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.
As a consequence of the process of embodiment, there is a rising need of adequateness to the context the embodied agent is currently in and the ones it has experienced and related to which it has associated memories. @cite_5 evaluated the importance of situatedness in dialogue when the system is using embodied agents. In order to support situated dialogue, work on generation and resolution of referring expressions has been accomplished based on vision, in which the dialogue system depends on input from a vision subsystem to allow a reference resolver, along with spatial reasoning, to match linguistic references to world entities @cite_3 . Further experiments by @cite_7 used a bidirectional layer model for resolution and generation of referring expressions for entities that might not be in the current context and therefore must be accounted for as such when producing and interpreting dialogue in a human-robot interaction. Lison and Kruijff @cite_1 proposed a solution for dialogue systems to cope with open domains through priming speech recognition based on the concept of salience, from both linguistic and visual points of view. This concept was also a main focus target of Kelleher and Costello @cite_3 .
@cite_5 @cite_1 @cite_7 @cite_3
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A robot participating in natural dialogue with a human interlocutor may need to discuss, reason about, or initiate actions concerning dialogue-referenced entities. To do so, the robot must first identify or create new representations for those entities, a capability known as reference resolution. We previously presented algorithms for resolving references occurring in definite noun phrases. In this paper we present GH-POWER : an algorithm for resolving references occurring in a wider array of linguistic forms, by making novel extensions to the Givenness Hierarchy, and evaluate GH-POWER on natural task-based human-human and human-robot dialogues. In this paper we present a plug and play dialogue system for smart environments. The environment description and its state are stored on a domain ontology. This ontology is formed by entities that represent real world contextual information and abstract concepts. This information is complemented with linguistic parts that allow to automatically create a spoken interface for the environment. The spoken interface is based on multiple dialogues, related to every ontology entity with linguistic information. Firstly, the dialogue system creates appropriate grammars for the dialogues. Secondly, it creates the dialogue parts, employing a tree structure. Grammars support the recognition process and the dialogue tree supports the interpretation and generation processes. The system is being tested with a prototype formed by a living room. Users may interact with and modify the physical state of this living room environment by means of the spoken dialogue interface. We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following different findings in spatial cognition, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system. The paper presents an implemented model for priming speech recognition, using contextual information about salient entities. The underlying hypothesis is that, in human-robot interaction, speech recognition performance can be improved by exploiting knowledge about the immediate physical situation and the dialogue history. To this end, visual salience (objects perceived in the physical scene) and linguistic salience (objects, events already mentioned in the dialogue) are integrated into a single cross-modal salience model. The model is dynamically updated as the environment changes. It is used to establish expectations about which words are most likely to be heard in the given context. The update is realised by continuously adapting the word-class probabilities specified in a statistical language model. The paper discusses the motivations behind the approach, and presents the implementation as part of a cognitive architecture for mobile robots. Evaluation results on a test suite show a statistically significant improvement of salience-driven priming speech recognition (WER) over a commercial baseline system.