For people to contribute to discourse, they must do more than utter the right sentence at the right time. The basic requirement is that they odd to their common ground in on orderly way. To do this, we argue, they try to establish for each utterance the mutual belief that the addressees hove understood what the speaker meant well enough for current purposes. This is accomplished by the collective actions of the current contributor and his or her partners, and these result in units of conversation called contributions. We present a model of contributions and show how it accounts for o variety of features of everyday conversations.

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We present a model of dialogue for embodied virtual agents that can communicate with multiple (human and virtual) agents in a multi-modal setting, including face-to-face spoken and nonverbal, as well as radio interaction, spanning multiple conversations in support of an extended complex task.

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We use the idea that actions performed in a conversation become part of the common ground as the basis for a model of context that reconciles in a general and systematic fashion the differences between the theories of discourse context used for reference resolution, intention recognition, and dialogue management. We start from the treatment of anaphoric accessibility developed in DRT, and we show first how to obtain a discourse model that, while preserving DRT's basic ideas about referential accessibility, includes information about the occurrence of speech acts and their relations. Next, we show how the different kinds of `structure' that play a role in conversation -- discourse segmentation, turn-taking, and grounding -- can be formulated in terms of information about speech acts, and use this same information as the basis for a model of the interpretation of fragmentary input.

This paper presents a human-machine dialogue model in the field of task-oriented dialogues. The originality of this model resides in the clear separation of dialogue knowledge from task knowledge in order to facilitate for the modeling of dialogue strategies and the maintenance of dialogue coherence. These two aspects are crucial in the field of oral dialogues with a machine considering the current state of the art in speech recognition and understanding techniques. One important theoretical innovation is that our dialogue model is based on a recent linguistic theory of dialogue modeling. The dialogue model considers reaMde situations, as our work was based on a real man-machine corpus of dialogues.

: Although avatars may resemble communicative interface agents, they have for the most part not profited from recent research into autonomous embodied conversational systems. In particular, even though avatars function within conversational environments (for example, chat or games), and even though they often resemble humans (with a head, hands, and a body) they are incapable of representing the kinds of knowledge that humans have about how to use the body during communication. Humans, however, do make extensive use of the visual channel for interaction management where many subtle and even involuntary cues are read from stance, gaze and gesture. We argue that the modeling and animation of such fundamental behavior is crucial for the credibility and effectiveness of the virtual interaction in chat. By treating the avatar as a communicative agent, we propose a method to automate the animation of important communicative behavior, deriving from work in conversation and discourse theory. B...

We describe a statistical approach for modeling agreements and disagreements in conversational interaction. Our approach first identifies adjacency pairs using maximum entropy ranking based on a set of lexical, durational, and structural features that look both forward and backward in the discourse. We then classify utterances as agreement or disagreement using these adjacency pairs and features that represent various pragmatic influences of previous agreement or disagreement on the current utterance. Our approach achieves 86.9 % accuracy, a 4.9 % increase over previous work. 1

Although avatars may resemble animated communicating interface agents, they have for the most part not profited from recent research into autonomous systems. In particular, even though avatars function within conversational environments (for example, chat or games), and even though they often resemble humans (with a head, hands, and a body) they are incapable of representing the kinds of knowledge that humans have about how to use the body during communication. Their appearance does not translate into increased communicative bandwidth. Face-to-face conversation among humans, however, does make extensive use of the visual channel for interaction management where many subtle and even involuntary cues are read from stance, gaze and gesture. We argue that the modeling and animation of such fundamental behavior is crucial for the credibility and effectiveness of the virtual interaction in chat. By treating the avatar as a communicative agent, we propose a method to automate the...

Negotiation is an important part of task-oriented expert-consultation dialogues. This paper presents a plan-based model for understanding cooperative negotiation subdialogues. Our sys-tem infers both the communicative actions that people pursue when speaking and the beliefs underlying these actions. Beliefs, and the strength of these beliefs, are recognized from the surface form of utterances,from discourse acts, and from the explicit and implicit acceptance of previous utterances. Our algorithm for recognizing discourse actions combines linguistic, world, and con-textual knowledge in a unified framework. By combining these different knowledge sources, we are able to recognize complex discourse acts such as expressing doubt, to identify the relationship of utterances to one another, and to model negotiation subdialogues. Since negotiation is an inte-gral part of multiagent activity, our process model addresses an important aspect of cooperative interaction and thus is a step toward an intelligent and robust natural language consultation system.