A model for reasoning about persistence and causation
Computational Intelligence
When policies are better than plans: decision-theoretic planning of recommendation sequences
Proceedings of the 6th international conference on Intelligent user interfaces
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Inferring user goals from personality and behavior in a causal model of user affect
Proceedings of the 8th international conference on Intelligent user interfaces
Pre-sending Documents on the WWW: A Comparative Study
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Building a Stochastic Dynamic Model of Application Use
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Requirements Analysis for Customizable Software Goals-Skills-Preferences Framework
RE '03 Proceedings of the 11th IEEE International Conference on Requirements Engineering
Learning and reasoning about interruption
Proceedings of the 5th international conference on Multimodal interfaces
SUPPLE: automatically generating user interfaces
Proceedings of the 9th international conference on Intelligent user interfaces
What role can adaptive support play in an adaptable system?
Proceedings of the 9th international conference on Intelligent user interfaces
A decision-theoretic approach to task assistance for persons with dementia
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Sharing experiences to learn user characteristics in dynamic environments with sparse data
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Estimating information value in collaborative multi-agent planning systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Supporting Multiple User Types with a Multimodal Dialog Agent
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
The need for an interaction cost model in adaptive interfaces
AVI '08 Proceedings of the working conference on Advanced visual interfaces
A probabilistic mental model for estimating disruption
Proceedings of the 14th international conference on Intelligent user interfaces
Interface feature prioritization for web services: Case of online flight reservations
Computers in Human Behavior
A decision-theoretic model of assistance
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A relational hierarchical model for decision-theoretic assistance
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Determining the value of information for collaborative multi-agent planning
Autonomous Agents and Multi-Agent Systems
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Automated software customization is drawing increasing attention as a means to help users deal with the scope, complexity, potential intrusiveness, and ever-changing nature of modern software. The ability to automatically customize functionality, interfaces, and advice to specific users is made more difficult by the uncertainty about the needs of specific individuals and their preferences for interaction. Following recent probabilistic techniques in user modeling, we model our user with a dynamic Bayesian network (DBN) and propose to explicitly infer the "user's type" --- a composite of personality and affect variables --- in real time. We design the system to reason about the impact of its actions given the user's current attitudes. To illustrate the benefits of this approach, we describe a DBN model for a text-editing help task. We show, through simulations, that user types can be inferred quickly, and that a myopic policy offers considerable benefit by adapting to both different types and changing attitudes. We then develop a more realistic user model, using behavioural data from 45 users to learn model parameters and the topology of our proposed user types. With the new model, we conduct a usability experiment with 4 users and 4 different policies. These experiments, while preliminary, show encouraging results for our adaptive policy.