Survey of the state of the art in human language technology
Survey of the state of the art in human language technology
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
Journal of Artificial Intelligence Research
Learning to sportscast: a test of grounded language acquisition
Proceedings of the 25th international conference on Machine learning
Training a multilingual sportscaster: using perceptual context to learn language
Journal of Artificial Intelligence Research
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Text generation requires a planning module to select an object of discourse and its properties. This is specially hard in descriptive games, where a computer agent tries to describe some aspects of a game world. We propose to formalize this problem as a Markov Decision Process, in which an optimal message policy can be defined and learned through simulation. Furthermore, we propose back-off policies as a novel and effective technique to fight state dimensionality explosion in this framework.