Analyzing the structure of argumentative discourse
Computational Linguistics
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian model of plan recognition
Artificial Intelligence
Procedural help in Andes: generating hints using a Bayesian network student model
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A computational architecture for conversation
UM '99 Proceedings of the seventh international conference on User modeling
ARGUER: using argument schemas for argument detection and rebuttal in dialogs
UM '99 Proceedings of the seventh international conference on User modeling
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Statistical source channel models for natural language understanding
Statistical source channel models for natural language understanding
A process model for recognizing communicative acts and modeling negotiation subdialogues
Computational Linguistics
Arguing about planning alternatives
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 3
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Using Dialogue Features to Predict Trouble During Collaborative Learning
User Modeling and User-Adapted Interaction
Tailoring Automatically Generated Hypertext
User Modeling and User-Adapted Interaction
Towards a noise-tolerant, representation-independent mechanism for argument interpretation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Towards the generation of rebuttals in a Bayesian Argumentation System
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
Lexical paraphrasing for document retrieval and node identification
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Incorporating a user model into an information theoretic framework for argument interpretation
UM'03 Proceedings of the 9th international conference on User modeling
Improving the presentation of argument interpretations based on user trials
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Tailoring Automatically Generated Hypertext
User Modeling and User-Adapted Interaction
A Probabilistic Approach to the Interpretation of Spoken Utterances
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Balancing conflicting factors in argument interpretation
SigDIAL '06 Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue
Modeling suppositions in users' arguments
UM'05 Proceedings of the 10th international conference on User Modeling
| Hi-index | 0.00 |
We describe a probabilistic approach for the interpretation of user arguments, and investigate the incorporation of different models of a user's beliefs and inferences into this mechanism. Our approach is based on the tenet that the interpretation intended by the user is that with the highest posterior probability. This approach is implemented in a computer-based detective game, where the user explores a virtual scenario, and constructs an argument for a suspect's guilt or innocence. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation -- a Bayesian network. This interpretation may differ from the user's argument in its structure and in its beliefs in the argument propositions. We conducted a synthetic evaluation of the basic interpretation mechanism, and a user-based evaluation which assesses the impact of the different user models. The results of both evaluations were encouraging, with the system generally producing argument interpretations our users found acceptable.