Institute
Citation:
Conroy, R., Zeng, Y., Tang, J. (2016) 'Approximating Value Equivalence in Interactive Dynamic Influence Diagrams Using Behavioral Coverage' International Joint Conference on Artificial Intelligence (IJCAI-16). New York City. 9-15th July 2016.
Conference:
International Joint Conference on Artificial Intelligence (IJCAI-16). New York City. 9-15th July 2016.
Issue Date:
9-Jul-2016
URI:
http://hdl.handle.net/10149/608516
Additional Links:
http://ijcai-16.org/index.php/welcome/view/home
Abstract:
Interactive dynamic influence diagrams (I-DIDs) provide an explicit way of modeling how a subject agent solves decision making problems in the presence of other agents in a common setting. To optimize its decisions, the subject agent needs to predict the other agents’ behavior, that is generally obtained by solving their candidate models. This becomes extremely difficult since the model space may be rather large, and grows when the other agents act and observe over the time. A recent proposal for solving I-DIDs lies in a concept of value equivalence (VE) that shows potential advances on significantly reducing the model space. In this paper, we establish a principled framework to implement the VE techniques and propose an approximate method to compute VE of candidate models. The development offers ample opportunity of exploiting VE to further improve the scalability of IDID solutions. We theoretically analyze properties of the approximate techniques and show empirical results in multiple problem domains.
Type:
Meetings and Proceedings
Language:
en
Rights:
Author can archive post print, advice recieved from publisher [30/04/2016]

Full metadata record

DC FieldValue Language
dc.contributor.authorConroy, R. (Ross)en
dc.contributor.authorZeng, Y. (Yifeng)en
dc.contributor.authorTang, J. (Jing)en
dc.date.accessioned2016-05-06T15:21:32Zen
dc.date.available2016-05-06T15:21:32Zen
dc.date.issued2016-07-09en
dc.identifier.urihttp://hdl.handle.net/10149/608516en
dc.description.abstractInteractive dynamic influence diagrams (I-DIDs) provide an explicit way of modeling how a subject agent solves decision making problems in the presence of other agents in a common setting. To optimize its decisions, the subject agent needs to predict the other agents’ behavior, that is generally obtained by solving their candidate models. This becomes extremely difficult since the model space may be rather large, and grows when the other agents act and observe over the time. A recent proposal for solving I-DIDs lies in a concept of value equivalence (VE) that shows potential advances on significantly reducing the model space. In this paper, we establish a principled framework to implement the VE techniques and propose an approximate method to compute VE of candidate models. The development offers ample opportunity of exploiting VE to further improve the scalability of IDID solutions. We theoretically analyze properties of the approximate techniques and show empirical results in multiple problem domains.en
dc.language.isoenen
dc.relation.urlhttp://ijcai-16.org/index.php/welcome/view/homeen
dc.rightsAuthor can archive post print, advice recieved from publisher [30/04/2016]en
dc.titleApproximating Value Equivalence in Interactive Dynamic Influence Diagrams Using Behavioral Coverageen
dc.typeMeetings and Proceedingsen
dc.contributor.departmentTeesside University. Digital Futures Instituteen
dc.identifier.conferenceInternational Joint Conference on Artificial Intelligence (IJCAI-16). New York City. 9-15th July 2016.en
or.citation.harvardConroy, R., Zeng, Y., Tang, J. (2016) 'Approximating Value Equivalence in Interactive Dynamic Influence Diagrams Using Behavioral Coverage' International Joint Conference on Artificial Intelligence (IJCAI-16). New York City. 9-15th July 2016.en
dc.eprint.versionPost-printen
dc.embargoNoneen
dc.date.accepted2016-04-05en
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