Inferring (mal) rules from pupils' protocols
Selected and updated papers from the proceedings of the 1982 European conference on Progress in artificial intelligence
Human-computer discourse in the design of a PASCAL tutor
CHI '83 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cooperative responses from a portable natural language data base query system.
Cooperative responses from a portable natural language data base query system.
Generating natural language text in response to questions about database structure
Generating natural language text in response to questions about database structure
Context dependent planning in a machine tutor (artificial intelligence, teaching systems, meno-tutor)
A knowledge representation approach to understanding metaphors
Computational Linguistics
Understanding pragmatically ill-formed input
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
Providing a unified account of definite noun phrases in discourse
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
A general user modelling facility
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Tailoring object descriptions to a user's level of expertise
Computational Linguistics - Special issue on user modeling
An architecture for voice dialog systems based on prolog-style theorem proving
Computational Linguistics
The repair of speech act misunderstandings by abductive inference
Computational Linguistics
A Computational Mechanism for Initiative in Answer Generation
User Modeling and User-Adapted Interaction
User interfaces and help systems: from helplessness to intelligent assistance
Artificial Intelligence Review
Qualified Answers That Reflect User Needs and Preferences
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Collaborative response generation in planning dialogues
Computational Linguistics - Special issue on natural language generation
User-system dialogues and the notion of focus
The Knowledge Engineering Review
Arguing about planning alternatives
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 3
Arguing about planning alternatives
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 3
Tailoring Automatically Generated Hypertext
User Modeling and User-Adapted Interaction
The correction machine: formulating explanations for user misconceptions
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Qualifying Answers According to User Needs and Preferences
Fundamenta Informaticae
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Responses to misconceptions given by human conversational partners very often contain information refuting possible reasoning which may have led to the misconceptions. Surprisingly there is a great deal of regularity in these responses across different domains of discourse. For instance, one reason a user might have given an object a property it does not have is that the user confused the object with another similar object. In correcting such a misconception, a human conversational partner is likely to point out this possible confusion.This work describes a method for generating responses like the one just described by reasoning on a highlighted model of the user to identify possible sources of the error. Through a transcript study a number of response strategies were abstracted. Each strategy was associated with a structural configuration of the user model. For example, the above mentioned strategy of pointing out a similar confused object is associated with a configuration of the user model that indicates the user believes there is an important similar object that has the property involved in the misconception. Upon finding that configuration in the highlighted user model, the system can respond with the associated strategy.Notice that the reasoning must be done on a highlighted user model since the perception of both an object's importance and its similarity with another object change with the perspective being taken on the domain. This paper investigates how domain perspective can be modeled to provide the needed highlighting and introduces a similarity metric that is sensitive to the highlighting provided by the domain perspective. Finally, the paper shows how the highlighting affects misconception responses.