Decreasing diagrams technique (van Oostrom, 1994) is a technique that can be widely applied to prove confluence of rewrite systems. To directly apply the decreasing diagrams technique to prove confluence of rewrite systems, rule-labelling heuristic has been proposed by van Oostrom (2008). We show how constraints for ensuring confluence of term rewriting systems constructed based on the rule-lab...

This paper presents the use of a cooperative co-evolutionary genetic algorithm (CCGA) in conjunction with a heuristic rule for solving a 3D container loading or bin packing problem. Unlike previous works which concentrate on using either a heuristic rule or an optimisation technique to find an optimal sequence of packages which must be loaded into the containers, the proposed heuristic rule is ...

This paper presents an eecient implementation method for a constructive negation approach in logic programming. The constructive negation approach is reformulated as a derivation rule. An heuristic method for eeciently implementing the derivation rule is presented and the complexity of the heuristic method is analysed.

This paper presents a statistical approach for rule-base generation of handwriting recognition. The new proposed method integrates the heuristic feature selection with the statistical evaluation and thus improves the generation speed and the performance of the fuzzy rule-based handwriting recognition system. Fuzzy statistical measures are employed to identify relevant features from a given larg...

Rule induction from examples is a machine learning technique that finds rules of the form condition → class, where condition and class are logic expressions of the form variable1 = value1 ∧ variable2 = value2 ∧... ∧ variablek = valuek. There are in general three approaches to rule induction: exhaustive search, divide-and-conquer, and separateand-conquer (or its extension as weighted covering). ...

LBR has demonstrated outstanding classification accuracy. However, it has high computational overheads when large numbers of instances are classified from a single training set. We compare LBR and the tree-augmented Bayesian classifier, and present a new heuristic LBR classifier that combines elements of the two. It requires less computation than LBR, but demonstrates similar prediction accuracy.