Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Intelligent interfaces: user models and planners
CHI '86 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Introduction to systems application architecture
IBM Systems Journal
Shaping user input: a strategy for natural language dialogue design
Interacting with Computers
An iterative design methodology for user-friendly natural language office information applications
ACM Transactions on Information Systems (TOIS)
Online help systems: a conspectus
Communications of the ACM
Talking to UNIX in English: an overview of UC
Communications of the ACM
An English language question answering system for a large relational database
Communications of the ACM
Understanding Computers and Cognition: A New Foundation for Design
Understanding Computers and Cognition: A New Foundation for Design
The effects of limited grammar on interactive natural language
CHI '83 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Scribe: a document specification language and its compiler
Scribe: a document specification language and its compiler
EUFID: a friendly and flexible front-end for data management systems
ACL '79 Proceedings of the 17th annual meeting on Association for Computational Linguistics
ConWIZ: a tool supporting contextual Wizard of Oz simulation
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
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The provision of intelligent user assistance has been an ongoing problem in designing computer interfaces. Interactive computing environments must support expert as well as novice users when providing advice for error correction and answers to questions directed to a system. To address these issues, we have investigated the application of fairly well-understood artificial intelligence techniques in novel ways to provide intelligent help. This paper describes the design methodology used to build REASON (Real-time Explanation And SuggestiON), an intelligent user-assistant prototype for a windowed, multitasking environment. REASON's central component is an inference engine that solves problems arising from a user's activity. When the user makes one of several different kinds of errors, the inference engine offers dynamically generated suggestions about what the user might have intended. The user can also query REASON using natural language. In addition to providing suggestions of corrected input or answers to questions, REASON can provide two complementary types of explanations of these responses, derived from the inferences that led to them.