M. Koubarakis. From Local to Global Consistency in Temporal Constraint Networks. Theoretical Computer Science, 173:89--112, February 1997. Invited submission to the special issue dedicated to the 1st International Conference on Principles and Practice of Constraint Programming (CP95), Editors: U. Montanari and F. Rossi.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....supported by TMR project CHOROCHRONOS funded by ESPRIT IV. 1 Two very important operations in CLP and CDB systems are deciding consistency of a set of constraints, and performing variable elimination. Subclasses of linear constraints over the reals have also been considered in temporal reasoning [5, 18, 19, 22, 23, 31, 14, 15]. Important operations in temporal reasoning applications are the following: i) deciding consistency of a set of binary temporal constraints, ii) performing variable elimination, and (iii) computing the strongest feasible constraints between every pair of variables. Disjunctions of linear ....

....are the following: i) deciding consistency of a set of binary temporal constraints, ii) performing variable elimination, and (iii) computing the strongest feasible constraints between every pair of variables. Disjunctions of linear constraints over the reals are important in many applications [13, 5, 18, 19, 20, 22, 23, 31, 14, 15]. The problem of deciding consistency for an arbitrary set of disjunctions of linear constraints is NP complete [34] It is therefore interesting to discover classes of disjunctions of linear constraints for which consistency can be decided in PTIME. In [30] Lassez and McAloon have studied the ....

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M. Koubarakis. From Local to Global Consistency in Temporal Constraint Networks. Theoretical Computer Science, 173:89--112, February 1997. Invited submission to the special issue dedicated to the 1st International Conference on Principles and Practice of Constraint Programming (CP95), Editors: U. Montanari and F. Rossi.

....class of UTVPI 6= constraints also includes disequations of the form Sigmax 1 6= c or Sigmax 1 Sigma x 2 6= c. 1 All the results of this paper still hold if our constraints are taken over the set of real numbers. Our results on UTVPI 6= constraints are original and extend the results of [28, 30]. In the full paper we also provide efficient algorithms for consistency checking and global consistency enforcement for UTVPI and UTVPI 6= constraints. 3. Finally, we restate the results of Item 1 with C ranging over the newly discovered tractable classes. Tractable query answering problems for ....

....a fixed number of variables. Theorem 6 ( 29] We can eliminate a fixed number of variables from a set of Horn disjunctive linear constraints in PTIME. Luckily there are subclasses of linear constraints where variable elimination can be done in PTIME. Let us consider them in turn. Definition 9 ([28, 30]) A DIFF 6= constraint is a linear constraint of the form t 1 c or t 1 Gamma t 2 c where t 1 ; t 2 are variables, c is a constant and is or 6= The above constraint class extends the class of difference constraints or DIFF constraints studied in temporal reasoning [8] by adding ....

[Article contains additional citation context not shown here]

M. Koubarakis. From Local to Global Consistency in Temporal Constraint Networks. Theoretical Computer Science, 173:89--112, February 1997. Invited submission to the special issue dedicated to the 1st International Conference on Principles and Practice of Constraint Programming (CP95), Editors: U. Montanari and F. Rossi.

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