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

By F. Lefèvre, Milica Gasic, Filip Jurcícek, Simon Keizer, François Mairesse, Blaise Thomson, Kai Yu, Susannah Young. 2009

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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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition