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 ...

Slides for October 2nd

... A CSP is a set of variables, domains and constraints: Variables: V1 , ..., Vn Variable Domains: D1 , ..., Dn Constraints: C1 , ..., Cm We want to assign a value to each variable Vi such that vi ∈ Di and all constraints are satisfied. This is a variable assignment problem, the order in which assignme ...

... A CSP is a set of variables, domains and constraints: Variables: V1 , ..., Vn Variable Domains: D1 , ..., Dn Constraints: C1 , ..., Cm We want to assign a value to each variable Vi such that vi ∈ Di and all constraints are satisfied. This is a variable assignment problem, the order in which assignme ...

Q - Duke Computer Science

... O(n2) arcs; each arc is added to the queue at most d times; consistency of an arc can be checked with d2 lookups in the constraint’s ...

... O(n2) arcs; each arc is added to the queue at most d times; consistency of an arc can be checked with d2 lookups in the constraint’s ...

Introduction to Artificial Intelligence – Course 67842

... [ WA = red then NT = green ] same as [ NT = green then WA = red ] So only need to consider assignments to a single variable at each node ...

... [ WA = red then NT = green ] same as [ NT = green then WA = red ] So only need to consider assignments to a single variable at each node ...

Lecture notes for week 5

... As constraint propagation techniques get more involved (in order to more effectively prune variable domains), CPU time increases. ...

... As constraint propagation techniques get more involved (in order to more effectively prune variable domains), CPU time increases. ...

Optimal 2-constraint satisfaction via sum

... 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 number of clauses m is less than 5n. But note that m may well grow quadrat ...

... 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 number of clauses m is less than 5n. But note that m may well grow quadrat ...

Disjunctive Temporal Planning with Uncertainty

... (SC) if there exists a decision that, combined with any realisation, satisfies the constraints. In other words, there is a way to assign values to the decision variables such that, given any values for the parameters, at least one disjunct on each constraint is satisfied. Note this means that a DTPU ...

... (SC) if there exists a decision that, combined with any realisation, satisfies the constraints. In other words, there is a way to assign values to the decision variables such that, given any values for the parameters, at least one disjunct on each constraint is satisfied. Note this means that a DTPU ...

Getting More Out of the Exposed Structure in Constraint

... the dark cells represent the pairs of values that satisfy the constraint (its solutions). On the left of and below a grid are depicted the marginal distributions over the first and second variable respectively (i.e. the projection of the set of solutions onto each axis). The core concept of support ...

... the dark cells represent the pairs of values that satisfy the constraint (its solutions). On the left of and below a grid are depicted the marginal distributions over the first and second variable respectively (i.e. the projection of the set of solutions onto each axis). The core concept of support ...

Constraint Programming - What is behind?

... a node consistency (NC). It removes values from variables’ domains that are inconsistent with unary constraints on respective variable. The most widely used consistency technique is called arc consistency (AC). This technique removes values from variables’ domains that are inconsistent with binary c ...

... a node consistency (NC). It removes values from variables’ domains that are inconsistent with unary constraints on respective variable. The most widely used consistency technique is called arc consistency (AC). This technique removes values from variables’ domains that are inconsistent with binary c ...

av -bv -c - IDA.LiU.se

... The pruning operation removes unsupported values from the domains of variables. Each constraint can be made arc consistent in this way. ...

... The pruning operation removes unsupported values from the domains of variables. Each constraint can be made arc consistent in this way. ...

ECAI Paper PDF - MIT Computer Science and Artificial Intelligence

... In this section we investigate how optimization over lattices, as defined in Sec. 2, and in particular diagnosis, can be framed as a semiring-CSP. Since the mathematical properties of semiring-CSPs ensure that local constraint propagation is applicable, this will be the basis for efficient solution ...

... In this section we investigate how optimization over lattices, as defined in Sec. 2, and in particular diagnosis, can be framed as a semiring-CSP. Since the mathematical properties of semiring-CSPs ensure that local constraint propagation is applicable, this will be the basis for efficient solution ...

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 ...

Constraint Programming: In Pursuit of the Holy Grail

... can be found without any search. But the worstcase complexity of the algorithm for obtaining Nconsistency in an N-node constraint graph is exponential. Unfortunately, if a graph is (strongly) K-consistent for K

... can be found without any search. But the worstcase complexity of the algorithm for obtaining Nconsistency in an N-node constraint graph is exponential. Unfortunately, if a graph is (strongly) K-consistent for K

Constraint Programming and Artificial Intelligence

... design a filtering algorithm by searching through the space of possible ‘programs’ in the grammar, evaluating their quality against the specification of the constraint. ...

... design a filtering algorithm by searching through the space of possible ‘programs’ in the grammar, evaluating their quality against the specification of the constraint. ...

Combining Linear Programming and Satisfiability Solving for

... The LPSAT architecture uses a systematic SAT solver as the controlling component of the engine and makes calls to an LP system. The LPSAT algorithm is very similar to the DPLL algorithm for solving boolean satisfiability problems [Davis et al., 1962]. The key difference is in the definitions of “sat ...

... The LPSAT architecture uses a systematic SAT solver as the controlling component of the engine and makes calls to an LP system. The LPSAT algorithm is very similar to the DPLL algorithm for solving boolean satisfiability problems [Davis et al., 1962]. The key difference is in the definitions of “sat ...

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 ...

Exploiting Past and Future: Pruning by Inconsistent Partial State

... an unsatisfiable core by keeping track of all constraints involved in the proof of unsatisfiability [1]. Such constraints are the ones used during search to remove, through their propagators, at least one value in the domain of one variable. We adapt this “proof-based” approach to extract an unsatis ...

... an unsatisfiable core by keeping track of all constraints involved in the proof of unsatisfiability [1]. Such constraints are the ones used during search to remove, through their propagators, at least one value in the domain of one variable. We adapt this “proof-based” approach to extract an unsatis ...

Calc/Cream - Related Web Pages

... Constraint programming is widely used to develop various applications including constraint satisfaction problems and optimization problems, such as production planning and scheduling, etc. Constraint programming is originally studied as an extension of logic programming languages. However, after 199 ...

... Constraint programming is widely used to develop various applications including constraint satisfaction problems and optimization problems, such as production planning and scheduling, etc. Constraint programming is originally studied as an extension of logic programming languages. However, after 199 ...

PDF - Programming Systems Lab

... The constraint programming approach to solving combinatorial search problems such as round robin scheduling problems works as follows. Encode the problem as a constraint satisfaction problem , find a new problem #' that has the same set of solutions by applying so-called consistency techniques. N ...

... The constraint programming approach to solving combinatorial search problems such as round robin scheduling problems works as follows. Encode the problem as a constraint satisfaction problem , find a new problem #' that has the same set of solutions by applying so-called consistency techniques. N ...

PDF

... point in the search. The current assignments of these variables may or may not correspond to the assignments specified in the label. Definition 3. A label, λ, is valid iff every variable assignment hx = ai ∈ λ is the current assignment of the variable x. During search we will induce nogoods, i.e. pa ...

... point in the search. The current assignments of these variables may or may not correspond to the assignments specified in the label. Definition 3. A label, λ, is valid iff every variable assignment hx = ai ∈ λ is the current assignment of the variable x. During search we will induce nogoods, i.e. pa ...

A Fast Arc Consistency Algorithm for n-ary Constraints Olivier Lhomme Jean-Charles R´egin

... Constraint satisfaction problems (CSPs) form a simple formal frame to represent and solve combinatorial problems in artificial intelligence. They involve finding values for problem variables subject to constraints on which combinations are acceptable. The problem of the existence of solutions to the ...

... Constraint satisfaction problems (CSPs) form a simple formal frame to represent and solve combinatorial problems in artificial intelligence. They involve finding values for problem variables subject to constraints on which combinations are acceptable. The problem of the existence of solutions to the ...

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 ...

Constraint Based Reasoning over Mutex Relations in Graphplan

... Given a set of actions and a goal the task is to find out how to reach a state satisfying the given goal by using the allowed actions only. The whole process of finding of how to satisfy the goal starts in a specified initial state of the planning world. This notion is described in the following def ...

... Given a set of actions and a goal the task is to find out how to reach a state satisfying the given goal by using the allowed actions only. The whole process of finding of how to satisfy the goal starts in a specified initial state of the planning world. This notion is described in the following def ...

Dynamic domain splitting for numeric CSPs

... are not allowed. Thus, for each domain only two values are to be kept: its lower bound and its upper bound. In this paper, we will only consider 2B-consistency 3 , which is a kind of arc-consistency restricted to the bounds. ...

... are not allowed. Thus, for each domain only two values are to be kept: its lower bound and its upper bound. In this paper, we will only consider 2B-consistency 3 , which is a kind of arc-consistency restricted to the bounds. ...