Achieving Robust Human-Computer Communication

This paper describes a computational approach to robust human-computer interaction. The approach relies on an explicit, declarative representation of the content and structure of the interaction that a computer system builds over the course of the interaction. In this paper, we will show how this representation allows the system to recognize and repair misunderstandings between the human and the computer. We demonstrate the utility of the representations by showing how they facilitate the repair process. 1 Introduction In dialogs between people or between people and machines, understanding is an uncertain process. If the goals or beliefs of two discourse 1 participants differ, one of them might interpret an event in the dialog in a way that she believes is complete and correct, although her interpretation is not the one that the other one had intended. When this happens, we as analysts (or observers) would say that a misunderstanding has occurred. The participants themselves might..

Uniform knowledge representation for language processing

We describe the natural language processing and knowledge representation components of B2, a collaborative system that allows medical students to practice their decision-making skills by considering a number of medical cases that differ from each other in a controlled manner. The underlying decision-support model of B2 uses a Bayesian network that captures the results of prior clinical studies of abdominal pain. B2 generates story-problems based on this model and supports natural language queries about the conclusions of the model and the reasoning behind them. B2 benefits from having a single knowledge representation and reasoning component that acts as a blackboard for intertask communication and cooperation. All knowledge is represented using a propositional semantic network formalism, thereby providing a uniform representation to all components. The natural language component is composed of a generalized augmented transition network parser/grammar and a discourse analyzer for managing the natural language interactions. The knowlege representation component supports the natural language component by providing a uniform representation of the content and structure of the interaction, at the parser, discourse, and domain levels. This uniform representation allows distinct tasks, such as dialog management, domain-specific reasoning, and meta-reasoning about the Bayesian network, to all use the same information source, without requiring mediation. This is important because there are queries, such as Why?, whose interpretation and response requires information from each of these tasks. By contrast, traditional approaches treat each subtask as a “black-box ” with respect to other task components, and have a separate knowledge representation language for each. As a result, they have had much more difficulty providing useful responses

The Need to Address Plan Misinference during Dialogues and Why Abduction Might Help

ion rename by copy oe rename file Figure 1: An example planning theory Speech act understanding [ Hinkelman, 1990 ] treats different features in the input, such as the mood of a sentence or the presence of a particular lexical item, as manifestations of different speech acts. For example, "please" is a manifestation of a request. The system matches features against the input to determine a set of candidates, which are then filtered on the basis of the consistency of their implicatures (similar to Allen's inference rules) with a model of prior beliefs. [ Traum and Hinkelman, 1992 ] extend this work, generalizing the notion of speech act to conversation acts. These acts include the taking and releasing of turns, and the initiating, clarifying, or acknowledging of an utterance. Unlike speech acts, conversation acts require some positive evidence by the listener before they are accepted as understood. To provide an additional filter on candidate interpretations, the acts have been organiz..

Mixed depth representations for dialog processing

We describe our work on developing a general purpose tutoring system that will allow students to practice their decision-making skills in a number of domains. The tutoring system, B2, supports mixed-initiative natural language interaction. The natural language processing and knowledge representation components are also general purpose—which leads to a tradeoff between the limitations of superficial processing and syntactic representations and the difficulty of deeper methods and conceptual representations. Our solution is to use a mixed-depth representation, one that encodes syntactic and conceptual information in the same structure. As a result, we can use the same representation framework to produce a detailed representation of requests (which tend to be well-specified) and to produce a partial representation of questions (which tend to require more inference about the context). Moreover, the representations use the same knowledge representation framework that is used to reason about discourse processing and domain information—so that the system can reason with (and about) the utterances, if necessary

A Declarative Model of Dialog

Abstract The general goal of our work is to investigate computational models of dialog that can support effective interaction between people and computer systems. We are particularly interested in the use of dialog for training and education. To support effective communication, dialog systems must facilitate users' understanding by incrementally presenting only the most relevant information, by evaluating users' understanding, and by adapting the interaction to address communication problems as they arise. Our model provides a specification and representation of the linguistic, intentional, and social information that influence how people understand and respond in an ongoing dialog and an architecture for combining this information. We represent knowledge uniformly in a single, declarative, logical language where the interpretation and performance of communicative acts in dialog occurs as a result of reasoning

The Repair of Speech Act Misunderstandings by Abductive Inference

this paper, we have concentrated on the repair of mis-understanding. Our colleagues Heeman and Edmonds have looked at the repair of non-understanding. The difference between the two situations is that in the former, the agent derives exactly one interpretation of an utterance and hence is initially unaware of any problem; in the latter, the agent derives either more than one interpretation, with no way to choose between them, or no interpretation at all, and so the problem is immediately apparent. Heeman and Edmonds looked in particular at cases in which a referring expression uttered by one conversant was not understood by the other (Heeman and Hirst 1995; Edmonds 1994; Hirst et. al. 1994). Clark and his colleagues (Clark and Wilkes-Gibbs 1986; Clark 1993) have shown that in such situations, conversants will collaborate on repairing the problem by, in effect, negotiating a reconstruction or elaboration of the referring expression. Heeman and Edmonds model this with a plan recognition and generation system that can recognize faulty plans and try to repair them. Thus (as in our own model) two copies of the system can converse with each other, negotiating referents of referring expressions that are not understood by trying to recognize the referring plans of the other, repairing them where necessary, and presenting the new referring plan to the other for approval

Uniform Knowledge Representation for Language Processing in the B2 System

We describe the natural language processing and knowledge representation components of B2, a collaborative system that allows medical students to practice their decision-making skills by considering a number of medical cases that differ from each other in a controlled manner. The underlying decision-support model of B2 uses a Bayesian network that captures the results of prior clinical studies of abdominal pain. B2 generates story-problems based on this model and supports natural language queries about the conclusions of the model and the reasoning behind them. B2 benefits from having a single knowledge representation and reasoning component that acts as a blackboard for intertask communication and cooperation. All knowledge is represented using a propositional semantic network formalism, thereby providing a uniform representation to all components. The natural language component is composed of a generalized augmented transition network parser/grammar and a discourse analyzer for manag..