Abstract: In one embodiment, payoffs to an actor during the strategic game according to a solution concept is simulated a predetermined number of times. A machine learning tool is used to predict a payoff based on the predetermined number of payoffs according to the solution concept, wherein the machine learning tool is trained with plurality of pairs, each pair comprising a set of simulated payoffs and an actual payoff, wherein the simulated payoffs are predetermined number of payoffs determined in accordance with the solution concept. In another embodiment, a process tree representative of a strategic game involving actors is obtained. The process tree may comprise computational nodes, and leaf nodes which define payoff based on computations of the computational nodes. The process tree is reduced to a game tree representing the strategic game by simulating routes in the process tree.

Abstract: Techniques for using a scaling relationship between crop drymass and elevation at a farm level to redistribute crop yield data are provided. In one aspect, a method for analyzing crop yield is provided. The method includes the steps of: obtaining crop yield data for a farm; cleansing the crop yield data using a data filter(s), wherein one or more data points are eliminated from the crop yield data by the data filter; and redistributing a value of the data points eliminated from the crop yield data to data points remaining in the crop yield data to create a crop yield distribution for the farm.

Abstract: A computer-implemented method performed by a computerized device, comprising: obtaining consumption data comprising readings indicating consumption of a product, the consumption data is monitored by a plurality of metering devices, wherein the metering devices are associated with a plurality of consumption entities, wherein the plurality of consumption entities comprising a consumption unit and one or more sub consumption units of the consumption unit, wherein the product is supplied serially to the one or more sub consumption units via the consumption unit; and calculating residual consumption of the plurality of consumption entities at a point in time, wherein the residual consumption is consumption of the consumption unit and which is not associated with a sub consumption unit, wherein the residual consumption is calculated based on the consumption by the plurality of consumption entities at a plurality of points in time.

Abstract: Techniques for using a scaling relationship between crop drymass and elevation at a farm level to redistribute crop yield data are provided. In one aspect, a method for analyzing crop yield is provided. The method includes the steps of: obtaining crop yield data for a farm; cleansing the crop yield data using a data filter(s), wherein one or more data points are eliminated from the crop yield data by the data filter; and redistributing a value of the data points eliminated from the crop yield data to data points remaining in the crop yield data to create a crop yield distribution for the farm.

Abstract: A method comprising calculating for each agent, an average quality of tasks that were completed in the past by the agent; allocating tasks to the agents, wherein said allocating comprises selecting an agent to perform a task, the selection is based on the average quality of the agent; in response to the agent completing the task, computing a reward for the agent, wherein the reward is calculated according to a total contribution of the agent to the system by completing the task; whereby biasing said allocating to prefer allocating tasks to a first agent over a second agent, if a quality of the first agent is greater than a quality of the second agent, wherein said biasing is not dependent on prior knowledge of the qualities. Optionally, the agents choose whether or not to perform a task and an agent's quality affects the contributions of the agent performing tasks.

Abstract: A method comprising calculating for each agent, an average quality of tasks that were completed in the past by the agent; allocating tasks to the agents, wherein said allocating comprises selecting an agent to perform a task, the selection is based on the average quality of the agent; in response to the agent completing the task, computing a reward for the agent, wherein the reward is calculated according to a total contribution of the agent to the system by completing the task; whereby biasing said allocating to prefer allocating tasks to a first agent over a second agent, if a quality of the first agent is greater than a quality of the second agent, wherein said biasing is not dependent on prior knowledge of the qualities. Optionally, the agents choose whether or not to perform a task and an agent's quality affects the contributions of the agent performing tasks.

Abstract: A method for configuring a Field Programmable Gate Array (FPGA) with a Constraint Satisfaction Problem (CSP) assignment having multiple constraint expressions, the method comprising: setting each of the multiple constraint expressions as a configurable logic block (CLB) in the FPGA, to yield multiple CLBs; setting an assignment vector in the FPGA, wherein the assignment vector is a number vector configured to store a candidate solution to the CSP assignment; and forming a feedback loop by connecting the assignment vector to inputs of the multiple CLBs, and connecting outputs of the multiple CLBs to the assignment vector. Further disclosed is a design structure for the FPGA, optionally residing on a storage medium as a data format used for the exchange of layout data of integrated circuits.

Abstract: A method comprising calculating for each agent, an average quality of tasks that were completed in the past by the agent; allocating tasks to the agents, wherein said allocating comprises selecting an agent to perform a task, the selection is based on the average quality of the agent; in response to the agent completing the task, computing a reward for the agent, wherein the reward is calculated according to a total contribution of the agent to the system by completing the task; whereby biasing said allocating to prefer allocating tasks to a first agent over a second agent, if a quality of the first agent is greater than a quality of the second agent, wherein said biasing is not dependent on prior knowledge of the qualities. Optionally, the agents choose whether or not to perform a task and an agent's quality affects the contributions of the agent performing tasks.

Abstract: A method for configuring a Field Programmable Gate Array (FPGA) with a Constraint Satisfaction Problem (CSP) assignment having multiple constraint expressions, the method comprising: setting each of the multiple constraint expressions as a configurable logic block (CLB) in the FPGA, to yield multiple CLBs; setting an assignment vector in the FPGA, wherein the assignment vector is a number vector configured to store a candidate solution to the CSP assignment; and forming a feedback loop by connecting the assignment vector to inputs of the multiple CLBs, and connecting outputs of the multiple CLBs to the assignment vector. Further disclosed is a design structure for the FPGA, optionally residing on a storage medium as a data format used for the exchange of layout data of integrated circuits.

Abstract: In one embodiment, payoffs to an actor during the strategic game according to a solution concept is simulated a predetermined number of times. A machine learning tool is used to predict a payoff based on the predetermined number of payoffs according to the solution concept, wherein the machine learning tool is trained with plurality of pairs, each pair comprising a set of simulated payoffs and an actual payoff, wherein the simulated payoffs are predetermined number of payoffs determined in accordance with the solution concept. In another embodiment, a process tree representative of a strategic game involving actors is obtained. The process tree may comprise computational nodes, and leaf nodes which define payoff based on computations of the computational nodes. The process tree is reduced to a game tree representing the strategic game by simulating routes in the process tree.

Abstract: A computer-implemented method, computerized apparatus and computer program product for generating economic model based on business transaction messages. The method comprising obtaining business transactions from a business collaboration network, wherein each business transaction is a business-to-business transaction; and automatically generating, by a processor, based on the business transactions, an economic simulator capable of simulating economic activity within the business collaboration network, wherein the economic activity is associated with a business transaction that is transmitted via the business collaboration network.

Abstract: A computer-implemented method performed by a computerized device, comprising: obtaining consumption data comprising readings indicating consumption of a product, the consumption data is monitored by a plurality of metering devices, wherein the metering devices are associated with a plurality of consumption entities, wherein the plurality of consumption entities comprising a consumption unit and one or more sub consumption units of the consumption unit, wherein the product is supplied serially to the one or more sub consumption units via the consumption unit; and calculating residual consumption of the plurality of consumption entities at a point in time, wherein the residual consumption is consumption of the consumption unit and which is not associated with a sub consumption unit, wherein the residual consumption is calculated based on the consumption by the plurality of consumption entities at a plurality of points in time.

Abstract: A method for solving a constraint satisfaction problem (CSP) comprises identifying a first illegal combination associated with at least one variable, wherein the first illegal combination contradicts at least one constraint; grouping the first illegal combination with a second illegal combination, in response to determining that the first and second illegal combinations contradict the same constraint; identifying at least one failure explanation for each group of illegal combinations, wherein the failure explanation is associated with at least one illegal combination in said group to provide a reason for a contradicted constraint associated with the illegal combination; assigning a value to each variable in the given domain to solve the CSP within the limitations defined by the constraints in a solution path; and generating a result, in response to determining that at least one assigned value to a variable contradicts a constraint.

Abstract: A method for solving a constraint satisfaction problem (CSP) comprises identifying a first illegal combination associated with at least one variable, wherein the first illegal combination contradicts at least one constraint; grouping the first illegal combination with a second illegal combination, in response to determining that the first and second illegal combinations contradict the same constraint; identifying at least one failure explanation for each group of illegal combinations, wherein the failure explanation is associated with at least one illegal combination in said group to provide a reason for a contradicted constraint associated with the illegal combination; assigning a value to each variable in the given domain to solve the CSP within the limitations defined by the constraints in a solution path; and generating a result, in response to determining that at least one assigned value to a variable contradicts a constraint.

Abstract: A method for resource management includes associating respective variables with resource consumers, and identifying resources as values applicable to the variables. A group of the variables are identified as preferred variables. An assignment of the values to the variables that satisfies constraints applying to the allocation of the resources is found by repeatedly performing the steps of choosing a variable from the group, instantiating the chosen variable with a value, removing the chosen variable from the group, and pruning the domains of the other variables by propagation of the constraints. The resources are assigned to the resource consumers responsively to the assignment of the values to the variables.

Abstract: A method for resource management includes associating respective variables with resource consumers, and identifying resources as values applicable to the variables. A group of the variables are identified as preferred variables. An assignment of the values to the variables that satisfies constraints applying to the allocation of the resources is found by repeatedly performing the steps of choosing a variable from the group, instantiating the chosen variable with a value, removing the chosen variable from the group, and pruning the domains of the other variables by propagation of the constraints. The resources are assigned to the resource consumers responsively to the assignment of the values to the variables.

Abstract: A computer-implemented method for resource management includes storing in a memory a set of resource consumers, resources for allocation to the resource consumers, and initial binary constraints applying to the allocation of the resources. Respective variables are associated with the resource consumers, and the resources are identified as values applicable to the variables. A non-binary scoring criterion is applied to generate scores on a graded scale for proposed assignments of one or more of the resources to respective ones of the resource consumers. One or more additional binary constraints are defined based on the graded scale of the scores. An assignment is found of the values to the variables that satisfies the initial and additional binary constraints using a constraint satisfaction problem (CSP) solver running on a processor that is coupled to the memory. The resources are assigned to the resource consumers responsively to the assignment of the values to the variables.

Abstract: A method for resource management includes associating respective variables with resource consumers, and identifying resources as values applicable to the variables. A group of the variables are identified as preferred variables. An assignment of the values to the variables that satisfies constraints applying to the allocation of the resources is found by repeatedly performing the steps of choosing a variable from the group, instantiating the chosen variable with a value, removing the chosen variable from the group, and pruning the domains of the other variables by propagation of the constraints. The resources are assigned to the resource consumers responsively to the assignment of the values to the variables.

Abstract: A method for job selection and resource allocation of massively parallel processors, the method includes: providing to a constraint satisfaction problem solver multiple domains, variables, and constraints representative of a massively parallel processor, of queued job requests and of jobs being processed by the massively parallel processor, and generating, by the constraint satisfaction problem solver a result representative of multiple jobs to be executed, at least partially in parallel, by multiple resources of the massively parallel processor.

Abstract: A truck configuration satisfying a truck configuration problem is automatically determined. The truck configuration problem is transformed to a target function, outputting values indicative of configurations satisfying the truck configuration problem. Stochastic local search methods are applied on the target function to determine the truck configuration. Preprocessing may be performed to improve efficiency, performance or the like of the stochastic local search methods. The truck configuration problem may be obtained from several sources, which may be independent of one another.