Qualitative probabilistic networks for planning under uncertainty
MP Wellman - arXiv preprint arXiv:1304.3115, 2013 - arxiv.org
arXiv preprint arXiv:1304.3115, 2013•arxiv.org
Bayesian networks provide a probabilistic semantics for qualitative assertions about
likelihood. A qualitative reasoner based on an algebra over these assertions can derive
further conclusions about the influence of actions. While the conclusions are much weaker
than those computed from complete probability distributions, they are still valuable for
suggesting potential actions, eliminating obviously inferior plans, identifying important
tradeoffs, and explaining probabilistic models.
likelihood. A qualitative reasoner based on an algebra over these assertions can derive
further conclusions about the influence of actions. While the conclusions are much weaker
than those computed from complete probability distributions, they are still valuable for
suggesting potential actions, eliminating obviously inferior plans, identifying important
tradeoffs, and explaining probabilistic models.
Bayesian networks provide a probabilistic semantics for qualitative assertions about likelihood. A qualitative reasoner based on an algebra over these assertions can derive further conclusions about the influence of actions. While the conclusions are much weaker than those computed from complete probability distributions, they are still valuable for suggesting potential actions, eliminating obviously inferior plans, identifying important tradeoffs, and explaining probabilistic models.
arxiv.org