Abstract: A method for ascertaining object information from image data. The method includes training an agent with the aid of reinforcement learning, successively recording images according to actions that are output by the agent, after each recording, the agent obtaining information, generated from the previously recorded images, concerning the location of surface points of an object as state information, and ascertaining the object information from the recorded images with the aid of the machine learning model.

Abstract: A method for picking up an object by means of a robotic device. The method includes obtaining at least one depth image of the object; determining, for each of a plurality of points of the object, the value of a measure of the scattering of surface normal vectors in an area around the point of the object; supplying the determined values to a neural network configured to output, in response to an input containing measured scattering values, an indication of object locations for pick-up; determining a location of the object for pick-up from an output which the neural network outputs in response to the supply of the determined values; and controlling the robotic device to pick up the object at the determined location.

Abstract: A method for generating training data for supervised learning for training a neural network to identify, from digital images of objects, locations of the objects for interacting with the objects. The method includes: acquiring, for each training object, at least one digital reference image and a plurality of further images of the training object; for each training object, specifying a location of the training object, mapping the at least one reference image onto a descriptor image, identifying descriptors of the specified location, mapping the further images of the training object onto further descriptor images, and determining locations in the further images by locating points in the further images, the descriptors of which in the further descriptor images correspond to the specified descriptors of the at least one specified location; and generating the training data for supervised learning by marking the determined locations for the further images of the training objects.

Abstract: A robot device, a method for training a robot control model, and a method for controlling a robot device. The method for training includes: supplying an image showing object(s), to a first and second prediction model to produce a first and second pickup prediction that has, for each pixel of the image, a first and second pickup robot configuration vector with an assigned first and second success probability; supplying the first and second pickup prediction to a blending model of the robot control model to produce a third pickup prediction that has, for each pixel of the image: a third pickup robot configuration vector that is a weighted combination of the first and second pickup robot configuration vector, and a third success probability that is a weighted combination of the first and second success probability; and training the robot control model by adapting the first and second weighting factors.

Abstract: A method for controlling a robotic device. The method includes: obtaining an image, processing the image using a neural convolutional network, which generates an image in a feature space from the image, the image in the feature space, feeding the image in the feature space to a neural actor network, which generates an action parameter image, feeding the image in the feature space and the action parameter image to a neural critic network, which generates an assessment image, which defines for each pixel an assessment for the action defined by the set of action parameter values for that pixel, selecting, from multiple sets of action parameters of the action parameter image, that set of action parameter values having the highest assessment, and controlling the robot for carrying out an action according to the selected action parameter set.

Abstract: A method for scheduling a set of jobs for a plurality of machines. Each job is defined by at least one feature which characterizes a processing time of the job. If any of the machines is free, a job from of the set of jobs is selected to be carrying out by said machine and scheduled for said machine. The job is selected as follows: a Graph Neural Network receives as input the set of jobs and a current state of at least the machine which is free, the Graph Neural Network outputs a reward for the set of jobs if launched on the machines, which states are inputted into the Graph Neuronal Network, and the job for the free machine is selected depending on the Graph Neural Network output.

Abstract: A method for predicting a future situation based on an analysis of at least one predictive pattern. The method comprises monitoring a plurality of events carried out by an event processing component, detecting a predictive pattern predictive of a future situation, selecting one of a plurality of proactive actions and an execution time according to its effect on at least one of a probability of occurrence and a cost of occurrence of the future situation, and outputting the selected proactive action.

Abstract: A system and method for identifying graphical model semantics, one aspect, receive a graphical diagram, associate each of a plurality of elements with at least one predetermined meta-types, identify a plurality of types in the graphical diagram, and determine a category for each of elements in said graphical diagram. Containment identification rules identify one or more containment relationships in the graphical diagram. Multiplicity identification rules identify multiplicity relationships in the graphical diagram. Advanced semantic rules identify visual elements that represent attributes and refine relationships to identify unique behavior.

Abstract: Systems and methods for admission control to a physical host system are provided herein. One aspect provides for receiving at least one resource request at an admission control component of a distributed computing system, the at least one resource request comprised of at least one system type; processing the at least one resource request utilizing at least one physical host accessible to the distributed computing system; specifying a number of resource request slots to be reserved for at least one system type based on at least one future reservation threshold accessible to the admission control component; and blocking resource requests from entering the system through the admission control component based on a number of available resource request slots and the at least one future reservation threshold. Other embodiments and aspects are also described herein.

Abstract: A method comprising using at least one hardware processor for: receiving historical responsiveness data of an offeree; computing predicted responsiveness of the offeree to one or more present offers, wherein said computing is based on the historical responsiveness data and is performed regardless of a content of the one or more present offers.

Abstract: A computerized method of adapting an event management framework comprising providing an event processing network (EPN) which models processing of a plurality of incoming events by the event management framework, providing at least one goal specifying a target value of at least one measurable attribute of the event management framework, performing a plurality of simulations on the EPN, each simulation of the processing of the plurality of incoming events according to a different set of a plurality of control values defining a behavioral pattern of at least one event processing agent of the EPN, selecting a control values set from the plurality of control values sets according to a match between an outcome of the plurality of simulations and the at least one target value, and adapting the event management framework according to the selected control values set.

Abstract: A method for predicting a future situation based on an analysis of at least one predictive pattern. The method comprises monitoring a plurality of events carried out by an event processing component, detecting a predictive pattern predictive of a future situation, selecting one of a plurality of proactive actions and an execution time according to its effect on at least one of a probability of occurrence and a cost of occurrence of the future situation, and outputting the selected proactive action.

Abstract: Systems and methods for admission control to a physical host system are provided herein. One aspect provides for receiving at least one resource request at an admission control component of a distributed computing system, the at least one resource request comprised of at least one system type; processing the at least one resource request utilizing at least one physical host accessible to the distributed computing system; specifying a number of resource request slots to be reserved for at least one system type based on at least one future reservation threshold accessible to the admission control component; and blocking resource requests from entering the system through the admission control component based on a number of available resource request slots and the at least one future reservation threshold. Other embodiments and aspects are also described herein.

Abstract: A system for learning admission policy for optimizing quality of service of computer resources networks is provided herein. The system includes a statistical data extractor configured to extract historical data of deployment requests issued to an admission unit of a computer resources network. The system further includes a Markov decision process simulator configured to generate a simulation model based on the extracted historical data and resources specifications of the computer resources network, in terms of a Markov decision process. The system further includes a value function generator configured to determine a value function for deployment requests admissions. The system further includes a machine learning unit configured to train a classifier based on the simulation model and the value function, to yield an admission policy usable for processing incoming deployment requests.

Abstract: A system and method for identifying graphical model semantics, one aspect, receive a graphical diagram, associate each of a plurality of elements with at least one predetermined meta-types, identify a plurality of types in the graphical diagram, and determine a category for each of elements in said graphical diagram. Containment identification rules identify one or more containment relationships in the graphical diagram. Multiplicity identification rules identify multiplicity relationships in the graphical diagram. Advanced semantic rules identify visual elements that represent attributes and refine relationships to identify unique behavior.

Abstract: A computer implemented method for solving a scheduling or capacity planning problem of a workforce of a service center, given an anticipated workload, is disclosed. The method includes the steps of calculating the number of workers and skills required in order to supply the adequate level of service; determining the number of workers required at a given period of time; and assigning specific workers subject to specific constraints to a specific period of time, by constructing and solving a mixed integer programming problem. The steps are implemented in either of computer hardware configured to perform said steps and computer software embodied in a non-transitory, tangible, computer-readable storage medium. Also disclosed are corresponding computer program product and data processing system.

Abstract: A system and method for identifying graphical model semantics, one aspect, receive a graphical diagram, associate each of a plurality of elements with one or more predetermined meta-types, identify a plurality of types in the graphical diagram, and determine a category for each of elements in said graphical diagram. Containment identification rules identify one or more containment relationships in the graphical diagram. Multiplicity identification rules identify multiplicity relationships in the graphical diagram. Advanced semantic rules identify visual elements that represent attributes and refine relationships to identify unique behavior.