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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The recent surge of intelligent personal assistants motivates spoken language understanding of dialogue systems. However, the domain constraint along with the inflexible intent schema remains a big issue. This paper focuses on the task of intent expansion, which helps remove the domain limit and make an intent schema flexible. A con-volutional deep structured semantic model (CDSSM) is applied to jointly learn the representations for human intents and associated utterances. Then it can flexibly generate new intent embeddings without the need of training samples and model-retraining, which bridges the semantic relation between seen and unseen intents and further performs more robust results. Experiments show that CDSSM is capable of performing zero-shot learning effectively, e.g. generating embeddings of previously unseen intents, and therefore expand to new intents without re-training, and outperforms other semantic embeddings. The discussion and analysis of experiments provide a future direction for reducing human effort about annotating data and removing the domain constraint in spoken dialogue systems. In situated human-robot dialogue, although humans and robots are co-present in a shared environment, they have significantly mismatched capabilities in perceiving the shared environment. Their representations of the shared world are misaligned. In order for humans and robots to communicate with each other successfully using language, it is important for them to mediate such differences and to establish common ground. To address this issue, this paper describes a dialogue system that aims to mediate a shared perceptual basis during human-robot dialogue. In particular, we present an empirical study that examines the role of the robot’s collaborative effort and the performance of natural language processing modules in dialogue grounding. Our empirical results indicate that in situated human-robot dialogue, a low collaborative effort from the robot may lead its human partner to believe a common ground is established. However, such beliefs may not reflect true mutual understanding. To support truly grounded dialogues, the robot should make an extra effort by making its partner aware of its internal representation of the shared world. Embodied conversational agents are computer-generated cartoonlike characters that demonstrate many of the same properties as humans in face-to-face conversation, including the ability to produce and respond to verbal and nonverbal communication. They constitute a type of (a) multimodal interface where the modalities are those natural to human conversation: speech, facial displays, hand gestures, and body stance; (b) software agent, insofar as they represent the computer in an interaction with a human or represent their human users in a computational environment (as avatars, for example); and (c) dialogue system where both verbal and nonverbal devices advance and regulate the dialogue between the user and the computer. With an embodied conversational agent, the visual dimension of interacting with an animated character on a screen plays an intrinsic role. Not just pretty pictures, the graphics display visual features of conversation in the same way that the face and hands do in face-to-face conversation among humans.This book describes research in all aspects of the design, implementation, and evaluation of embodied conversational agents as well as details of specific working systems. Many of the chapters are written by multidisciplinary teams of psychologists, linguists, computer scientists, artists, and researchers in interface design. The authors include Elisabeth Andre, Norm Badler, Gene Ball, Justine Cassell, Elizabeth Churchill, James Lester, Dominic Massaro, Cliff Nass, Sharon Oviatt, Isabella Poggi, Jeff Rickel, and Greg Sanders. This thesis puts forward a computational framework that can be used by embodied artificial agents (and in particular autonomous robots) for ontogenetic development. The research investigates methods, endowed with which, an embodied agent can develop control structures for increasingly complex and better adapted behaviour, explicitly and incrementally from its history of interaction with its environment. The temporal horizon of an agent is extended so that past experience can be self-organized into a developing structure that can be used to anticipate the future and act appropriately in environments where state information is incomplete, such as a social environment. A formal definition of sensorimotor experience is given, and Crutchfield’s information metric is used as the basis for comparison of experiences. Information metrics are demonstrated to be able to characterize and identify time-extended behaviour. A definition of a metric space of experiences is followed by the introduction of an architecture that combines this with environmental reinforcement as the basis for a system for robot ontogeny. The architecture is demonstrated and tested in various robotic and simulation experiments. This thesis also introduces the early communication game “Peekaboo” as a tool for the study of human-robot interaction and development. The interaction history architecture is then used by two different robots to develop the capability to engage in the peekaboo game.