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Communication Dans Un Congrès Année : 2009

k-Nearest Neighbor Monte-Carlo Control Algorithm for POMDP-based Dialogue Systems

Résumé

In real-world applications, modelling dialogue as a POMDP requires the use of a summary space for the dialogue state representation to ensure tractability. Sub-optimal estimation of the value function governing the selection of system responses can then be obtained using a grid-based approach on the belief space. In this work, the Monte-Carlo control technique is extended so as to reduce training over-fitting and to improve robustness to semantic noise in the user input. This technique uses a database of belief vector prototypes to choose the optimal system action. A locally weighted k-nearest neighbor scheme is introduced to smooth the decision process by interpolating the value function, resulting in higher user simulation performance.
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Dates et versions

hal-01318161 , version 1 (19-05-2016)

Identifiants

  • HAL Id : hal-01318161 , version 1

Citer

Fabrice Lefèvre, Milica Gašić, Filip Jurcicek, S Keizer, F Mairesse, et al.. k-Nearest Neighbor Monte-Carlo Control Algorithm for POMDP-based Dialogue Systems. SIGDIAL '09: the 10th Annual Meeting of the Special Interest Group in Discourse and Dialogue, Sep 2009, Londres, United Kingdom. ⟨hal-01318161⟩

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