Uncertainty in Artificial Intelligence
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First-Order Open-Universe POMDPs
Siddharth Srivastava, Stuart Russell, Paul Ruan, Xiang Cheng
Open-universe probability models, representable by a variety of probabilistic programming lan- guages (PPLs), handle uncertainty over the ex- istence and identity of objects‚??forms of uncer- tainty occurring in many real-world situations. We examine the problem of extending a declar- ative PPL to define decision problems (specifi- cally, POMDPs) and identify non-trivial repre- sentational issues in describing an agent‚??s ca- pability for observation and action‚??issues that were avoided in previous work only by making strong and restrictive assumptions. We present semantic definitions that lead to POMDP speci- fications provably consistent with the sensor and actuator capabilities of the agent. We also de- scribe a variant of point-based value iteration for solving open-universe POMDPs. Thus, we han- dle cases‚??such as seeing a new object and pick- ing it up‚??that could not previously be repre- sented or solved.
Pages: 742-751
PS Link:
PDF Link: /papers/14/p742-srivastava.pdf
AUTHOR = "Siddharth Srivastava and Stuart Russell and Paul Ruan and Xiang Cheng",
TITLE = "First-Order Open-Universe POMDPs",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "742--751"

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