Evolving optimal inspectable strategies for spoken dialogue systems
Dave Toney, Johanna D. Moore, Oliver Lemon · 2006
We report on a novel approach to generating strategies for spoken dialogue systems. We present a series of experiments that illustrate how an evolutionary reinforcement learning algorithm can produce strategies that are both optimal and easily inspectable by human developers. Our experimental strategies achieve a mean performance of 98.9% with respect to a predefined evaluation metric. Our approach also produces a dramatic reduction in strategy size when compared with conventional reinforcement learning techniques (87% in one experiment). We conclude that this algorithm can be used to evolve optimal inspectable dialogue strategies.