Adaptive dialogue strategy selection through imprecise probabilistic query answering

  • Authors:

  • Ian O'Neill;Anbu Yue;Weiru Liu;Phil Hanna

  • Affiliations:

  • School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK;School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK;School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK;School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK

  • Venue:
  • ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

In a human-computer dialogue system, the dialogue strategy can range from very restrictive to highly flexible. Each specific dialogue style has its pros and cons and a dialogue system needs to select the most appropriate style for a given user. During the course of interaction, the dialogue style can change based on a user's response and the system observation of the user. This allows a dialogue system to understand a user better and provide a more suitable way of communication. Since measures of the quality of the user's interaction with the system can be incomplete and uncertain, frameworks for reasoning with uncertain and incomplete information can help the system make better decisions when it chooses a dialogue strategy. In this paper, we investigate how to select a dialogue strategy based on aggregating the factors detected during the interaction with the user. For this purpose, we use probabilistic logic programming (PLP) to model probabilistic knowledge about how these factors will affect the degree of freedom of a dialogue. When a dialogue system needs to know which strategy is more suitable, an appropriate query can be executed against the PLP and a probabilistic solution with a degree of satisfaction is returned. The degree of satisfaction reveals how much the system can trust the probability attached to the solution.