Showing papers by "Konrad Scheffler published in 2002"
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24 Mar 2002TL;DR: Q-learning with eligibility traces was applied to obtain policies for a telephone-based cinema information system, and the policies outperformed handcrafted policies that operated in the same restricted state space, and gave performance similar to the original design that had been through several iterations of manual refinement.
Abstract: This paper describes a method for automatic design of human-computer dialogue strategies by means of reinforcement learning, using a dialogue simulation tool to model the user behaviour and system recognition performance. To the authors' knowledge this is the first application of a detailed simulation tool to this problem. The simulation tool is trained on a corpus of real user data. Compared to direct state transition modelling, it has the major advantage that different state space representations can be studied without collecting more training data.
We applied Q-learning with eligibility traces to obtain policies for a telephone-based cinema information system, comparing the effect of different state space representations and evaluation functions. The policies outperformed handcrafted policies that operated in the same restricted state space, and gave performance similar to the original design that had been through several iterations of manual refinement.
153 citations
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01 Jan 2002TL;DR: In this article, a method for automatic design of human-computer dialogue strategies by means of reinforcement learning, using a dialogue simulation tool to model the user behaviour and system recognition performance, is described.
Abstract: This paper describes a method for automatic design of human-computer dialogue strategies by means of reinforcement learning, using a dialogue simulation tool to model the user behaviour and system recognition performance. To the authors' knowledge this is the first application of a detailed simulation tool to this problem. The simulation tool is trained on a corpus of real user data. Compared to direct state transition modelling, it has the major advantage that different state space representations can be studied without collecting more training data.
We applied Q-learning with eligibility traces to obtain policies for a telephone-based cinema information system, comparing the effect of different state space representations and evaluation functions. The policies outperformed handcrafted policies that operated in the same restricted state space, and gave performance similar to the original design that had been through several iterations of manual refinement.
14 citations