CS 785 Course Description | SCS | UW

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To address the topic of designing intelligent interfaces, including an overview of higher level natural language processing techniques of particular value in the construction of interfaces, models of plan recognition, discourse and natural language generation, and user modeling. To expose students to a variety of intelligent system applications currently being explored in the field, including agents and multi-agent systems, data mining and information retrieval, case-based reasoning and intelligent tutoring. To provide students with an overview of the subtopics of the course as well as exposure to current research papers, with an aim to developing skills in presentation of research, critical analysis of research and contribution of original thought.


Series of papers which provide an overview of each course topic. Set of research papers determined by instructor which students select to present as part of course requirements. Current Recommended Reading: Readings in Agents, Huhns and Singh (eds.), Morgan Kaufmann, 1998.


3 hours per week - typically 2 hour lecture and 1 hour of paper presentations by students to compliment the lecture material.


Introduction (4 hrs)

Overview of natural language processing: syntax, semantics and pragmatics. Plan recognition. Models of discourse. Natural language generation. User Modeling. Paper presentations on topics of plan recognition, discourse, generation and user modeling.

Intelligent Interfaces (14 hrs)

Agents (personal digital assistants) and multi-agent systems. AI approaches to data mining and information retrieval applications. Case-based reasoning and knowledge-based systems. Intelligent tutoring systems. Paper presentations on agents and multi-agent systems; on data mining and information processing; on case-based reasoning and knowledge-based systems; on intelligent tutoring systems. Current software systems for these intelligent applications.

Course Conclusion (7 hrs)

Project presentations to provide an overview of current research topics and directions. Wrap-up of course; summary of lessons learned.