]> 2012-05-29T10:14:29-04:00 Prasad Tadepalli Prasad Prasad Tadepalli Tadepalli Inductive Logic Programming, 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers Active Learning with Committees for Text Categorization Active Learning with Committees Auto-Exploratory Average Reward Reinforcement Learning Gradient Boosting for Sequence Alignment Learning Goal-Decomposition Rules Using Exercises A Theory of Unsupervised Speedup Learning Optimizing the Predictive Value of Diagnostic Decision Rules Lazy ExplanationBased Learning: A Solution to the Intractable Theory Problem A Formalization of Explanation-Based Macro-operator Learning A Decision-Theoretic Model of Assistance Learning Horn Definitions with Equivalence and Membership Queries Guest editors' introduction: special issue on inductive logic programming (ILP-2007) Learning First-Order Acyclic Horn Programs from Entailment Automatic discovery and transfer of MAXQ hierarchies Logical Hierarchical Hidden Markov Models for Modeling User Activities Structured machine learning: the next ten years Transfer in variable-reward hierarchical reinforcement learning Multi-task reinforcement learning: a hierarchical Bayesian approach Learning for efficient retrieval of structured data with noisy queries A Relational Hierarchical Model for Decision-Theoretic Assistance A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems Exploiting prior knowledge in Intelligent Assistants - Combining relational models with hierarchies Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery Dynamic preferences in multi-criteria reinforcement learning Learning first-order probabilistic models with combining rules Model-based Hierarchical Average-reward Reinforcement Learning Learning Decision Rules by Randomized Iterative Local Search On Exact Learning of Unordered Tree Patterns Exact Learning of Unordered Tree Patterns from Queries Learning Horn Definitions: Theory and an Application to Planning Exact Learning of Tree Patterns from Queries and Counterexamples Model-Based Average Reward Reinforcement Learning Learning from Examples and Membership Queries with Structured Determinations Learning Goal-Decomposition Rules using Exercises Hierarchical Explanation-Based Reinforcement Learning Theory-guided Empirical Speedup Learning of Goal Decomposition Rules Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function A Formal Framework for Speedup Learning from Problems and Solutions Quantifying Prior Determination Knowledge Using the PAC Learning Model Learning from Queries and Examples with Tree-structured Bias An Apprentice-Based Approach to Knowledge Acquisition Learning with Incrutable Theories Maximizing the Predictive Value of Production Rules Planning Approximate Plans for Use in the Real World On the Tractability of Learning from Incomplete Theories Two New Frameworks for Learning An ensemble learning and problem solving architecture for airspace management An ensemble learning and problem solving architecture for airspace management Author Has author Has editor Has identifier Foaf:surname Foaf:name Foaf:firstName Anonymous.png Foaf:depiction Person