International Business Machines Corporation

Yehuda Naveh

Roy Emek

Itai Jaeger

Yossi Richter

Amir Ronen

Levente Klein

Ehud Aharoni

Oded Margalit

Avi Ziv

Amir Nahir

Gil Eliezer Shurek

Edi Shmueli

Daniel Patrick Connors

Upendra D Chitnis

Shai Fine

Tamar Domany

Dorit Baras

Sivan Sabato

Einat Kermany

Ilia Averbouch

Felix Geller

Donna Leigh Gresh

Ari Freund

Oleg Goldshmidt

Yaniv Altshuler

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.

System and method for decision making in strategic environments

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.

Accurately determining crop yield at a farm level

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.

Method and system for automatic residual consumption

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.

System, method and product for task allocation

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.

Solving constraint satisfaction problems using a field programmable gate array

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.

Context-based failure reporting for a constraint satisfaction problem

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.

Resource management using constraint programming with efficient ordering of variables

Workforce management using constraint programming with efficient ordering of variables

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.

Extending constraint satisfaction problem solving

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.

Method and computer program product for job selection and resource allocation of a massively parallel processor

Techniques are provided for modeling memory operations when generating test cases to verify multi-processor designs. Each memory operation has associated therewith a set of transfer attributes that can be referenced by a test generator. Using the transfer attributes, it is possible to generate a variety of interesting scenarios that handle read-write collisions and generally avoid reloading or resources. The model provides accurate result prediction, and allows write access restrictions to be removed from sensitive memory areas, such as control areas.

Memory model for functional verification of multi-processor systems

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.

Reformulation of constraint satisfaction problems for stochastic search

Test generation is improved by learning the relationship between an initial state vector for a stimuli generator and generation success. A stimuli generator for a design-under-verification is provided with information about the success probabilities of potential assignments to an initial state bit vector. Selection of initial states according to the success probabilities ensures a higher success rate than would be achieved without this knowledge. The approach for obtaining an initial state bit vector employs a CSP solver. A learning system is directed to model the behavior of possible initial state assignments. The learning system develops the structure and parameters of a Bayesian network that describes the relation between the initial state and generation success.

Harnessing machine learning to improve the success rate of stimuli generation

A method for solving a constraint satisfaction problem (CSP) includes choosing a first state corresponding to a first set of values of a set of variables, and selecting a hop distance within a state space of the variables responsively to a random distance selection criterion. A second state corresponding to a second set of the values of the variables is selected, such that the second state is separated from the first state by the hop distance. Constraint costs of the first and second states are compared. If the cost of the second state is lower than the cost of the first state, the first state is redefined to correspond to the second set of the values of the variables. These steps are repeated until a solution of the CSP is found.

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Haifa, Israel

Yehuda Naveh