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DeJong, G.

Publications and source records attributed to DeJong, G..

Improving Learning Performance Through Rational Resource Allocation

This article shows how rational analysis can be used to minimize learning cost for a general class of statistical learning problems. We discuss the factors that influence learning cost and show that the problem of efficient learning can be cast as a resource optimization problem. Solutions found in this way can be significantly more efficient than the best solutions that do not account for these factors. We introduce a heuristic learning algorithm that approximately solves this optimization problem and document its performance improvements on synthetic and real-world problems.

resource optimization

Constructing Simplified Plans via Truth Criteria Approximation

This paper has presented an approach to dealing with the complexity of explanation-based learning plans in complex domains. This approach uses a simplified algorithm to construct plans, and employs later refinements to repair bugs in constructed plans. This algorithm has the theoretical properties of completeness and convergence upon soundness. This incremental reasoning planning and learning algorithm has been implemented using a partial-order constraint posting planner and empirically compared to a conventional exhaustive reasoning partial-order constraint-posting planner and learning algorithm. This comparison showed that 1) incremental reasoning significantly reduced learning costs compared to exhaustive reasoning, 2) Explanation-based Learning (EBL) reduced failures from incremental reasoning, 3) EBL with incremental reasoning required less search to solve problems than EBL with exhaustive reasoning.

Explanation-based learning EBL incremental reasoni