Plan recognition and discourse analysis: an integrated approach for understanding dialogues
Plan recognition and discourse analysis: an integrated approach for understanding dialogues
A Bayesian model of plan recognition
Artificial Intelligence
Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
Automated user modeling for intelligent interface
International Journal of Human-Computer Interaction
Plan Recognition in Natural Language Dialogue
Plan Recognition in Natural Language Dialogue
Statistical Language Learning
Partial Plan Recognition Using Predictive Agents
PRIMA '98 Selected papers from the First Pacific Rim International Workshop on Multi-Agents, Multiagent Platforms
A sound and fast goal recognizer
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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The incompleteness and uncertainty about the state of the world and about the consequences of actions are unavoidable. If we want to predict the performance of multiuser computing systems, we have the uncertainty of what the users are going to do, and how that affects system performance. Intelligent interface agent development is one way to mitigate the uncertainty about user behaviors by predicting what users will do based on learned users' behaviors, preferences, and intentions. This work focuses on developing user models that can analyze and predict user behavior in multi-agent systems. We have developed a formal theory of user behavior prediction based on hidden Markov models. This work learns the user model through a time-series action analysis and abstraction by taking users' preferences and intentions into account in order to formally define user modeling.