Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
An analytic solution to discrete Bayesian reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
Computer Speech and Language
Finding approximate POMDP solutions through belief compression
Journal of Artificial Intelligence Research
Solving POMDPs with continuous or large discrete observation spaces
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
Computer Speech and Language
Scaling POMDPs for Spoken Dialog Management
IEEE Transactions on Audio, Speech, and Language Processing
A statistical spoken dialogue system using complex user goals and value directed compression
EACL '12 Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics
Probabilistic dialogue models with prior domain knowledge
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
| Hi-index | 0.00 |
We point out several problems in scaling-up statistical approaches to spoken dialogue systems to enable them to deal with complex but natural user goals, such as disjunctive and negated goals and preferences. In particular, we explore restrictions imposed by current independence assumptions in POMDP dialogue models. This position paper proposes the use of Automatic Belief Compression methods to remedy these problems.