Yuanlin Zhang

An optimal coarse-grained arc consistency algorithm

The use of constraint propagation is the main feature of any constraint solver. It is thus of prime importance to manage the propagation in an efficient and effective fashion. There are two classes of propagation algorithms for general constraints: fine-grained algorithms where the removal of a value for a variable will be propagated to the corresponding values for other variables, and coarse-grained algorithms where the removal of a value will be propagated to the related variables. One big advantage of coarse-grained algorithms, like AC-3, over fine-grained algorithms, like AC-4, is the ease of integration when implementing an algorithm in a constraint solver. However, fine-grained algorithms usually have optimal worst case time complexity while coarse-grained algorithms do not. For example, AC-3 is an algorithm with non-optimal worst case complexity although it is simple, efficient in practice, and widely used. In this paper we propose a coarse-grained algorithm, AC2001/3.1, that is worst case optimal and preserves as much as possible the ease of its integration into a solver (no heavy data structure to be maintained during search). Experimental results show that AC2001/3.1 is competitive with the best fine-grained algorithms such as AC-6. The idea behind the new algorithm can immediately be applied to obtain a path consistency algorithm that has the best-known time and space complexity. The same idea is then extended to non-binary constraints.

Integrating answer set programming and constraint logic programming

We introduce a knowledge representation language

Automated epilepsy diagnosis using interictal scalp EEG

{\cal AC(C)}

Arc Consistency on n-ary Monotonic and Linear Constraints

extending the syntax and semantics of ASP and CR-Prolog, give some examples of its use, and present an algorithm,

Vicious Circle Principle and Logic Programs with Aggregates

\mathcal{AC}\!solver

Automated rule selection for opinion target extraction

, for computing answer sets of

Towards Answer Set Programming with Sorts

{\cal AC(C)}

Solving connected row convex constraints by variable elimination

programs. The algorithm does not require full grounding of a program and combines “classical” ASP solving methods with constraint logic programming techniques and CR-Prolog based abduction. The

A REVIEW OF TREE CONVEX SETS TEST

{\cal AC(C)}

Properties of tree convex constraints

based approach often allows to solve problems which are impossible to solve by more traditional ASP solving techniques. We believe that further investigation of the language and development of more efficient and reliable solvers for its programs can help to substantially expand the domain of applicability of the answer set programming paradigm.