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Papers by Chavalit Likitvivatanavong

We explore the problem of deriving explanations and implications for constraint satisfaction prob... more We explore the problem of deriving explanations and implications for constraint satisfaction problems (CSPs). We show that consistency methods can be used to generate inferences that support both functions. Explanations take the form of trees that showthe basis for assignments and deletions in terms of previous selections. These ideas are illustrated by dynamic, interactive testbeds.

Generalized arc consistency (GAC) is one of the most fundamental properties for reducing the sear... more Generalized arc consistency (GAC) is one of the most fundamental properties for reducing the search space when solving constraint satisfaction problems (CSPs). Consistencies stronger than GAC have also been shown useful, but the challenge is to develop efficient and simple filtering algorithms. Several CSP transformations are proposed recently so that the GAC algorithms can be applied on the transformed CSP to enforce stronger consistencies. Among them, the factor encoding (FE) is shown to be promising with respect to recent higher-order consistency algorithms. Nonetheless, one potential drawback of the FE is the fact that it enlarges the table relations as it increases constraint arity. We propose a variation of the FE that aims at reducing redundant columns in the constraints of the FE while still preserving full pairwise consistency. Experiments show that the new approach is competitive over a variety of random and structured benchmarks.

In this extended abstract we consider con guration problems with preferences rather than just har... more In this extended abstract we consider con guration problems with preferences rather than just hard constraints, and we analyze and discuss the features that such con gurators should have. In particular, these con gurators should provide explanations for the current state, implications of a future choice, and also information about the quality of future solutions, all with the aim of guiding the user in the process of making the right choices to obtain a good solution.

Lecture Notes in Computer Science, 2014

Lecture Notes in Computer Science, 2001

ABSTRACT We explore the problem of deriving explanations and implications for constraint satisfac... more ABSTRACT We explore the problem of deriving explanations and implications for constraint satisfaction problems (CSPs). We show that consistency methods can be used to generate inferences that support both functions. Explanations take the form of trees that showthe basis for assignments and deletions in terms of previous selections. These ideas are illustrated by dynamic, interactive testbeds.

ACM SIGAPP Applied Computing Review, 2013

Lecture Notes in Computer Science, 2007

Lecture Notes in Computer Science, 2008

Artificial Intelligence, 2015

ABSTRACT Constraint propagation is a key to the success of Constraint Programming (CP). The princ... more ABSTRACT Constraint propagation is a key to the success of Constraint Programming (CP). The principle is that filtering algorithms associated with constraints are executed in sequence until quiescence is reached. Many such algorithms have been proposed over the years to enforce the property called Generalized Arc Consistency (GAC) on many types of constraints, including table constraints that are defined extensionally. Recent advances in GAC algorithms for extensional constraints rely on directly manipulating tables during search. This is the case with a simple approach called Simple Tabular Reduction (STR), which systematically maintains tables of constraints to their relevant lists of tuples. In particular, STR2, a refined STR variant is among the most efficient GAC algorithms for positive table constraints. In this paper, we revisit this approach by proposing a new GAC algorithm called STR3 that is specifically designed to enforce GAC during backtrack search. By indexing tables and reasoning from deleted values, we show that STR3 can avoid systematically iterating over the full set of current tuples, contrary to STR2. An important property of STR3 is that it can completely avoid unnecessary traversal of tables, making it optimal along any path of the search tree. We also study a variant of STR3, based on an optimal circular way for traversing tables, and discuss the relationship of STR3 with two other optimal GAC algorithms introduced in the literature, namely, GAC4 and AC5TC-Tr. Finally, we demonstrate experimentally how STR3 is competitive with the state-of-the-art. In particular, our extensive experiments show that STR3 is generally faster than STR2 when the average size of tables is not reduced too drastically during search, making STR3 complementary to STR2.

Lecture Notes in Computer Science, 2003

We consider configuration problems with preferences rather than just hard constraints, and we ana... more We consider configuration problems with preferences rather than just hard constraints, and we analyze and discuss the features that such configurators should have. In particular, these configurators should provide explanations for the current state, implications of a future ...

Abstract: In this extended abstract we consider conguration problems with preferencesrather than ... more Abstract: In this extended abstract we consider conguration problems with preferencesrather than just hard constraints, and we analyze and discuss thefeatures that such congurators should have. In particular, these conguratorsshould provide explanations for the current state, implications of afuture choice, and also information about the quality of future solutions,all with the aim of guiding the user in the process of