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» Grounding Abstractions in Predictive State Representations
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IJCAI
2003
8 years 9 months ago
A Planning Algorithm for Predictive State Representations
We address the problem of optimally controlling stochastic environments that are partially observable. The standard method for tackling such problems is to define and solve a Part...
Masoumeh T. Izadi, Doina Precup
INTETAIN
2005
Springer
9 years 1 months ago
Grounding Emotions in Human-Machine Conversational Systems
In this paper we investigate the role of user emotions in human-machine goal-oriented conversations. There has been a growing interest in predicting emotions from acted and non-act...
Giuseppe Riccardi, Dilek Z. Hakkani-Tür
ISCIS
2009
Springer
9 years 2 months ago
Predicting future object states using learned affordances
Abstract—The notion of affordances was proposed by J.J. Gibson, to refer to the action possibilities offered to the organism by its environment. In a previous formalization, affo...
Emre Ugur, Erol Sahin, Erhan Oztop
AAAI
2011
7 years 8 months ago
Combining Learned Discrete and Continuous Action Models
Action modeling is an important skill for agents that must perform tasks in novel domains. Previous work on action modeling has focused on learning STRIPS operators in discrete, r...
Joseph Z. Xu, John E. Laird
AROBOTS
2011
8 years 3 months ago
Learning GP-BayesFilters via Gaussian process latent variable models
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
Jonathan Ko, Dieter Fox
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