I Agents, Bodies, Constraints, Dynamics, and Evolution Alan K. Mackworth
... of interest in artificial intelligence can each be characterized as a CSP with a set of variables; each variable has a domain of possible values, and there are various constraints on those variables, specifying which combinations of values for the variables are allowed (Mackworth 1977). The constrai ...
... of interest in artificial intelligence can each be characterized as a CSP with a set of variables; each variable has a domain of possible values, and there are various constraints on those variables, specifying which combinations of values for the variables are allowed (Mackworth 1977). The constrai ...
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 ...
Constraints and AI Planning
... specific value is assigned and then determine the value itself. This assignment process is called labeling and iterates until it has assigned values to all variables. Researchers have proposed numerous variable and value-ordering heuristics, such as smallest-domain-first or smallest-value-first.1 In ...
... specific value is assigned and then determine the value itself. This assignment process is called labeling and iterates until it has assigned values to all variables. Researchers have proposed numerous variable and value-ordering heuristics, such as smallest-domain-first or smallest-value-first.1 In ...
On the Space-Time Trade-off in Solving Constraint Satisfaction
... A constraint satisfaction problem (CSP) is a set of variables and a set of constraints. Each variable is associated with a finite value domain, and each constraint consists of a subset of the problem variables called its scheme and a set of mappings of domain values to variables in the scheme. An as ...
... A constraint satisfaction problem (CSP) is a set of variables and a set of constraints. Each variable is associated with a finite value domain, and each constraint consists of a subset of the problem variables called its scheme and a set of mappings of domain values to variables in the scheme. An as ...
and QUALITATIVE CONSTRAINTS - Dipartimento di Informatica
... Many different approaches in the literature, e.g., - simulation-based approaches (Petri Nets, Markov Models, Workflows, ...) ...
... Many different approaches in the literature, e.g., - simulation-based approaches (Petri Nets, Markov Models, Workflows, ...) ...
click here
... constraints. We may be asked to find an admissible, feasible solution to a problem or to find a good or even optimal solution according to some evaluation criteria. From a computational point of view, we know that most of these problems are difficult. They belong to the class of NP-hard [GJ79] probl ...
... constraints. We may be asked to find an admissible, feasible solution to a problem or to find a good or even optimal solution according to some evaluation criteria. From a computational point of view, we know that most of these problems are difficult. They belong to the class of NP-hard [GJ79] probl ...
Modelling Equidistant Frequency Permutation
... Symbols, codewords and positions may all be freely permuted. In order to break some of this symmetry, we lexicographically order (lex order) planes of the matrix in all three dimensions, using the technique of Flener et al. [16]. (We rely on Tailor to vectorize the planes in a consistent manner.) Th ...
... Symbols, codewords and positions may all be freely permuted. In order to break some of this symmetry, we lexicographically order (lex order) planes of the matrix in all three dimensions, using the technique of Flener et al. [16]. (We rely on Tailor to vectorize the planes in a consistent manner.) Th ...
Exploiting Past and Future: Pruning by Inconsistent Partial State
... In this section, we propose a second advanced extraction operator of (inconsistent) partial states. Unlike the proof-based extraction operator, this new one can be applied each time we reach a new node by analyzing all propagation performed so far. The principle of this operator is to build a partia ...
... In this section, we propose a second advanced extraction operator of (inconsistent) partial states. Unlike the proof-based extraction operator, this new one can be applied each time we reach a new node by analyzing all propagation performed so far. The principle of this operator is to build a partia ...
Dynamic domain splitting for numeric CSPs
... times. So, it is not sufficient to keep only the variable. For that reason, an explanation in dynamic domain splitting will be a set of splitting constraints. A failure occurs only when a domain becomes empty during the 2B-consistency filtering. An explanation for an empty domain, and hence for a fa ...
... times. So, it is not sufficient to keep only the variable. For that reason, an explanation in dynamic domain splitting will be a set of splitting constraints. A failure occurs only when a domain becomes empty during the 2B-consistency filtering. An explanation for an empty domain, and hence for a fa ...
Constraint Based Reasoning over Mutex Relations in Graphplan
... set of variables, D is a finite domain of values for the variables from X and C is a finite set of constraints over the variables from X. The constraint can be an arbitrary relation over the elements of the domains of its variables. Having a constraint satisfaction problem the task is to find an ass ...
... set of variables, D is a finite domain of values for the variables from X and C is a finite set of constraints over the variables from X. The constraint can be an arbitrary relation over the elements of the domains of its variables. Having a constraint satisfaction problem the task is to find an ass ...
Lparse Programs Revisited: Semantics and Representation of
... has not been fully studied. In [11], it is shown that lparse programs can be transformed to logic programs with monotone weight constraints while preserving the lparse semantics. Based on this result, in [10] weight constraints are translated to pseudo-boolean constraints. We do not know of any stud ...
... has not been fully studied. In [11], it is shown that lparse programs can be transformed to logic programs with monotone weight constraints while preserving the lparse semantics. Based on this result, in [10] weight constraints are translated to pseudo-boolean constraints. We do not know of any stud ...
PDF
... variables. We developed a new hybrid constraint solving schema, called systematic local search (Havens & Dilkina 2004), which retains some systematicity of constructive search. Our method backtracks through a space of complete but possibly inconsistent solutions while supporting the freedom to move ...
... variables. We developed a new hybrid constraint solving schema, called systematic local search (Havens & Dilkina 2004), which retains some systematicity of constructive search. Our method backtracks through a space of complete but possibly inconsistent solutions while supporting the freedom to move ...
Simple Stochastic Temporal Constraint Networks
... said to be consistent if at least one solution exists. A value v is said to be a feasible value for a variable X i if there exists a solution in which X i = v . The set of all feasible values of a variable is called the minimal domain of the variable. Major reasoning tasks with metric temporal const ...
... said to be consistent if at least one solution exists. A value v is said to be a feasible value for a variable X i if there exists a solution in which X i = v . The set of all feasible values of a variable is called the minimal domain of the variable. Major reasoning tasks with metric temporal const ...
David Bergman Assistant Professor Operations and Information
... from Binary Decision Diagrams [Extended Abstract]. Proceedings of the International Conference on Principles and Practice of Constraint Programming (CP 2014) , volume 8656 of Lecture Notes in Computer Science, pages 903-907, 2014. D. Bergman, A.A. Cire, A. Sabharwal, H. Samulowitz, W.-J van Hoeve. D ...
... from Binary Decision Diagrams [Extended Abstract]. Proceedings of the International Conference on Principles and Practice of Constraint Programming (CP 2014) , volume 8656 of Lecture Notes in Computer Science, pages 903-907, 2014. D. Bergman, A.A. Cire, A. Sabharwal, H. Samulowitz, W.-J van Hoeve. D ...
Solving Distributed Constraint Optimization Problems Using Logic
... rules for communication (sending, receiving, and interpreting messages) and a set of rules for generating an ASP program used for the computation of the utility tables as in Table 1 and the computation of the solution. We omit the detailed code of Cai due to space constraints. Instead, we will descr ...
... rules for communication (sending, receiving, and interpreting messages) and a set of rules for generating an ASP program used for the computation of the utility tables as in Table 1 and the computation of the solution. We omit the detailed code of Cai due to space constraints. Instead, we will descr ...
David Bergman Assistant Professor Operations and Information Management Department School of Business,
... Parallel Combinatorial Optimization with Decision Diagrams. Proceedings of the International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR) , volume 8451 of Lecture Notes in Computer Science, pages 351-367, 2014. D. Bergma ...
... Parallel Combinatorial Optimization with Decision Diagrams. Proceedings of the International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR) , volume 8451 of Lecture Notes in Computer Science, pages 351-367, 2014. D. Bergma ...
PDF - The Insight Centre for Data Analytics
... Because of the dynamism and uncertainty associated with many real life problems, these problems and their associated Constraint Satisfaction Problem (CSP) models may change over time; thus an earlier solution found for the latter may become invalid. Moreover, many approaches proposed in the literatu ...
... Because of the dynamism and uncertainty associated with many real life problems, these problems and their associated Constraint Satisfaction Problem (CSP) models may change over time; thus an earlier solution found for the latter may become invalid. Moreover, many approaches proposed in the literatu ...
Solving Distributed Constraint Optimization Problems Using Logic
... The agent controller, denoted by Cai , consists of a set of rules for communication (sending, receiving, and interpreting messages) and a set of rules for generating an ASP program used for the computation of the utility tables as in Table 1 and the computation of the solution. We omit the detailed ...
... The agent controller, denoted by Cai , consists of a set of rules for communication (sending, receiving, and interpreting messages) and a set of rules for generating an ASP program used for the computation of the utility tables as in Table 1 and the computation of the solution. We omit the detailed ...
Introduction to Artificial Intelligence – Course 67842
... States are defined by the values assigned so far. Initial state: the empty assignment { } Successor function: assign a value to an unassigned variable that does not conflict with current assignment fail if no legal assignments ...
... States are defined by the values assigned so far. Initial state: the empty assignment { } Successor function: assign a value to an unassigned variable that does not conflict with current assignment fail if no legal assignments ...
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. ...
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 ...
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. ...
Combining satisfiability techniques from AI and OR
... of complete methods to unsatisfiable problems. The reason for this is that any complete depth-first backtracking method, when attempting to solve even a satisfiable problem P , will of necessity spend the bulk of its time working on subproblems of P that are in fact unsatisfiable. After all, if the ...
... of complete methods to unsatisfiable problems. The reason for this is that any complete depth-first backtracking method, when attempting to solve even a satisfiable problem P , will of necessity spend the bulk of its time working on subproblems of P that are in fact unsatisfiable. After all, if the ...
Combining Linear Programming and Satisfiability Solving for
... 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 checks, or metric constraint solves. problem unsatisfiable. In order to maintain the s ...
... 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 checks, or metric constraint solves. problem unsatisfiable. In order to maintain the s ...