Planning Responses From High-Level Goals: Adopting the Respondent\u27s Perspective Cooperative Response Generation

Within the natural-language research community it has long been acknowledged that the conventions and pragmatics of natural-language communication often oblige dialogue systems to consider and address the underlying purposes of queries in their responses rather than answering them literally and without further comment or elaboration. Such systems cannot simply translate their users\u27 requests into transactions on database or expert systems, but must apply many more complex reasoning mechanisms to the task of selecting responses that are both appropriate and useful. This idea has given rise to a broadly-defined program of research in cooperative response generation (CRG). Research in CRG carried on over more than a decade has yielded a substantial body of literature. Analysis of that literature, however, shows that investigators have focused primarily on modeling manifestations of cooperative behavior without directly considering the nature and motivations of the behavior itself. But if we want to develop natural language dialogue systems that are truly to function as cooperative respondents instead of serving only as models of particular kinds of cooperative responses, a different approach is required. I identify two opposing perspectives on the process of cooperative response generation: the questioner-based and the respondent-based perspectives. I argue that past research efforts have largely been questioner-based, and that this view has led to the development of theories that are incompatible and cannot be integrated. I propose the respondent-based view as an alternative, and provide evidence that taking such a perspective might allow several interesting but otherwise poorly-understood aspects of cooperative response behavior to be modeled. The final portion of the dissertation explores the computational implications of a respondent-based perspective. I outline the architecture of a Cooperative Response Planning System, a dialogue system that raises, reasons about, and attempts to satisfy high-level cooperative goals in its responses. This architecture constitutes a first approximation to a theory of how a system might reason from the beliefs it derives from a questioner\u27s utterances to choose a cooperative response. The processing of two sample responses in this framework is described in detail to illustrate the architecture\u27s capabilities

The Architecture of a Cooperative Respondent

If natural language question-answering (NLQA) systems are to be truly effective and useful, they must respond to queries cooperatively, recognizing and accommodating in their replies a questioner\u27s goals, plans, and needs. Transcripts of natural dialogue demonstrate that cooperative responses typically combine several communicative acts: a question may be answered, a misconception identified, an alternative course of action described and justified. This project concerns the design of cooperative response generation systems, NLQA systems that are able to provide integrated cooperative responses. Two questions must be answered before a cooperative NLQA system can be built. First, what are the reasoning mechanisms that underlie cooperative response generation? In partial reply, I argue that plan evaluation is an important step in the process of selecting a cooperative response, and describe several tests that may usefully be applied to inferred plans. The second question is this: what is an appropriate architecture for cooperative NLQA (CNLQA) systems? I propose a four-level decomposition of the cooperative response generation process and then present a suitable CNLQA system architecture based on the blackboard model of problem solving

Embedded Training for Complex Information Systems

One approach to providing affordable operator training in the workplace is to augment applications with intelligent embedded training systems (ETS). Intelligent embedded training is highly interactive: trainees practice realistic problem-solving tasks on the prime application with guidance and feedback from the training system. This article makes three contributions to the theory and technology of ETS design. First, we describe a framework based on Norman’s “stages of user activity” model for defining the instructional objectives of an ETS. Second, we demonstrate a non-invasive approach to instrumenting software applications, thereby enabling them to collaborate with an ETS. Third, we describe a method for interpreting observed user behavior during problem solving, and using that information to provide task-oriented hints on demand

Planning responses from high-level goals: Adopting the respondent\u27s perspective in cooperative response generation

Within the natural-language research community it has long been acknowledged that the conventions and pragmatics of natural-language communication often oblige dialogue systems to consider and address the underlying purposes of queries in their responses rather than answering them literally and without further comment or elaboration. Such systems cannot simply translate their users\u27 requests into transactions on database or expert systems, but must apply many more complex reasoning mechanisms to the task of selecting responses that are both appropriate and useful. This idea has given rise to a broadly-defined program of research in cooperative response generation (CRG). Research in CRG carried on over more than a decade has yielded a substantial body of literature. Analysis of that literature, however, shows that investigators have focused primarily on modeling manifestations of cooperative behavior without directly considering the nature and motivations of the behavior itself. But if we want to develop natural-language dialogue systems that are truly to function as cooperative respondents instead of serving only as models of particular kinds of cooperative responses, a different approach is required. I identify two opposing perspectives on the process of cooperative response generation: the questioner-based and the respondent-based perspectives. I argue that past research efforts have largely been questioner-based, and that this view has led to the development of theories that are incompatible and cannot be integrated. I propose the respondent-based view as an alternative, and provide evidence that taking such a perspective might allow several interesting but otherwise poorly-understood aspects of cooperative response behavior to be modeled. The final portion of the dissertation explores the computational implications of a respondent-based perspective. I outline the architecture of a Cooperative Response Planning System, a dialogue system that raises, reasons about, and attempts to satisfy high-level cooperative goals in its responses. This architecture constitutes a first approximation to a theory of how a system might reason from the beliefs it derives from a questioner\u27s utterances to choose a cooperative response. The processing of two sample responses in this framework is described in detail to illustrate the architecture\u27s capabilities

GIA: An Agent-Based Architecture for Intelligent Tutoring Systems

this paper focuses on GIA's high-level design

Elements of a Computational Model of Cooperative Response Generation*

If natural language question-answering (NLQA) systems are to be truly effective and useful, they must respond to queries cooperatively, recognizing and accommodating in their replies a questioner's goals, plans, and needs. This paper concerns the design of cooperative response generation (CRG) systems, NLQA systems that are able to produce integrated cooperative responses. We propose two characteristics of a computational model of cooperative response generation. First, we argue that CRG systems should be able to explicitly reason about and choose among the different response options available to them in a given situation. Second, we suggest that some choices of response content motivate others--that through a process called reflection, respondents detect the need to explain, justify, clarify or otherwise augment information they have already decided to convey.

Elements of a Computational Model of Cooperative Response Generation*

If natural language question-answering (NLQA) systems are to be truly effective and useful, they must respond to queries cooperatively, recognizing and accommodating in their replies a questioner's goals, plans, and needs. This paper concerns the design of cooperative response generation (CRG) systems, NLQA systems that are able to produce integrated cooperative responses. We propose two characteristics of a computational model of cooperative response generation. First, we argue that CRG systems should be able to explicitly reason about and choose among the different response options available to them in a given situation. Second, we suggest that some choices of response content motivate others--that through a process called reflection, respondents detect the need to explain, justify, clarify or otherwise augment information they have already decided to convey.