Armin Wolf

Adaptive Constraint Handling with CHR in Java

The most advanced implementation of adaptive constraint processing with Constraint Handling Rules (CHR) is introduced in the imperative object-oriented programming language Java. The presented Java implementation consists of a compiler and a run-time system, all implemented in Java. The run-time system implements data structures like sparse bit vectors, logical variables and terms as well as an adaptive unification and an adaptive entailment algorithm. Approved technologies like attributed variables for constraint storage and retrieval as well as code generation for each head constraint are used. Also implemented are theoretically sound algorithms for adapting of rule derivations and constraint stores after arbitrary constraint deletions. The presentation is rounded off with some novel applications of CHR in constraint processing: simulated annealing for the n queens problem and intelligent backtracking for some SAT benchmark problems.

Transforming Ordered Constraint Hierarchies into Ordinary Constraint Systems

In this paper, we propose ordered constraint hierarchies and a non-trivial error function to model and solve over-constrained real-world problems adequately. We substantiate our proposition by an example. Then we present a new method to transform constraint hierarchies into equivalent ordinary constraint systems. For practical applications, we present a modified algorithm based on an incomplete finite domain constraint solver. We conclude with a prototype implementation of the method and some aspects about our future work.

Adaptive Solving of Equations over Rational Trees

The solution of Dynamic Constraint Satisfaction Problems (DCSPs) in Constraint Logic Programming (CLP) basically requires a solution of dynamically changing systems of syntactical equations over the rational trees