dc.contributor.author |
Rens, G
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dc.date.accessioned |
2016-08-19T08:13:13Z |
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dc.date.available |
2016-08-19T08:13:13Z |
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dc.date.issued |
2016-04 |
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dc.identifier.citation |
Rens, G. 2016. On stochastic belief revision and update and their combination. In: Sixteenth International Workshop on Non-Monotonic Reasoning, 22-24 April 2016, Cape Town, South Africa |
en_US |
dc.identifier.uri |
https://arxiv.org/pdf/1604.02126v1.pdf
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dc.identifier.uri |
http://hdl.handle.net/10204/8713
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dc.description |
Sixteenth International Workshop on Non-Monotonic Reasoning, 22-24 April 2016, Cape Town, South Africa. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website |
en_US |
dc.description.abstract |
I propose a framework for an agent to change its probabilistic beliefs when a new piece of propositional information a is observed. Traditionally, belief change occurs by either a revision process or by an update process, depending on whether the agent is informed with a in a static world or, respectively, whether a is a 'signal' from the environment due to an event occurring. Boutilier suggested a unified model of qualitative belief change, which "combines aspects of revision and update, providing a more realistic characterization of belief change." In this paper, I propose a unified model of quantitative belief change, where an agent's beliefs are represented as a probability distribution over possible worlds. As does Boutilier, I take a dynamical systems perspective. The proposed approach is evaluated against several rationality postulated, and some properties of the approach are worked out. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Association for the Advancement of Artificial Intelligence |
en_US |
dc.relation.ispartofseries |
Workflow;17262 |
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dc.subject |
Non-monotonic reasoning |
en_US |
dc.subject |
Artificial intelligence |
en_US |
dc.subject |
Probabilistic beliefs |
en_US |
dc.subject |
Quantitative belief changes |
en_US |
dc.title |
On stochastic belief revision and update and their combination |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Rens, G. (2016). On stochastic belief revision and update and their combination. Association for the Advancement of Artificial Intelligence. http://hdl.handle.net/10204/8713 |
en_ZA |
dc.identifier.chicagocitation |
Rens, G. "On stochastic belief revision and update and their combination." (2016): http://hdl.handle.net/10204/8713 |
en_ZA |
dc.identifier.vancouvercitation |
Rens G, On stochastic belief revision and update and their combination; Association for the Advancement of Artificial Intelligence; 2016. http://hdl.handle.net/10204/8713 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Rens, G
AB - I propose a framework for an agent to change its probabilistic beliefs when a new piece of propositional information a is observed. Traditionally, belief change occurs by either a revision process or by an update process, depending on whether the agent is informed with a in a static world or, respectively, whether a is a 'signal' from the environment due to an event occurring. Boutilier suggested a unified model of qualitative belief change, which "combines aspects of revision and update, providing a more realistic characterization of belief change." In this paper, I propose a unified model of quantitative belief change, where an agent's beliefs are represented as a probability distribution over possible worlds. As does Boutilier, I take a dynamical systems perspective. The proposed approach is evaluated against several rationality postulated, and some properties of the approach are worked out.
DA - 2016-04
DB - ResearchSpace
DP - CSIR
KW - Non-monotonic reasoning
KW - Artificial intelligence
KW - Probabilistic beliefs
KW - Quantitative belief changes
LK - https://researchspace.csir.co.za
PY - 2016
T1 - On stochastic belief revision and update and their combination
TI - On stochastic belief revision and update and their combination
UR - http://hdl.handle.net/10204/8713
ER -
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en_ZA |