International Business Machines Corporation

Vladimir Lipets

Segev Eliezer Wasserkrug

Michael Masin

Dany Moshkovich

Michael Katz

Michael Edward Factor

Paula Kim Ta-Shma

Alexander Zadorojniy

Avishay Traeger

A method for improving a machine operation are provided. The method includes receiving a plurality of domain specific heuristics and a set of states and a set of actions, where an immediate cost and/or reward is associated with a pair of state and action. The method also includes generating at least one of: a graph of state transitions for the actions, and a transition probability matrix. The method also includes executing a Markov Decision Process (MDP) model for solving an MDP problem, and outputting an MDP optimal policy of an optimal mapping of a given state to an action. The method also includes selecting one of the plurality of domain specific heuristics and heuristic input parameters thereof. The method also includes controlling the machine for solving a predefined optimization problem in a plurality of execution iterations.

Optimizing a machine for solving offline optimization problems

A method comprising obtaining a scheduling problem comprising: a set of agents and a set of tasks to be performed by the set of agents, wherein solving the scheduling problem using an automated solver is not feasible using available predetermined resources. The method comprises automatically generating a plurality of alternative scheduling problems, wherein a solution to each such alternative scheduling problem defines a solution to the scheduling problem and determining a solution to the scheduling problem by applying the automated solver to solve, while using the available predetermined resources, an alternative problem of the plurality of alternative scheduling problems to determine a solution to the alternative problem and by mapping the solution to the alternative problem to the scheduling problem, whereby determining the solution.

Automatic solution to a scheduling problem

A computer program product comprising a non-transitory computer readable storage medium retaining program instructions configured to cause a processor to perform actions, which program instructions implement: a framework for creating a model of an NP-hard problem, the model comprising at least one entity selected from the group comprising: an objective, a variable, an equation and a constraint, wherein the framework provides methods for automatically transforming the model, comprising: one or more methods for manipulating or changing a status of the entity of the model, the methods comprising a method for imposing or ignoring the constraint; and one or more methods related to operations to be applied to the entity of the model.

Reusable modeling for solving problems

A method for processing data using snapshots is provided. The method comprises generating a primary snapshot of data stored in a first storage medium, wherein the primary snapshot is a copy of the data stored in the first storage medium at a point in time; generating one or more secondary snapshots of the primary snapshot, wherein the secondary snapshots are copies of the data stored on the first storage medium at the point in time; and extracting the data stored in the first storage medium at the point in time by accessing the data included in the secondary snapshots, wherein multiple processing units concurrently transform the extracted data and load the transformed data onto a second storage medium.

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Haifa, Israel

Vladimir Lipets