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... In this section we discuss the motivation for the use of constraint logic programming. We explain which type of problem is best suited for the CLP approach and where these problems occur in practice. They share a set of characteristics, which make them very hard to tackle with conventional problem s ...
... In this section we discuss the motivation for the use of constraint logic programming. We explain which type of problem is best suited for the CLP approach and where these problems occur in practice. They share a set of characteristics, which make them very hard to tackle with conventional problem s ...
A Partial Taxonomy of Substitutability and Interchangeability
... Basic Interchangeability Concepts ...
... Basic Interchangeability Concepts ...
Simple Stochastic Temporal Constraint Networks
... The work presented in Kirillov [1994] was concerned primarily with heuristic solutions to the situation assessment task under stochastic uncertainty with emphasis on military surveillance applications. This work offers a framework for the representation of stochastically imprecise time points and in ...
... The work presented in Kirillov [1994] was concerned primarily with heuristic solutions to the situation assessment task under stochastic uncertainty with emphasis on military surveillance applications. This work offers a framework for the representation of stochastically imprecise time points and in ...
The Specification of Agent Behavior by Ordinary People: A Case Study
... complex goals and yet are intended to be used by a wide range of untrained people. For instance, consider the process of scheduling a meeting with numerous people subject to certain timing and participation constraints. E-Agents support the common task where an originator wants to ask a set of parti ...
... complex goals and yet are intended to be used by a wide range of untrained people. For instance, consider the process of scheduling a meeting with numerous people subject to certain timing and participation constraints. E-Agents support the common task where an originator wants to ask a set of parti ...
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 ...
Constraint Programming: In Pursuit of the Holy Grail
... integers (although often they are), they need not even be numeric. A solution to a CSP is an assignment of a value from its domain to every variable, in such a way that all constraints are satisfied at once. We may want to find: • just one solution, with no preference as to which one, • all solution ...
... integers (although often they are), they need not even be numeric. A solution to a CSP is an assignment of a value from its domain to every variable, in such a way that all constraints are satisfied at once. We may want to find: • just one solution, with no preference as to which one, • all solution ...
Combining Linear Programming and Satisfiability Solving for
... constraint inconsistency; solve returns constraint variable values. T (u) returns the constraint triggered by u (possibly null). Σ(u) denotes the result of setting literal u true in Σ and simplifying. The DPLL algorithm is similar but makes no reference to R, ∆, trigger variables, inconsistency chec ...
... constraint inconsistency; solve returns constraint variable values. T (u) returns the constraint triggered by u (possibly null). Σ(u) denotes the result of setting literal u true in Σ and simplifying. The DPLL algorithm is similar but makes no reference to R, ∆, trigger variables, inconsistency chec ...
Fixed-parameter complexity in AI and nonmonotonic reasoning
... We also study the complexity of circumscriptive inference from a general propositional theory when the attention is restricted to models of size k. This problem, referred-to as small model circumscription (SMC), is easily seen to be fixed-parameter intractable, but it does not seem to be complete fo ...
... We also study the complexity of circumscriptive inference from a general propositional theory when the attention is restricted to models of size k. This problem, referred-to as small model circumscription (SMC), is easily seen to be fixed-parameter intractable, but it does not seem to be complete fo ...
Automated Modelling and Solving in Constraint Programming
... Intelligence (www.aaai.org). All rights reserved. ...
... Intelligence (www.aaai.org). All rights reserved. ...
Constraint Programming - What is behind?
... 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
Learning bayesian network structure using lp relaxations Please share
... constraints involve edge selection variables that correspond to a non-planar graph (at least 5 nodes). The second and more subtle reason for why the above cycle relaxation is not tight is that the parent set selection variables ηi (si ), which were necessary to formulate a linear objective, couple t ...
... constraints involve edge selection variables that correspond to a non-planar graph (at least 5 nodes). The second and more subtle reason for why the above cycle relaxation is not tight is that the parent set selection variables ηi (si ), which were necessary to formulate a linear objective, couple t ...
Constraint Programming and Artificial Intelligence
... QuickXplain (Junker, 2004) As well known family of algorithms due to Junker have been developed for this purpose. A user’s preferences over the constraints can be accommodated easily. ...
... QuickXplain (Junker, 2004) As well known family of algorithms due to Junker have been developed for this purpose. A user’s preferences over the constraints can be accommodated easily. ...
Practical Issues in Modeling Large Diagnostic Systems with Multiply
... each PDi is the product of the probability tables associated with nodes in Di . A triplet Si = (Ni , Di , PDi ) is called a subnet of M . Definition 4 specifies the joint belief of all agents, which is a well defined probability distribution (the jpd). Furthermore, the jpd is consistent with the bel ...
... each PDi is the product of the probability tables associated with nodes in Di . A triplet Si = (Ni , Di , PDi ) is called a subnet of M . Definition 4 specifies the joint belief of all agents, which is a well defined probability distribution (the jpd). Furthermore, the jpd is consistent with the bel ...
PDF only - at www.arxiv.org.
... indeed NP-hard by reduction from the secondary problem of non-serial dynamic programming. In section 7 through 10 a new method, based on Li and D'Ambrosio's, is given that uses an OFP solution to build a data structure (called a factor tree, which is similar to the expression tree of [LD93]) from wh ...
... indeed NP-hard by reduction from the secondary problem of non-serial dynamic programming. In section 7 through 10 a new method, based on Li and D'Ambrosio's, is given that uses an OFP solution to build a data structure (called a factor tree, which is similar to the expression tree of [LD93]) from wh ...
5. Constraint Satisfaction Problems CSPs as Search Problems
... becomes a tree after removal of S. S is called a cycle cutset. For each possible assignment of variables in S that satisfies all constraints on S ...
... becomes a tree after removal of S. S is called a cycle cutset. For each possible assignment of variables in S that satisfies all constraints on S ...
Exploiting Past and Future: Pruning by Inconsistent Partial State
... such explanations were also exploited to put values back into domains when constraints are retracted. In constraint satisfaction, eliminating explanations are classically decisionbased, which means that each removed value is explained by a set of positive ...
... such explanations were also exploited to put values back into domains when constraints are retracted. In constraint satisfaction, eliminating explanations are classically decisionbased, which means that each removed value is explained by a set of positive ...
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
... levels of consistency is computationally tractable. The gamble here is that some of the other queries we are interested in are tractable as well. We review next what has been accomplished so far for a few common combinatorial structures. alldifferent constraint. This constraint restricts a set of va ...
... levels of consistency is computationally tractable. The gamble here is that some of the other queries we are interested in are tractable as well. We review next what has been accomplished so far for a few common combinatorial structures. alldifferent constraint. This constraint restricts a set of va ...
ECAI Paper PDF - MIT Computer Science and Artificial Intelligence
... instance of dynamic programming. This step can be viewed as generating an exact heuristic for search. In a second, top-down step, these values are used to enumerate solutions. This step can be viewed as a search guided by an exact heuristic, and is therefore backtrack-free. Previous work on constrai ...
... instance of dynamic programming. This step can be viewed as generating an exact heuristic for search. In a second, top-down step, these values are used to enumerate solutions. This step can be viewed as a search guided by an exact heuristic, and is therefore backtrack-free. Previous work on constrai ...
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 ...
Optimal 2-constraint satisfaction via sum
... Williams’s construction allows for partitioning the variables into more than three groups, but the complexity bound is obtained with three groups.) In addition to William’s work, constraint satisfaction problems have also been viewed as special classes of a more general family of sum-product problem ...
... Williams’s construction allows for partitioning the variables into more than three groups, but the complexity bound is obtained with three groups.) In addition to William’s work, constraint satisfaction problems have also been viewed as special classes of a more general family of sum-product problem ...
Document
... • As Deep Blue goes deeper and deeper into a position, it displays elements of strategic understanding. Somewhere out there mere tactics translate into strategy. This is the closet thing I've ever seen to computer intelligence. It's a very weird form of intelligence, but you can feel it. It feels li ...
... • As Deep Blue goes deeper and deeper into a position, it displays elements of strategic understanding. Somewhere out there mere tactics translate into strategy. This is the closet thing I've ever seen to computer intelligence. It's a very weird form of intelligence, but you can feel it. It feels li ...
Slides for October 2nd
... On a backtrack we must restore the domain values to a previous state We check what possible assignments can be next instead of making assignments until a conflict occurs. This is called forward checking. We prune domain values to be consistent with constraints. Pruning one domain value may allow fur ...
... On a backtrack we must restore the domain values to a previous state We check what possible assignments can be next instead of making assignments until a conflict occurs. This is called forward checking. We prune domain values to be consistent with constraints. Pruning one domain value may allow fur ...
Chapter 11 - 서울대 : Biointelligence lab
... Assigning values to variables subject to constraints Examples Eight-Queens problem Crossword puzzles ...
... Assigning values to variables subject to constraints Examples Eight-Queens problem Crossword puzzles ...
Decomposition method (constraint satisfaction)
In constraint satisfaction, a decomposition method translates a constraint satisfaction problem into another constraint satisfaction problem that is binary and acyclic. Decomposition methods work by grouping variables into sets, and solving a subproblem for each set. These translations are done because solving binary acyclic problems is a tractable problem.Each structural restriction defined a measure of complexity of solving the problem after conversion; this measure is called width. Fixing a maximal allowed width is a way for identifying a subclass of constraint satisfaction problems. Solving problems in this class is polynomial for most decompositions; if this holds for a decomposition, the class of fixed-width problems form a tractable subclass of constraint satisfaction problems.