Author name cluster
Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.
AAAI Conference 1992 Conference Paper
Learning from experience to predict sequences of discrete symbols is a fundamental problem in machine learning with many applications. We present a simple and practical algorithm (TDAG) for discrete sequence prediction, verify its performance on data compression tasks, and apply it to problem of dynamically optimizing Prolog programs for good average-case behavior.
AIJ Journal 1992 Journal Article
The paper describes a simple heuristic approach to solving large-scale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a value-ordering heuristic, the min-conflicts heuristic, that attempts to minimize the number of constraint violations after each step. The heuristic can be used with a variety of different search strategies. We demonstrate empirically that on the n-queens problem, a technique based on this approach performs orders of magnitude better than traditional backtracking techniques. We also describe a scheduling application where the approach has been used successfully. A theoretical analysis is presented both to explain why this method works well on certain types of problems and to predict when it is likely to be most effective.
AAAI Conference 1990 Conference Paper
We show that the familiar explanation-based generalization (EBG) procedure is applicable to a large family of programming languages, including three families of importance to AI: logic programming (such as Prolog); lambda calculus (such as LISP); and combinator languages (such as FP). The main application of this result is to extend the algorithm to domains for which predicate calculus is a poor representation. In addition, many issues in analytical learning become clearer and easier to reason about.