Our goal in this paper is to establish a means for a dialogue platform to be able to cope with open domains considering the possible interaction between the embodied agent and humans. To this end we present an algorithm capable of processing natural language utterances and validate them against knowledge structures of an intelligent agent's mind. Our algorithm leverages dialogue techniques in order to solve ambiguities and acquire knowledge about unknown entities.
On the other hand, Bohus and Horvitz @cite_6 proposed an open-world platform which attempts to allow a dialogue system to support multi-dynamic user interaction along with heavily situated context information acquired, mostly from vision features, to adapt the dialogue domain. We show that open domain dialogue adaptation can also rely on linguistic information rather than there approach, which focused mainly on visual information.
@cite_6
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Copyright © 2014 ISCA. An important property of open domain spoken dialogue systems is their ability to deal with a set of new, previously unseen, concepts introduced in the conversation. The dialogue manager must then quickly learn how to talk about the new concepts using its knowledge of the existing concepts. It has previously been shown that a single new concept could be accommodated by mapping the kernel function of a Gaussian process to incorporate an additional concept into the domain of a statistical dialogue manager. Here we present an incremental scheme which enables the domain of a dialogue manager to be repeatedly extended by recursively specifying priors in Gaussian processes. We show that it is possible to effectively double the number of concepts understood by a system providing restaurant information using only 1000 adaptation dialogues with real users.