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

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.

Common Platform Enumeration: Naming Specification Version 2.3

(NIST) promotes the U.S. economy and public welfare by providing technical leadership for the nation’s measurement and standards infrastructure. ITL develops tests, test methods, reference data, proof of concept implementations, and technical analysis to advance the development and productive use of information technology. ITL’s responsibilities include the development of technical, physical, administrative, and management standards and guidelines for the cost-effective security and privacy of sensitive unclassified information in Federal computer systems. This Interagency Report discusses ITL’s research, guidance, and outreach efforts in computer security and its collaborative activities with industry, government, and academic organizations. National Institute of Standards and Technology Interagency Report 7695 49 pages (Aug. 2011) Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose