Exact Solution Counting for Artificial Intelligence based
... Counting models in propositional logic (#SAT) and counting solutions for constraint satisfaction problems (#CSP) are challenging problems. They have numerous applications in AI, e.g. in approximate reasoning [1], in diagnosis [2], in belief revision [3], in probabilistic inference [4–7], in planning ...
... Counting models in propositional logic (#SAT) and counting solutions for constraint satisfaction problems (#CSP) are challenging problems. They have numerous applications in AI, e.g. in approximate reasoning [1], in diagnosis [2], in belief revision [3], in probabilistic inference [4–7], in planning ...
Solving Distributed Constraint Optimization Problems Using Logic
... maps each variable to one agent. A solution is a value assignment for all variables and its corresponding utility is the evaluation of all utility functions on such solution. The goal is to find a utility-maximal solution. A DCOP can be described by a constraint graph, where the nodes correspond to ...
... maps each variable to one agent. A solution is a value assignment for all variables and its corresponding utility is the evaluation of all utility functions on such solution. The goal is to find a utility-maximal solution. A DCOP can be described by a constraint graph, where the nodes correspond to ...
5. Constraint Satisfaction Problems CSPs as Search Problems
... The arc-consistency algorithm AC-3. After applying AC-3, either every arc is arcFoundations of AI Mai 11, 2012 be solved. 28 / 39 The variable has an empty domain, indicating that the CSP cannot name “AC-3” was used by the algorithm’s inventor (?) because it’s the third version developed in the ...
... The arc-consistency algorithm AC-3. After applying AC-3, either every arc is arcFoundations of AI Mai 11, 2012 be solved. 28 / 39 The variable has an empty domain, indicating that the CSP cannot name “AC-3” was used by the algorithm’s inventor (?) because it’s the third version developed in the ...
Lecture notes for week 5
... Involves consistencies at a higher level than arc-consistency E.g., 3-consistent (“path consistency”), k-consistent. if a n-node CSP can be shown to be n-consistent, a solution can be ...
... Involves consistencies at a higher level than arc-consistency E.g., 3-consistent (“path consistency”), k-consistent. if a n-node CSP can be shown to be n-consistent, a solution can be ...
PDF - Programming Systems Lab
... This works well for highly regular tournaments. However, in the presence of irregular constraints which occur in tournament planning practice and which are difficult to capture as properties of graphs, constructive methods fail and the problem degenerates to a combinatorial search problem. Techniqu ...
... This works well for highly regular tournaments. However, in the presence of irregular constraints which occur in tournament planning practice and which are difficult to capture as properties of graphs, constructive methods fail and the problem degenerates to a combinatorial search problem. Techniqu ...
PDF
... network timeslots with limited capacities within a 17 week season. The NFL season is a combination of two types of problems: constraint satisfaction and optimization. It involves hard constraints, those that need to be fulfilled by any schedule that would be acceptable, and soft constraints, those d ...
... network timeslots with limited capacities within a 17 week season. The NFL season is a combination of two types of problems: constraint satisfaction and optimization. It involves hard constraints, those that need to be fulfilled by any schedule that would be acceptable, and soft constraints, those d ...
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 ...
Building a Constraint Solver that Learns. In Proceedings of the AAAI
... game player, learned to play19 different two-dimensional, finite-board games as well or better than the best human experts (Epstein, 2001). Ariadne, a FORR-based pathfinder for two-dimensional mazes, learned to find its way efficiently through complex mazes modeled on real-world spaces (Epstein, 199 ...
... game player, learned to play19 different two-dimensional, finite-board games as well or better than the best human experts (Epstein, 2001). Ariadne, a FORR-based pathfinder for two-dimensional mazes, learned to find its way efficiently through complex mazes modeled on real-world spaces (Epstein, 199 ...
Combining satisfiability techniques from AI and OR
... C = {c1 , c2 , . . . , cm }. A clause is a disjunction of literals, where a literal is a boolean variable vi , or its negation v i . A clause is satisfied if and only if any one of its literals evaluates to true. A solution to a SAT problem is an assignment of values to variables that satisfies ever ...
... C = {c1 , c2 , . . . , cm }. A clause is a disjunction of literals, where a literal is a boolean variable vi , or its negation v i . A clause is satisfied if and only if any one of its literals evaluates to true. A solution to a SAT problem is an assignment of values to variables that satisfies ever ...
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
... are boolean-valued; typeface are real. must be solved to solve the entire LCNF problem1 . The key to the encoding is the simple but expressive concept of triggers — each propositional variable may trigger a constraint; this constraint is then enforced whenever the variable’s truth assignment is true ...
... are boolean-valued; typeface are real. must be solved to solve the entire LCNF problem1 . The key to the encoding is the simple but expressive concept of triggers — each propositional variable may trigger a constraint; this constraint is then enforced whenever the variable’s truth assignment is true ...
Introduction to Artificial Intelligence – Course 67842
... At 10 million nodes/sec, 280 = 4 billion years At 10 million nodes/sec, 4 * 220 = 0.4 seconds ...
... At 10 million nodes/sec, 280 = 4 billion years At 10 million nodes/sec, 4 * 220 = 0.4 seconds ...
DUCT: An Upper Confidence Bound Approach to Distributed
... be obtained from the constraint graph by finding a pseudotree (Freuder and Quinn 1985) of the graph. A pseudo-tree G ′ is simply a rooted directed spanning tree on G. In the algorithms we propose, agent communication takes place only via the edges in G ′ . Any edge in G \ G ′ is called a back edge. ...
... be obtained from the constraint graph by finding a pseudotree (Freuder and Quinn 1985) of the graph. A pseudo-tree G ′ is simply a rooted directed spanning tree on G. In the algorithms we propose, agent communication takes place only via the edges in G ′ . Any edge in G \ G ′ is called a back edge. ...
ECAI Paper PDF - MIT Computer Science and Artificial Intelligence
... Formalisms for soft constraints aim at more closely integrating constraint satisfaction and optimization. Soft constraints extend hard constraints by defining preference levels for the constraints, such that assignments are associated with an element from an ordered set. This element can be interpre ...
... Formalisms for soft constraints aim at more closely integrating constraint satisfaction and optimization. Soft constraints extend hard constraints by defining preference levels for the constraints, such that assignments are associated with an element from an ordered set. This element can be interpre ...
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 ...
av -bv -c - IDA.LiU.se
... 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. By induction, this holds for all clauses in the resoluti ...
... 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. By induction, this holds for all clauses in the resoluti ...
Constraint Programming - What is behind?
... and, consequently, finding solution satisfying all the constraints. Naturally, we do not satisfy one constraint only but a collection of constraints that are rarely independent. This complicates the problem a bit, so, usually, we have to give and take. ...
... and, consequently, finding solution satisfying all the constraints. Naturally, we do not satisfy one constraint only but a collection of constraints that are rarely independent. This complicates the problem a bit, so, usually, we have to give and take. ...
Automated Modelling and Solving in Constraint Programming
... Given a specification of the problem to be solved, there are many different ways to formulate the problem as a CSP. A major aim in reformulation is to ensure that the resulting CSP can be solved as efficiently as possible. Recently a large number of papers have been published that study the reformul ...
... Given a specification of the problem to be solved, there are many different ways to formulate the problem as a CSP. A major aim in reformulation is to ensure that the resulting CSP can be solved as efficiently as possible. Recently a large number of papers have been published that study the reformul ...
PDF - The Insight Centre for Data Analytics
... In this section we explain our strategy for searching for solutions that combine robustness and stability according to the definitions of Section 2. The measure of the distance from the dynamic bounds of the solution space (required for the robustness measurement) is not always obvious or easy to de ...
... In this section we explain our strategy for searching for solutions that combine robustness and stability according to the definitions of Section 2. The measure of the distance from the dynamic bounds of the solution space (required for the robustness measurement) is not always obvious or easy to de ...
Q - Duke Computer Science
... • Random restarts: if your hill-climbing search fails (or returns a result that may not be optimal), restart at a random point in the search space – Not always easy to generate a random state ...
... • Random restarts: if your hill-climbing search fails (or returns a result that may not be optimal), restart at a random point in the search space – Not always easy to generate a random state ...
Global Optimization for Multiple Agents - Infoscience
... that pj ∈ Pi and |Tj | > 1, ti owns a binary variable xij . Packets pj for which |Tj | = 1 are assumed to be delivered by this courier, if within courierRange. If xij = 1, then ti will service packet pj , and if xij = 0 it will not service it. If a packet is not serviced, a penalty γ is incurred. Fo ...
... that pj ∈ Pi and |Tj | > 1, ti owns a binary variable xij . Packets pj for which |Tj | = 1 are assumed to be delivered by this courier, if within courierRange. If xij = 1, then ti will service packet pj , and if xij = 0 it will not service it. If a packet is not serviced, a penalty γ is incurred. Fo ...
Constraint Programming and Artificial Intelligence
... Assuming a rule grammar, or primitive constraint language, design a filtering algorithm by searching through the space of possible ‘programs’ in the grammar, evaluating their quality against the specification of the constraint. ...
... Assuming a rule grammar, or primitive constraint language, design a filtering algorithm by searching through the space of possible ‘programs’ in the grammar, evaluating their quality against the specification of the constraint. ...
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 ...