text: 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.
  @cite_5: Dialog systems for mobile robots operating in the real world should enable mixed-initiative dialog style, handle multi-modal information involved in the communication and be relatively independent of the domain knowledge. Most dialog systems developed for mobile robots today, however, are often system-oriented and have limited capabilities. We present an agent-based dialog model that are specially designed for human-robot interaction and provide evidence for its efficiency with our implemented system.
  @cite_1: 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.
  @cite_7: In this paper we present an approach to the task of generating and resolving referring expressions (REs) for conversational mobile robots. It is based on a spatial knowledge base encompassing both robot- and human-centric representations. Existing algorithms for the generation of referring expressions (GRE) try to find a description that uniquely identifies the referent with respect to other entities that are in the current context. Mobile robots, however, act in large-scale space, that is environments that are larger than what can be perceived at a glance, e.g. an office building with different floors, each containing several rooms and objects. One challenge when referring to elsewhere is thus to include enough information so that the interlocutors can extend their context appropriately. We address this challenge with a method for context construction that can be used for both generating and resolving REs - two previously disjoint aspects. Our approach is embedded in a bi-directional framework for natural language processing for robots.
  @cite_3: This article describes the application of computational models of spatial prepositions to visually situated dialog systems. In these dialogs, spatial prepositions are important because people often use them to refer to entities in the visual context of a dialog. We first describe a generic architecture for a visually situated dialog system and highlight the interactions between the spatial cognition module, which provides the interface to the models of prepositional semantics, and the other components in the architecture. Following this, we present two new computational models of topological and projective spatial prepositions. The main novelty within these models is the fact that they account for the contextual effect which other distractor objects in a visual scene can have on the region described by a given preposition. We next present psycholinguistic tests evaluating our approach to distractor interference on prepositional semantics, and illustrate how these models are used for both interpretation and generation of prepositional expressions.
summary: 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 .