User Models in Dialog Systems - Tapa blanda

Sinopsis

I. A Survey of User Modeling.- 1 User Models in Dialog Systems.- 1. Introduction.- 2. Constructing User Models.- 3. Representing User Models.- 4. Exploiting User Models.- 5. Open Questions and Future Research in User Modeling.- 6. References.- 2 Stereotypes and User Modeling.- 1. Introduction.- 2. The Definition of a Stereotype.- 3. The Space of User Models.- 4. Stereotypes and User Modeling.- 5. Stereotypes and Contradiction Resolution.- 6. Adaptation of Stereotype Systems.- 7. Parallel Models of System Knowledge and of Users.- 8. Summary.- 9. References.- 3 A Taxonomy of Beliefs and Goals for User Models in Dialog Systems.- 1. Introduction.- 2. Beliefs, Goals and Plans.- 3. Basic Beliefs.- 4. Basic Goals.- 5. Beliefs with Respect to Other Agents' Beliefs and Goals.- 6. Goals with Respect to Other Agents' Beliefs and Goals.- 7. A Classification of Existing User Models.- 8. Discussion.- 9. References.- II. Building User Models.- 4 KNOME: Modeling What the User Knows in UC.- 1. Introduction.- 2. Internal Representation of Users.- 3. Deducing the User's Level of Expertise.- 4. Modeling UC's Knowledge.- 5. Exploiting KNOME.- 6. Conclusion.- 7. References.- 5 Detecting and Responding to Plan-Oriented Misconceptions.- 1. Introduction.- 2. An Explanation-Based Approach.- 3. Representing User and Advisor Beliefs.- 4. Explanation-Based Misconception Recognition and Response.- 5. A Taxonomy of Potential Explanations.- 6. A Detailed Process Model.- 7. Accessing Advisor Planning Knowledge.- 8. Related Work.- 9. Implementation Details.- 10. Limitations and Future Work.- 11. Conclusions.- 12. References.- 6 Plan Recognition and Its Use in Understanding Dialog.- 1. Introduction.- 2. Plan Recognition in Dialog Systems.- 3. Inferring and Modeling the Task-Related Plan.- 4. Application of User Models.- 5. Improving Plan Recognition.- 6. Constructing and Exploiting Other Components of a User Model.- 7. Conclusions and Current Research.- 8. References.- 7 Learning the User's Language: A Step Towards Automated Creation of User Models.- 1. Introduction: Adaptable Interfaces.- 2. Foundations: Least-Deviant-First Parsing and MULTIPAR.- 3. CHAMP: Design for an Adaptive Parser.- 4. Hidden Operator Experiments with Professional Secretaries.- 5. Concluding Remarks.- 6. References.- III. Exploiting User Models.- 8 The Use of Explicit User Models in a Generation System for Tailoring Answers to the User's Level of Expertise.- 1. Introduction.- 2. Identifying What Needs to Be in the User Model.- 3. Two Descriptions Strategies Found in Texts: Constituency Schema and Process Trace.- 4. Mixing the Strategies.- 5. TAILOR.- 6. Further Work and Related Issues.- 7. Conclusions.- 8. References.- 9 Highlighting a User Model to Respond to Misconceptions.- 1. Introduction.- 2. Knowledge Available.- 3. Related Work on Correcting Misconceptions.- 4. Misclassifications.- 5. Misattributions.- 6. A Rule for Choosing a Strategy.- 7. Highlighting and Object Similarity.- 8. Object Perspective.- 9. Using Perspective to Set f.- 10. Modeling a Domain with Perspectives.- 11. Choosing the Active Perspective.- 12. Perspective's Influence on Responses.- 13. Conclusions.- 14. References.- 10 But What Will the Listener Think? Belief Ascription and Image Maintenance in Dialog.- 1. Introduction.- 2. Situation 1: Generating an Informative Monolog.- 3. Situation 2: Positive or Negative Bias.- 4. Situation 3: Anticipating the Pragmatic Interpretation of Comments.- 5. Situation 4: Discrepancy Between Actual and Projected Biases.- 6. Situation 5: Responding to Specific Questions.- 7. Situation 6: Establishing a Desired Projected Bias.- 8. Situation 7: Discrepancy Between Actual and Projected Ascriptions.- 9. Situation 8: Uncertainty in the Listener About the Speaker's Ascriptions.- 10. Situation 9: Uncertainty in the Speaker About Her Projected Ascriptions.- 11. Conclusions.- 12. References.- 11 Incorporating User Models into Expert Systems for Educational Diagnosis.- 1. Introduction

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