Constraint Programming: In Pursuit of the Holy Grail
... logical relation among several unknowns (or variables), each taking a value in a given domain. The constraint thus restricts the possible values that variables can take, it represents partial information about the variables of interest. Constraints can also be heterogeneous, so they can bind unknown ...
... logical relation among several unknowns (or variables), each taking a value in a given domain. The constraint thus restricts the possible values that variables can take, it represents partial information about the variables of interest. Constraints can also be heterogeneous, so they can bind unknown ...
s 1 - UNL CSE
... Gottlob, G., Leone, N., Scarcello, F. : On Tractable Queries and Constraints. In: 10th International Conference and Workshop on Database and Expert System Applications (DEXA 1999). (1999) Decther, R.: Constraint Processing. Morgan Kaufmann (2003) Freuder, E.C.: A Sufficient Condition for Backtrack-B ...
... Gottlob, G., Leone, N., Scarcello, F. : On Tractable Queries and Constraints. In: 10th International Conference and Workshop on Database and Expert System Applications (DEXA 1999). (1999) Decther, R.: Constraint Processing. Morgan Kaufmann (2003) Freuder, E.C.: A Sufficient Condition for Backtrack-B ...
Calc/Cream - Related Web Pages
... – New constraint x ≥ 0 is added by executing a method x.ge(0). Similarly, constraints y ≥ 0, x + y = 7, 2x + 4y = 20 are added to the network. – After adding all variables and constraints to the network, a constraint solver (using constraint propagation and backtrack) is created by the constructor n ...
... – New constraint x ≥ 0 is added by executing a method x.ge(0). Similarly, constraints y ≥ 0, x + y = 7, 2x + 4y = 20 are added to the network. – After adding all variables and constraints to the network, a constraint solver (using constraint propagation and backtrack) is created by the constructor n ...
GQR: A Fast Solver for Binary Qualitative Constraint Networks
... A (binary) constraint network is defined by a set of variables taking values in a given domain and a family of binary constraint relations between pairs of variables (on this domain). The constraint satisfaction problem is to determine for a given constraint network, whether there exists an assignme ...
... A (binary) constraint network is defined by a set of variables taking values in a given domain and a family of binary constraint relations between pairs of variables (on this domain). The constraint satisfaction problem is to determine for a given constraint network, whether there exists an assignme ...
Building a Constraint Solver that Learns. In Proceedings of the AAAI
... architecture for the rapid development of expertise (Epstein, 1992). To produce an adaptive, robust problem solver, FORR exploits many techniques observable in human learners. FORR itself is domain independent; a FORR-based application requires a set of domain-specific state representations and heur ...
... architecture for the rapid development of expertise (Epstein, 1992). To produce an adaptive, robust problem solver, FORR exploits many techniques observable in human learners. FORR itself is domain independent; a FORR-based application requires a set of domain-specific state representations and heur ...
Optimal 2-constraint satisfaction via sum
... Much theoretical work has been devoted to developing asymptotically fast exact algorithms for MAX-2-SAT. One of the best results is due to Gramm et al. [5] who give an algorithm that runs in time Õ(2m/5 ), where m is the number of clauses. 1 This is better than the trivial bound Õ(2n ), when the n ...
... Much theoretical work has been devoted to developing asymptotically fast exact algorithms for MAX-2-SAT. One of the best results is due to Gramm et al. [5] who give an algorithm that runs in time Õ(2m/5 ), where m is the number of clauses. 1 This is better than the trivial bound Õ(2n ), when the n ...
Constraint Programming and Artificial Intelligence
... Develop tools to support the automated integration of systematic and non-systematic constraint programming methods with operations research techniques. ...
... Develop tools to support the automated integration of systematic and non-systematic constraint programming methods with operations research techniques. ...
Getting More Out of the Exposed Structure in Constraint
... specifying the possible values that each variable in X can take, and C = {c1 , c2 , . . . , cm } a finite set of constraints restricting the combinations of values that the variables may take, which can be seen as relations over subsets of X. The exposed structure in CP models, corresponding to each ...
... specifying the possible values that each variable in X can take, and C = {c1 , c2 , . . . , cm } a finite set of constraints restricting the combinations of values that the variables may take, which can be seen as relations over subsets of X. The exposed structure in CP models, corresponding to each ...
ECAI Paper PDF - MIT Computer Science and Artificial Intelligence
... For the boolean polycell example in Fig. 1, the cardinalityminimal diagnoses are o1=B, o2=G, o3=G, a1=G, a2=G with value 1 and o1=G, o2=G, o3=G, a1=B, a2=G with value 1. If we assume that OR gates have 1% probability of failure and AND gates have .5% probability of failure, then the two leading prob ...
... For the boolean polycell example in Fig. 1, the cardinalityminimal diagnoses are o1=B, o2=G, o3=G, a1=G, a2=G with value 1 and o1=G, o2=G, o3=G, a1=B, a2=G with value 1. If we assume that OR gates have 1% probability of failure and AND gates have .5% probability of failure, then the two leading prob ...
Constraint Programming - What is behind?
... (CSP) is defined as: • a set of variables X={x1,...,xn}, • for each variable xi, a finite set Di of possible values (its domain), and • a set of constraints restricting the values that the variables can simultaneously take. Note that values need not be a set of consecutive integers (although often t ...
... (CSP) is defined as: • a set of variables X={x1,...,xn}, • for each variable xi, a finite set Di of possible values (its domain), and • a set of constraints restricting the values that the variables can simultaneously take. Note that values need not be a set of consecutive integers (although often t ...
BN with uncertain evidence
... [1] Peng, Y., Zhang, S., Pan, R.: “Bayesian Network Reasoning with Uncertain Evidences”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 18 (5), 539564, 2010 [2] Pan, R., Peng, Y., and Ding, Z: “Belief Update in Bayesian Networks Using Uncertain Evidence”, in Proceedings ...
... [1] Peng, Y., Zhang, S., Pan, R.: “Bayesian Network Reasoning with Uncertain Evidences”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 18 (5), 539564, 2010 [2] Pan, R., Peng, Y., and Ding, Z: “Belief Update in Bayesian Networks Using Uncertain Evidence”, in Proceedings ...
PPT - ConSystLab - University of Nebraska–Lincoln
... Soft assignment: using some conflict resolution strategy, some of the variables are assigned the value in a manner that respects its capacity constraint. ...
... Soft assignment: using some conflict resolution strategy, some of the variables are assigned the value in a manner that respects its capacity constraint. ...
Document
... CP and IP differ in modeling CP has clean models with [1..n] variables IP uses 0-1 variables reasonably naturally Practical interest in instances at the easy/hard interface ...
... CP and IP differ in modeling CP has clean models with [1..n] variables IP uses 0-1 variables reasonably naturally Practical interest in instances at the easy/hard interface ...
Automated Modelling and Solving in Constraint Programming
... set, which is given, for instance, as a set of examples of its solutions and non-solutions. This kind of learning is called constraint acquisition (Bessiere et al. 2005). The motivations for constraint acquisition are many. For example, in order to solve partially defined constraints more efficient ...
... set, which is given, for instance, as a set of examples of its solutions and non-solutions. This kind of learning is called constraint acquisition (Bessiere et al. 2005). The motivations for constraint acquisition are many. For example, in order to solve partially defined constraints more efficient ...
Q - Duke Computer Science
... x’s domain at most d times; – each time we have to check for at most d of z’s values whether it is consistent with the removed value for x; – so O(n2d2) lookups ...
... x’s domain at most d times; – each time we have to check for at most d of z’s values whether it is consistent with the removed value for x; – so O(n2d2) lookups ...
Disjunctive Temporal Planning with Uncertainty
... not yet occurred. Note this means that a DTPU is DC if some component STPU is DC, provided the decision for the component STPU can be consistently extended to a decision to all decision variables in the complete DTPU. (A component STPU may involve a strict subset of Vd .) Again, the converse is fals ...
... not yet occurred. Note this means that a DTPU is DC if some component STPU is DC, provided the decision for the component STPU can be consistently extended to a decision to all decision variables in the complete DTPU. (A component STPU may involve a strict subset of Vd .) Again, the converse is fals ...
I 1
... • the work extends Constraint Reasoning with ODEs • it may support decision in applications where one is interested in finding the range of parameters for which some constraints on the ODE solutions are met • it is an expressive and declarative constraint approach • it relies on safe methods that do ...
... • the work extends Constraint Reasoning with ODEs • it may support decision in applications where one is interested in finding the range of parameters for which some constraints on the ODE solutions are met • it is an expressive and declarative constraint approach • it relies on safe methods that do ...
Constraint Modelling: A Challenge for First Order Automated Reasoning (invited talk)
... A traditional debugging move, also useful in the other cases of inconsistency, is to find and present a [near] minimal inconsistent core: that is, a minimally inconsistent subset of the constraints. The problem of “axiom pinpointing” in reasoning about large databases is similar, except that in the ...
... A traditional debugging move, also useful in the other cases of inconsistency, is to find and present a [near] minimal inconsistent core: that is, a minimally inconsistent subset of the constraints. The problem of “axiom pinpointing” in reasoning about large databases is similar, except that in the ...
ASP-DPOP: Solving Distributed Constraint Optimization Problems
... Declarative programs differ from imperative programs in that declarative programs only specify the problem as a set of logical rules to be solved without defining a specific control flow. The declarative programming paradigm offers several advantages, including a more compact representation of the p ...
... Declarative programs differ from imperative programs in that declarative programs only specify the problem as a set of logical rules to be solved without defining a specific control flow. The declarative programming paradigm offers several advantages, including a more compact representation of the p ...
av -bv -c - IDA.LiU.se
... Proof for the second part: Consider a case where there is such an assignment, and assume that there is a resolution sequence leading to False. One can easily see that if two clauses are true with a given assignment, then any clause that is obtained by resolving them is also true with that assignment ...
... Proof for the second part: Consider a case where there is such an assignment, and assume that there is a resolution sequence leading to False. One can easily see that if two clauses are true with a given assignment, then any clause that is obtained by resolving them is also true with that assignment ...
Slides for October 2nd
... We will update possible domain values under the current partial assignment If some domain has no possible values left (a domain wipeout) then we must backtrack On a backtrack we must restore the domain values to a previous state We check what possible assignments can be next instead of making assign ...
... We will update possible domain values under the current partial assignment If some domain has no possible values left (a domain wipeout) then we must backtrack On a backtrack we must restore the domain values to a previous state We check what possible assignments can be next instead of making assign ...
March 30, 2015 Workshop
... --------------------------------------------------------------------The workshop aims at providing a forum to discuss novel issues on planning, scheduling, and constraint satisfaction problems. Solutions to many real-world problems need to integrate plan synthesis capabilities with time and resource ...
... --------------------------------------------------------------------The workshop aims at providing a forum to discuss novel issues on planning, scheduling, and constraint satisfaction problems. Solutions to many real-world problems need to integrate plan synthesis capabilities with time and resource ...
Artificial Intelligence
... 100000*D + 10000*O + 1000*N + 100*A+ 10*L + D + 100000*G + 10000*E + 1000*R + 100*A+ 10*L + D = 100000*R + 10000*O + 1000*B + 100*E+ 10*R + T ...
... 100000*D + 10000*O + 1000*N + 100*A+ 10*L + D + 100000*G + 10000*E + 1000*R + 100*A+ 10*L + D = 100000*R + 10000*O + 1000*B + 100*E+ 10*R + T ...