Abstract: One embodiment of the present disclosure provides a system and method for determining a decision-making strategy. During operation, the system can obtain sample data corresponding to a service. The system can pre-determine, based on the sample data and the service, a basic parametric shape model and can generate a plurality of shape parameters for the basic parametric shape mode. The system can then generate one or more parametric shape models with different shapes based on the plurality of shape parameters. The system can determine goodness-of-fit of the one or more parametric shape models with respect to a pre-defined decision-making strategy. Next, the system can in response to determining that the goodness-of-fit of at least one parametric shape model satisfies a set of objectives, outputting the at least one parametric shape model as an optimum decision-making strategy for the service.

Abstract: This disclosure relates to a method of generating a symbol string based on an input sentence represented by a sequence of symbols. In particular, the method involves receiving an input symbol string representing a sentence, generating, using a neural network based on a sequence of dependency structure of elements in the input symbol string, an output symbol string corresponding to the input sentence. The neural network includes an encoder that converts elements of the input symbol string to a first hidden state in a form of a multi-dimensional vector, an attention mechanism that applies a weight to the first hidden state and generates the weighted first hidden state as a second hidden state, a decoder that generates a third hidden state based on at least one element of the input symbol string, an element of the output symbol string, and the second hidden state, and an output generator that generates an element of the output symbol string based on the second hidden state and the third hidden state.

Abstract: This disclosure involves verifying that a power network model corresponds to an electric power network providing electrical power in a geographical area. For instance, a validation device computes a validation score for a power network model based on a connectivity score, an asset score, and a power-flow score. The connectivity score indicates connectivity errors in the power network model as compared to the power network. The asset score indicates power-delivery errors in the power network model with respect to power-consuming assets serviced by the power network. The power-flow score indicates power-flow calculation errors in the power network model with respect to voltage ranges for the power network. The validation score is repeatedly computed for iteratively updated versions of the power network model until a threshold validation score is obtained. The validated power network model is provided to a control system for identifying and correcting errors in the power network.

Abstract: Systems, methods and computer software are disclosed for providing an OpenRAN solution suite. In one embodiment, a method is disclosed, the method including communicating, by an all G COTS (Commercial off the Shelf) Base Band Unit (BBU), with a plurality of different G user devices; communicating, by a software platform, with the all G COTS BBU, wherein the software platform includes virtualized software providing open RAN controller functionality, network orchestrator functionality, and SON edge core functionality; and communicating, by the software platform, with a plurality of different G core networks.

Abstract: A method including receiving, in a machine learning model (MLM), a corpus including words. The MLM includes layers configured to extract keywords from the corpus, plus a retrospective layer. A first keyword and a second keyword from the corpus are identified in the layers. The first and second keywords are assigned first and second probabilities. Each probability is a likelihood that a keyword is to be included in a key phrase. A determination is made, in the retrospective layer, of a first probability modifier that modifies the first probability based on a first dependence relationship between the second keyword being placed after the first keyword. The first probability is modified using the first probability modifier. The first modified probability is used to determine whether the first keyword and the second keyword together form the key phrase. The key phrase is stored in a non-transitory computer readable storage medium.

Abstract: A method and an apparatus for creating a workflow, the method being performed by a computing device, and including obtaining a log file generated while a user performs a task, parsing each of a plurality of events recorded in the log file, and creating an activity sequence to correspond to an order of the events, grouping a plurality of partial sequences extracted from the activity sequence, and creating a plurality of activity groups to correspond to each of a plurality of different repetitive tasks included in the task and creating the workflow of at least one activity group among the plurality of activity groups.

Abstract: A system and a method are disclosed that generate for display to a remote operator a user interface comprising a map, the map comprising visual representations of a source area, a plurality of candidate robots, and a plurality of candidate destination areas. The system receives, via the user interface, a selection of a visual representation of a candidate robot of the plurality of candidate robots, and detects a drag-and-drop gesture within the user interface of the visual representation of the candidate robot being dragged-and-dropped to a visual representation of a candidate destination area of the plurality of candidate destination areas. Responsive to detecting the drag-and-drop gesture, the system generates a mission, where the mission causes the candidate robot to autonomously transport an object from the source area to the candidate destination area.

Abstract: The present invention relates to an integrated sleep diagnosis and treatment device, and more particularly to an integrated apnea diagnosis and treatment device. The present invention additionally relates to methods of sleep diagnosis and treatment. The sleep disorder treatment system of the present invention can use a diagnosis device to perform various forms of analysis to determine or diagnose a subject's sleeping disorder or symptoms of a subject's sleep disorder, and using this analysis or diagnosis can with or in some embodiments without human intervention treat the subject either physically or chemically to improve the sleeping disorder or the symptoms of the sleeping disorder. The diagnostic part of the system can use many different types of sensors and methods for diagnosing the severity of the symptoms of or the sleep disorder itself. The treatment part of the system can use a device to physically or chemically treat the subject's symptoms or sleep disorder itself.

Abstract: Systems and methods for simultaneously presenting to a user with a plurality of available options for resolving delinquent debts to local, state and national tax authorities and providing the user with appropriate documentation to resolve his/her delinquency status based on a selected option. The system uses all available information from the tax agency website and personal information of the taxpayer, such as expenses and income, to compute the most favorable resolution solution for the taxpayer. The system further uses deep learning to ranks the generated options based on previously accepted solutions by the tax agency.

Abstract: The present disclosure relates to a pre-5th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4th-Generation (4G) communication system such as Long Term Evolution (LTE). The embodiments of the present invention provide methods and devices for network access. The method includes receiving a first request message forwarded by a distributed unit in a base station and indication information of the distributed unit with respect to the first request message, the first request message requesting to connect a user equipment to a network; determining a processing to be performed on the first request message based on the indication Information; and transmitting an indication of the determined processing to the distributed unit.

Abstract: The invention relates to a method for testing an air traffic management electronic system. The method includes receiving by the system of input data representative of a state of air traffic and establishing by the system of information related to air traffic as a function of the input data and delivery of the information to an electronic test device of the system. The method further includes determining by the electronic test device, as a function of the delivered information, of air traffic control instructions and providing said system with the instructions and receiving and processing of the instructions by the system.

Abstract: In various example embodiments, a system and method for data mesh visualization are presented. An avatar representation of a user is generated. A user-specified change to the avatar representation is received and the avatar representation is changed based on the user-specified change. A current status of the user is determined, and an updated avatar representation is generated based on the current status of the user. The updated avatar is displayed in association with a communication of the user.

Abstract: Disclosed is a non-transitory computer readable medium storing a computer program, wherein the computer program includes instructions to perform following steps for data processing when the computer program is executed by one or more processors, the steps including: recognizing at least one continuous section from each raw data subset; determining at least one serialization point, based on a start point and an end point of each of the at least one continuous section for each of the raw data subset; and generating a training data set by generating serialized training data, based on the at least one serialization point.

Abstract: A storage system configured to house a plurality of containers housing inventory items includes support members, a first set of parallel rails to support a mobile, manipulator robot, and a fluid supply line having a plurality of valves disposed within the fluid supply line. Each of the valves having a closed condition in which the supply line is in fluid isolation from an outside environment and an open condition in which the supply line is in fluid communication with the environment such that the supply line is configured to supply fluid to a mobile, manipulator robot. Mobile, manipulator robots for retrieving inventory items stored within the containers and retrieval methods are also disclosed herein.

Abstract: Methods include training, using a generative adversarial network, a generator model using data noise that includes data corresponding to real problems of a telecommunication site, generating a generated problem that has not occurred at the telecommunication site and that has a non-zero probability of occurring at the site in the future, providing the generated problem to a virtual agent that is configured to generate a solution action to resolve the generated problem, evaluating the solution action relative to the generated problem to determine a performance value corresponding to the solution action, and responsive to the performance value being higher than other performance values corresponding to other solution actions for the generated problem, generating a generic problem model that corresponds to the generated problem and that is associated with the solution action.

Abstract: One or more embodiments herein can facilitate a process to guide a user at a digital medium. An exemplary system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an obtaining component that obtains user behavior data from a digital medium, the user behavior data defining an interaction of the user with the digital medium, a prediction component that, based on the user behavior data, predicts a path of the user within the digital medium, and a classification component that, based on the user behavior data and on the path prediction, classifies a position of the user along a defined path to a conversion objective of the digital medium. The conversion objective can comprise obtaining the user as a customer, achieving a financial transaction, or presentation of a communication.

Abstract: Computer systems and methods modify a base deep neural network (DNN). The method comprises replacing the target node of the base DNN with a compound node to thereby create a modified base DNN. The compound node comprises at least first and second nodes. The first node is trained to detect target node patterns in inputs to the first node and the second node is trained to detect an absence of the target node patterns in inputs to the second node, and the first and second nodes are trained to be non-complementary.

Abstract: The disclosed computer-implemented method for utilizing federated machine-learning to protect against potentially malicious data may include (i) arranging a set of client devices into groups for applying a federated machine-learning model, (ii) determining model updates for each of the groups over a predetermined period, (iii) training one or more recurrent neural networks to derive a low-dimensional representation of the model updates, (iv) calculating a data quality score for each of the client devices based on the model updates, (v) applying the federated machine-learning model to classify data instances on each of the client devices as including clean data or potentially corrupt data, and (vi) performing a security action that protects against the potentially malicious data by tagging the data instances classified as the potentially corrupt data. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Techniques for automatic scene change detection are described. As one example, a computer-implemented method includes receiving a request to train an ensemble of machine learning models on a training dataset of videos having labels that indicate scene changes to detect a scene change in a video, partitioning each video file of the training dataset of videos into a plurality of shots, training the ensemble of machine learning models into a trained ensemble of machine learning models based at least in part on the plurality of shots of the training dataset of videos and the labels that indicate scene changes, receiving an inference request for an input video, partitioning the input video into a plurality of shots, generating, by the trained ensemble of machine learning models, an inference of one or more scene changes in the input video based at least in part on the plurality of shots of the input video, and transmitting the inference to a client application or to a storage location.

Abstract: The present disclosure discloses an artificial intelligence apparatus including an input interface configured to obtain input data, a sensing interface configured to obtain environment information, and one or more processors configured to classify an object by inputting the input data obtained from the input interface to an artificial intelligence model, in which the artificial intelligence model uses a first learning model and a second learning model which is connected with the first learning model and includes a plurality of output layers to respectively assign weights to the respective result values output by the plurality of output layers and combine the respective result values to which the weights are assigned to derive the final result.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Embodiments of the present disclosure are directed to a system, methods, and computer-readable media for facilitating stylistic expression transfers in machine translation of source sequence data. Using integrated loss functions for style transfer along with content preservation and/or cross entropy, source sequence data is processed by an autoencoder trained to reduce loss values across the loss functions at each time step encoded for the source sequence data. The target sequence data generated by the autoencoder therefore exhibits reduced loss values for the integrated loss functions at each time step, thereby improving content preservation and providing for stylistic expression transfer.

Abstract: A polarizing beam splitting system is described. The polarizing beam splitting system may include first and second prisms where the volume of the first prism is no greater than half the volume of the second prism. The first prism includes first and second surfaces and a light source may be disposed adjacent the first surface and an image forming device may be disposed adjacent the second surface. The first prism has a first hypotenuse and the second prism has a second hypotenuse. A reflective polarizer is disposed between the first and second hypotenuses.

Abstract: A method may include obtaining data representative of an NNT of a graph-based model that includes multiple components. The multiple components may include multiple neural nodes and at least one connection. The at least one connection may associate two or more of the neural nodes. The method may include displaying the NNT including the multiple components in a GUI via a display screen. The method may include receiving user input effective to indicate that at least one of the components of the NNT is to be modified. The user input may be received via the GUI. The method may include modifying the at least one of the components of the NNT. The at least one of the components may be modified based on the user input. The method may include displaying the NNT that comprises the modification of the at least one of the components via the GUI.

Abstract: A hearing assistance device including: a microphone arranged to receive sound of an environment in which the hearing assistance device is located; a wireless communication device arranged to wirelessly communicate data with an external device; a controller operably connected with the microphone and arranged to process the received sound using sound processing settings that have been determined using a trained machine learning processing model; and a speaker operably connected with the controller and arranged to output the processed sound. The sound processing settings have been determined using the trained machine learning processing model based on a hearing response of a user and one or more properties of the environment.

Abstract: A first and second blending profile may be created for a set of question answering pipelines. A set of test answer data may be generated for a first answering pipeline. The test answer data may be generated based on a set of test question and using an answer key associated with the test questions. Based on the test answer data, a first blending profile can be created for the first answering pipeline. Using the set of test questions and a second answer key, another set of test answer data may be generated. This set may be generated for the second answering pipeline. Using this second answering pipeline test answer data, a second blending profile can be generated for the second answering pipeline. Each blending profile may have metadata about a confidence of each pipeline.

Abstract: A method includes obtaining (i) device data associated with multiple electronic devices and (ii) advertisement data associated with one or more advertisement campaigns. The method also includes identifying first features and second features, where the first features correspond to the device data and the second features correspond to the advertisement data. The method further includes generating a graph relating usage history of the multiple electronic devices and one or more advertisement genres. The method also includes identifying a specified electronic device from among the multiple electronic devices and an advertisement segment from among the one or more advertisement campaigns using the first features, the second features, and the graph. In addition, the method includes providing the advertisement segment to the specified electronic device.

Abstract: A data exchange method, a data exchange device, and a computing device for data exchange between a provider and a recipient for machine learning, the method including: (a) receiving a machine learning model from the provider (S1100); (b) respectively transforming output data samples into corresponding output eigenvectors by utilizing the machine learning model from the provider (S1200); (c) after transformation, combining the output eigenvectors with corresponding identifiers to form exchange samples (S1300). According to the data exchange method, original data is transformed into vector information which cannot be restored but can be applied to machine learning, for use in exchange, so as to, on one hand, enable efficient use of data for machine learning and, on the other hand, prevent unauthorized use, sale or disclosure of the original data.

Abstract: An illustrative embodiment disclosed herein is an apparatus including a processor having programmed instructions to receive, from a client, a request to create a catalog item corresponding to a file, determine an identifier of a cluster manager associated with a cluster having available capacity, send, to the cluster manager, a source location of the catalog item, and cause the cluster manager to store the catalog item from the source location in the cluster.

Abstract: A method, a computer-readable medium, and an apparatus for feature learning are provided. The apparatus may receive a data sample as an input to a feature learning model. The apparatus may calculate a reconstruction error based on the data sample and a plurality of features of the feature learning model. The apparatus may determine whether the reconstruction error satisfies a first threshold. The apparatus may add a feature into the feature learning model to represent the data sample if the data sample satisfies the first threshold. The apparatus may determine whether the reconstruction error satisfies a second threshold. The apparatus may ignore the data sample if the reconstruction error satisfies the second threshold. The apparatus may update the weights associated with the plurality of features of the feature learning model if the reconstruction error satisfies neither the first threshold nor the second threshold.

Abstract: According to an example aspect of the present invention, there is provided an apparatus comprising memory configured to store convolutional artificial neural network information comprising at least one filter definition, and at least one processing core configured to generate, from a preceding layer, a convolutional result of a succeeding layer of the artificial neural network in accordance with the at least one filter definition, and generate, from the convolutional result, an activation result of the succeeding layer by using an activation function, the activation function taking three arguments, the three arguments being derived from the convolutional result.

Abstract: Techniques and mechanisms for processing differential video data with a spiking neural network to provide action recognition functionality. In an embodiment, the spiking neural network is coupled to receive and process a first one or more spike trains which represent an encoded version of a sequence comprising frames of differential video data. In turn, the frames of differential video data are each based on a difference between a respective two frames of raw video data. Based on the processing of the first one or more spike trains, the spiking neural network may output a second one or more spike trains. In another embodiment, the second one or more spike trains are provided to train the spiked neural network to recognize an activity type, or to classify a video sequence as including a representation of an instance of the activity type.

Abstract: A method of generating training spectra for training of a neural network includes measuring a first plurality of training spectra from one or more sample substrates, measuring a characterizing value for each training spectra of the plurality of training spectra to generate a plurality of characterizing values with each training spectrum having an associated characterizing value, measuring a plurality of dummy spectra during processing of one or more dummy substrates, and generating a second plurality of training spectra by combining the first plurality of training spectra and the plurality of dummy spectra, there being a greater number of spectra in the second plurality of training spectra than in the first plurality of training spectra. Each training spectrum of the second plurality of training spectra having an associated characterizing value.

Abstract: Systems, methods and computer software are disclosed for providing a Service Bus for telecommunications infrastructure. The services bus provides a communications system between mutually interacting software applications, including a plurality of microservices, each microservice comprising: an internal bus; a data store in communication with the internal bus; a data access object in communication with the internal bus; a message exchange object in communication with the internal bus; a MAPReduce engine in communication with the internal bus; and a restful Application Programming Interface (API) bus in communication with the data access object, the message exchange object and the MAPReduce engine.

Abstract: According to an embodiment of the present disclosure for solving the aforementioned problem, disclosed is a computer program stored in a computer-readable storage medium executable by one or more processors, in which when the computer program is executed by one or more processors of a computer device, the computer program allows the one or more processors to perform the following operations for data processing, and the operations may include: an operation of generating a plurality of transformed data based on each of a plurality of data included in a data set; an operation of generating a test data set based on the plurality of data and the plurality of transformed data; and an operation of testing the performance of the model by calculating the test data set by using the model.

Abstract: Aspects of the present invention disclose a method, computer program product, and system for employing keystroke dynamics authentication. The method includes one or more processors recording, for a plurality of users, a respective keystroke session of multiple typing samples (typed consecutively) of a credential associated with a respective user of the plurality. The method further includes extracting a set of features that characterize typing changes between the multiple typing samples. The method further includes detecting repeated typing of a certain one of the credentials that is associated with a certain user of the plurality. The method further includes extracting, from the repeated typing of the certain credential, a set of features that characterize typing changes between repetitions. The method further includes comparing the feature set extracted from the repeated typing against feature sets extracted from a set of the keystroke sessions, estimating which user is the first user.

Abstract: Examples for relational time-series classification method for corrosion maintenance dispatch. A method may involve generating a self-organizing map for each parameter that corresponds to a group of aircraft. The self-organizing map organizes time-series data associated with a parameter into a two-dimensional representation. The method may further involve, for each map, identifying polar coordinates that represent a location of a particular node, in a grid of nodes within the self-organizing map, that is located closest to the time-series data organized in the two-dimensional representation. The method may then involve providing polar coordinates identified from each self-organizing map, along with indications of corrosion levels corresponding to the aircraft, as inputs into a classification model to train the classification model to determine trends between the parameters.

Abstract: Disclosed is a strategy guide method performed by an artificial intelligence (AI)-based strategy guide system, the method including generating an environment that interacts with a user; and controlling a strategy of the user based on a preset multi-objectives by changing the environment in response to observing a decision making of the user in the environment.

Abstract: In some implementations, a device may identify a form field, of a form, that is to be filled with text. The device may determine a formatting for the text that is to be used for filling the form field. The formatting for the text may be determined based on content that is indicative of the formatting that is to be used for filling the form field, or information relating to at least one previous filling of the form field that is associated with a successful submission of the form. The device may modify the text in accordance with the formatting that is determined to obtain formatted text. The device may cause filling of the form field with the formatted text.

Abstract: A warehouse storage and retrieval system including an array of multilevel storage racks having at least one transfer deck, picking isles and storage areas disposed along picking isles, the storage areas being configured to hold differing loads, and a controller including a management module configured to variably size the storage areas of the array of multilevel storage rack modules and assign each of the variably sized storage areas to a corresponding one of the differing loads, wherein the storage and retrieval system is arranged to transport the differing loads for placement in the variably sized storage areas assigned by the controller.

Abstract: A method and a system are provided for training a machine-learning (ML) system to function as a chatbot. According to one embodiment, a method for training and ML system includes providing to the machine-learning system: in a first iteration, a first input-output pair that includes a first input and a first output; and, in a second iteration, a second input-output pair that includes a second input and a second output, where the second input includes the first input-output pair and the second output is different from the first output, so that a context for the second input-output pair is stored in the memory of the ML system.

Abstract: Recurrent neural networks, and methods therefor, are provided with diagonal and programming fluctuation to find energy global minima. The method may include storing the matrix of weights in memory cells of a crossbar array of a recursive neural network prior to operation of the recursive neural network; altering the weights according to a probability distribution; setting the weights to non-zero values in at least one of the memory cells in a diagonal of the memory cells in the crossbar array; and operating the recursive neural network.

Abstract: Aspects of the detailed technologies concern training and use of neural networks for fine-grained classification of large numbers of items, e.g., as may be encountered in a supermarket. Mitigating false positive errors is an exemplary area of emphasis. Novel network topologies are also detailed—some employing recognition technologies in addition to neural networks. A great number of other features and arrangements are also detailed.

Abstract: Methods, systems, and computer program products for combined data pre-process and architecture search for deep learning models are provided herein. A computer-implemented method includes obtaining data corresponding to a deep learning task; performing, based on the deep learning task and the data, a multi-objective learning process to select an optimal combination of (i) a deep learning architecture for the deep learning task and (ii) a data pre-processing strategy to be applied to the data, the data pre-processing strategy comprising one or more pre-processing steps; pre-processing the data for the selected deep learning architecture based on the data pre-processing strategy; and providing the pre-processed data as input to the selected deep learning architecture to perform the deep learning task.

Abstract: Embodiments are directed to data ingestion over a network. Raw data and integrated data associated with a plurality of separate data sources may be provided such that the raw data includes content associated with a plurality of subjects. Categorization models may be employed to categorize the raw data based on various features, such as, format, structure, data source, variability, volume, or associated entities. Matching models may be determined based on the categorization of the of the raw data, the integrated data and the content associated with the plurality of subjects. Matching models may generate a plurality of unified facts based on the raw data and the integrated data such that each unified fact is associated with a score associated with a quality of its match with a unified schema.

Abstract: There are provided systems and methods for a fallback artificial intelligence (AI) system for redundancy during system failover. A service provider may provide AI systems for automated decision-making, such as for risk analysis, marketing, and the like. An AI system may operate in a production computing environment in order to provide AI decision-making based on input data, for example, by providing an output decision. In order to provide redundancy to the production AI system, the service provider may train a fallback AI system using the input/output data pairs from the production AI system. This may utilize a deep neural network and a continual learning trainer. Thereafter, when a failover condition is detected for the production AI system, the service provider may switch from the production AI system to the fallback AI system, which may provide decision-making operations during failure of within the production computing environment.

Abstract: Techniques and mechanisms for determining the value of a weight associated with a synapse of a spiking neural network. In an embodiment, a first spike train and a second spike train are output, respectively, by a first node and a second node of the spiking neural network, wherein the synapse is coupled between said nodes. The weight is applied to signaling communicated via the synapse. A value of the weight is updated based on a product of a first value and a second value, wherein the first value is based on a first rate of spiking by the first spike train, and the second value is based on a second rate of spiking by the second spike train. In another embodiment, the weight is updated based on a product of a derivative of the first rate of spiking and a derivative of the second rate of spiking.

Abstract: Methods and computer systems improve a trained base deep neural network by structurally changing the base deep neural network to create an updated deep neural network, such that the updated deep neural network has no degradation in performance relative to the base deep neural network on the training data. The updated deep neural network is subsequently training. Also, an asynchronous agent for use in a machine learning system comprises a second machine learning system ML2 that is to be trained to perform some machine learning task. The asynchronous agent further comprises a learning coach LC and an optional data selector machine learning system DS. The purpose of the data selection machine learning system DS is to make the second stage machine learning system ML2 more efficient in its learning (by selecting a set of training data that is smaller but sufficient) and/or more effective (by selecting a set of training data that is focused on an important task).

Abstract: There is described a method and system in an interactive computing environment modified with user experience values based on behavior logs. An experience valuation system determines an experience value and an estimated experience value. The experience value is based on a current state of interaction data from a user session, based on a history of past events, and an estimation function defined by parameters to model the user experience values. The estimated experience value is determined based on, in addition to the current state and the estimation function, next states associated with the current state, and a reward function. The parameters of the estimation function are updated based on a comparison of the expected experience value and the estimated experience value. For another aspect, the method and system may further include a state prediction system to determine probabilities of transitioning that may be applied to determine the estimated experience value.

Abstract: Systems and methods include obtaining a network state of a network having a plurality of nodes interconnected by a plurality of links and with services configured between the plurality of nodes on the plurality of links; utilizing a reinforcement learning engine to analyze the services and the network state to determine modifications to one or more candidate services of the services to increase a value of the network state; and, responsive to implementation of the modification to the one or more candidate services, updating the network state based thereon. The modifications can include changes to any of routing, modulation, and spectral assignment to the one or more candidate services.