A model aggregation device includes a communication device able to communicate with a plurality of vehicles in which neural network models are learned, a storage device storing a part of the neural network models sent from the plurality of vehicles, and a control device. The neural network model outputs at least one output parameter from a plurality of input parameters. The control device is configured to, if receiving a new neural network model from one vehicle among the plurality of vehicles through the communication device, compare ranges of the plurality of input parameters which were used for learning the new neural network model and ranges of the plurality of input parameters which were used for learning a current neural network model stored in the storage device to thereby determine whether to replace the current neural network model with the new neural network model.

The disclosed system and method provide a way to create, update, and execute dynamic goal plans. Updating a dynamic goal plan may be based on the initial sequence of actions of the goal plan as well as the corresponding states of the actions. By using a sequence to sequence model, a goal plan can still be processed when the length of the input (initial sequence of actions) differs from the length of the output (updated sequence of actions). A sequence to sequence model can determine the interdependencies between actions that can contribute to the optimal order in which actions can efficiently be performed. A single layer neural network or clustering can be used to approximate the state of a goal plan that may be capable infinite states. This approximation improves accuracy in capturing the state of a goal plan, thereby improving accuracy in predicting the future state of a system, which can help with planning (e.g., gathering resources in advance). Projects involving collaboration between virtual and/or human assistants can greatly benefit from the ability to update a dynamic goal plan in real time.

A method for operating an administrator viewer on an administrator computing device. The method includes receiving first information pertaining to a service performed by a person for a user. The method further includes receiving an action instruction generated by an artificial intelligence engine of a cognitive intelligence platform based on a data structure associated with a user and a condition of the user, wherein the action instruction specifies that an administrator performs an action tailored for the user based on the first information. The method further includes presenting the first information and the action instruction in a screen of the administrator viewer.

This application provides an agent decision-making method and an apparatus, to improve decision-making performance of an agent. The method is applied to a communications system. The communications system includes at least two function modules. The at least two function modules include a first function module and a second function module, where the first function module is configured with a first agent, and the second function module is configured with a second agent. The method further includes the first agent obtaining related information of the second agent, and makes a decision on the first function module based on the related information of the second agent.

Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to enhance capabilities of peer devices. In an implementation, at least one agent to: identify one or more learnable capabilities enabled by one or more parameters that are accessible via receipt of one or more message at the one or more communication devices from one or more other computing devices; and determine a utility of augmenting at least one of the one or more learning engines with at least one of the one or more learnable capabilities.

In some examples, a smart assistant device may receive information associated with a previous medical treatment provided to a patient by one or more medical devices. The smart assistant device may determine, based at least in part on the received information and a medical treatment plan for the patient, an adjustment to one or more parameters of an upcoming scheduled medical treatment provided by the one or more medical devices. The smart assistant device may program or otherwise control the one or more medical devices to make the adjustment to the one or more parameters of the upcoming scheduled medical treatment provided by the one or more medical devices.

In one aspect there is provided a method. The method may include collecting one or more functions that implement the decision logic of a solution. A snapshot of the one or more functions can be generated. The snapshot can executable code associated with the one or more functions. The solution can be deployed by at least storing the snapshot of the one or more functions to a repository Systems and articles of manufacture, including computer program products, are also provided.

A method for large-scale distributed machine learning using input data comprising formal knowledge and/or training data. The method consisting of independently calculating discrete algebraic models of the input data in one or many computing devices, and in sharing indecomposable components of the algebraic models among the computing devices without constraints on when or on how many times the sharing needs to happen. The method uses an asynchronous communication among machines or computing threads, each working in the same or related learning tasks. Each computing device improves its algebraic model every time it receives new input data or the sharing from other computing devices, thereby providing a solution to the scaling-up problem of machine learning systems.

This disclosure relates generally to method and system for performing negotiation task using reinforcement learning agents. Performing negotiation on a task is a complex decision making process and to arrive at consensus on contents of a negotiation task is often expensive and time consuming due to the negotiation terms and the negotiation parties involved. The proposed technique trains reinforcement learning agents such as negotiating agent and an opposition agent. These agents are capable of performing the negotiation task on a plurality of clauses to agree on common terms between the agents involved. The system provides modelling of a selector agent on a plurality of behavioral models of a negotiating agent and the opposition agent to negotiate against each other and provides a reward signal based on the performance. This selector agent emulate human behavior provides scalability on selecting an optimal contract proposal during the performance of the negotiation task.

Various embodiments provide a data analysis apparatus, system, and method. The data analysis apparatus may collect data of a network node in a mobile access network, and then perform data analysis and adjust a configuration parameter of the network node, to implement targeted data analysis and parameter adjustment for the network node, so that a speed of data analysis and configuration parameter adjustment can be improved. In addition, modules of the data analysis apparatus may run in parallel, so that the speed of data analysis and configuration parameter adjustment can be further improved.