94 results about "Constraint satisfaction problem" patented technology

A feature checker provides the ability to review communications service features for interaction problems at service creation time, or at runtime. Hence a feature can be reviewed against existing service features or against a specific other user's features.The feature checker is based on modelling communications service features as constraint satisfaction problems in which constraints 600, 605 determine preset relationships between values 610, 615 for variables 200, 215, 300, 400 of the feature. The model can then be searched for inconsistencies which indicate feature interaction. Interaction can be solved by building in an "alternative invoke" 610 for a feature which overcomes the inconsistency. This might be for instance a form of invoke which, when instantiated, actively suppresses a conflicting feature.

A new type of Markovian sequence generator and generation method generates a Markovian sequence having controllable properties, notably properties that satisfy at least one control criterion which is a computable requirement holding on items in the sequence. The Markovian sequence is generated chunkwise, each chunk containing a plurality of items in the sequence. During generation of each chunk a search is performed in the space of Markovian sequences to find a chunk-sized series of items which enables the control criterion to be satisfied. The search can be performed using a generate and test approach in which chunk-sized Markovian sequences are generated then tested for compliance with the requirement(s) of the control criteria. Alternatively, the search can be performed by formulating the sequence-generation task as a constraint satisfaction problem, with one or more constraints ensuring that the generated sequence is Markovian and one or more constraints enforcing the requirement(s) of the control criteria. The sequence generator can be used in an interactive system where a user specifies the control criterion via an inputting device (20).

A computer-implemented method for solving a constraint satisfaction problem (CSP), which is defined by variables and constraints applicable to the variables, and which has states corresponding to respective sets of values of the variables. The method includes assigning cost functions to the constraints so that the states have respective costs determined by application of the cost functions to the respective sets of values of the variables, the respective costs defining a problem topography of the CSP having global extrema corresponding to solutions of the CSP. The constraints of the CSP are reformulated so as to perform at least one of increasing a density of the solutions in the problem topography and smoothing a gradient of the problem topography. One or more of the solutions of the CSP are found by applying a stochastic CSP solver to the reformulated constraints.

A method and system for automatic generation of sequences (notably temporal or spatial sequences) from items in a database involves the organisation of the data in a generic format, and the formulation of the problem as a Constraint Satisfaction Problem using special constraint classes. The database format includes attribute values referring to a predetermined taxonomy allowing similarity and difference between the respective values to be evaluated. The special constraint classes include the classes of cardinality constraints (which allow it to be specified that the number of items whose attribute belongs to a given set is within a particular range, and/or that the number of values of an attribute is within a specified range), similarity or dissimilarity constraints (which allow it to be specified that successive items must be similar or different from each other) and global difference constraints (which allow it to be specified that the respective values of a given attribute for all items in a range must be different from each other).

Methods, systems and apparatus for modeling a target system includes defining a constraint satisfaction problem (CSP) that characterizes the target system in terms of a set of variables, each having a respective input domain, and initial constraints applicable to the variables. The variables are partitioned into at least first and second sets. An abstract solution is found to the CSP, including a given assignment of the variables in the first set. A reduced domain of at least one of the variables in the second set is computed, so as to be compatible with the abstract solution. A redundant constraint on the abstract solution is determined responsively to the reduced domain. A concrete solution to the CSP is then found, using the abstract solution and the redundant constraint.

In one embodiment, the invention includes sorting a set of constraints based on weights associated with each constraint, the constraints being capable of being satisfied based on values selected for corresponding associated parameters, selecting a constraint that is unsatisfied and that has the greatest weight, selecting parameters associated with the constraint and finding parameter values to satisfy the constraint, after satisfying the selected unsatisfied constraint, selecting a further unsatisfied constraint having the next greatest weight and finding values for associated parameters to satisfy the further constraint, and repeating the selecting of further constraints and the finding of values for associated parameters.

A method for automatically generating test scenarios for a plurality of business rules created to form a ruleset for a business domain. A plurality of decision variables is extracted from a business object model associated with the business domain using a computer system. The plurality of business rules is parsed using a syntactic parser operating on the computer system to extract at least one condition expression and an assignment action. A collection of mutually exclusive constraint satisfaction problems is built from the decision variables, condition expressions, and assignment actions using a constraint problem generator operating on the computer system. A plurality of test scenarios is generated automatically from each identified constraint satisfaction problem in the collection using a constraint solver engine operating on the computer system.

A constraint satisfaction problem (CSP) is used to model a real-world problem in an application domain. The CSP includes a plurality of variables, a plurality of states, and one or more constraints determining which states each variable is permitted to be assigned to in a solution to the CSP. A binary tree is created using the CSP. The binary tree is iteratively searched to determine an efficient solution to the CSP. A new iteration of the search of the binary tree is started when a search depth is less than a restart depth. The restart depth based on a deepest depth previously searched minus a restart value. When the search is restarted, a NoGood marker is created indicating a portion of the binary tree that is not to be searched in the new iteration of the search. An efficient solution to the CSP is reported.

A method for solving a constraint satisfaction problem includes receiving a set of variables having respective input domains and a set of relations among the variables, and building a network of one or more hyper-arcs representative of the set of relations, each hyper-arc corresponding to one of the relations and linking nodes in the network corresponding to the variables that are subject to the relation. For each of the hyper-arcs, the variables are assembled in a hierarchy based on the relation corresponding to the hyper-arc. The input domains of the variables in the hierarchy are reduced, so as to determine respective output domains of the variables that are consistent with the relations.

The invention discloses a PPI network comparison-oriented graph matching constraint solving symbol method. According to the method, a CSP model is established (a constraint condition with a shortest path is established) by utilizing a constraint relationship in a graph structure; by adoption of a graph-based backtracking algorithm, an OBDD symbol technology and each included symbol operation, theaim of solving subgraph isomorphism problems is achieved; and finally, the technology is imported in to a PPI network comparison problem, the problem is solved and a PPI network comparison-oriented graph matching constraint solving symbol technology is given. According to the method, the graph-based backtracking algorithm which is used for solving constraint satisfaction problems is combined, theOBDD symbol technology is adopted, advantages of an operation method are developed, and a graph matching symbol algorithm is applied to the protein mutual action network comparison problems in the field of biological information according to a constraint symbol solving technology which is used for solving sub-graph isomorphism, so that the problem solving efficiency can be improved to a certain extent and the state space complexity is reduced.

A system and method are disclosed for allocating human resources to tasks using constraint satisfaction programming, where fictitious persons are used to satisfy required constraints, to ensure that the solution process continues until a solution is found, and capability constraints and job constraints are relaxed until a solution is found. Tasks using fictitious persons are identified, and information about task capability requirements not met and capabilities of unallocated human resources are displayed so that constraints may be relaxed and fictitious persons removed. There is provision for handling multi-task jobs where if a fictitious person is assigned to any task then all tasks will be assigned a fictitious person.

An intelligent design and evaluation method for a flight control actuation system of a large aircraft includes the steps of reducing a configuration set of a flight control actuation system by an artificial intelligence method based on a constraint satisfaction problem according to a constraint condition; according to the safety requirements of the flight control actuation system, eliminating theconfiguration that does not meet the requirements; establishing an evaluation method of a flight control actuation system optimization index; according to the objective function constructed by the evaluation method, carrying out the multi-objective optimization of flight control actuation system, and obtaining a Pareto optimization set to make the configuration set shrink further; carrying out decision analysis for the Pareto optimization set to determine the target configuration of the flight control actuation system.

Enterprises express the concepts of their electronic business-to-business (B2B) communication in differently structured ontology-like schemas. Collaborations benefit from merging the common concepts into semantically unique Business Entities (BEs) in a merged schema. Methods and systems for labeling the merged schema with descriptive, yet short and unique names, are described. A heuristically ranked list of descriptive candidate phrases for each BE is derived locally from the names and descriptions of the underlying concepts. A semantically unique candidate phrase is assigned to each BE that discriminates it from the other BEs by employing a constraint satisfaction problem solver.

A system for solving a dynamic constraint satisfaction problem comprises a constraint network of variables and constraints. The system creates a first sub-problem model that includes a first model type, one or more first variables and zero or more first constraints. The system propagates the first constraints through the constraint network and determines if a first conflict is detected from propagating the first constraints. If the first conflict is detected, the system restores the constraint network variables to a first previous state before the first constraints were propagated. The system creates a first sub-problem set that includes a second model type and one or more sub-problem models. The system connects the first sub-problem model to the first sub-problem set via a second constraint and propagates the second constraint through the constraint network.