Technical Note: \cal Q-Learning
Machine Learning
Resource-bounded reasoning in intelligent systems
ACM Computing Surveys (CSUR) - Special issue: position statements on strategic directions in computing research
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Building intelligent agents that learn to retrieve and extract information
Building intelligent agents that learn to retrieve and extract information
Web-scale information extraction in knowitall: (preliminary results)
Proceedings of the 13th international conference on World Wide Web
An Expected Utility Approach to Active Feature-Value Acquisition
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Feature value acquisition in testing: a sequential batch test algorithm
ICML '06 Proceedings of the 23rd international conference on Machine learning
Information extraction from Wikipedia: moving down the long tail
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and inference in weighted logic with application to natural language processing
Learning and inference in weighted logic with application to natural language processing
Planning executing sensing and replanning for information gathering
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Knowledge base completion via search-based question answering
Proceedings of the 23rd international conference on World wide web
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Given a database with missing or uncertain content, our goal is to correct and fill the database by extracting specific information from a large corpus such as the Web, and to do so under resource limitations. We formulate the information gathering task as a series of choices among alternative, resource-consuming actions and use reinforcement learning to select the best action at each time step. We use temporal difference q-learning method to train the function that selects these actions, and compare it to an online, error-driven algorithm called SampleRank. We present a system that finds information such as email, job title and department affiliation for the faculty at our university, and show that the learning-based approach accomplishes this task efficiently under a limited action budget. Our evaluations show that we can obtain 92.4% of the final F1, by only using 14.3% of all possible actions.