A system for providing a situationally aware social agent includes processing hardware and a memory storing a software code. The processing hardware executes the software code to receive radar data and audio data, process the radar data and the audio data to obtain radar-based location data and audio-based location data each corresponding to a location of one or more user(s), and process the radar data and the audio data to obtain radar-based venue data and audio-based venue data each corresponding to an environment surrounding the user(s). The software code further determines, using the radar-based location data and the audio-based location data, the location of the user(s), determines, using the radar-based venue data and the microphone-based venue data, the environment surrounding the user(s), and identifies, based on the location and the environment, an interactive expression for use by the situationally aware social agent to interact with the user(s).

Systems and methods are presented for immersive and simultaneous animation in a mixed reality environment. Techniques disclosed represent a physical object, present at a scene, in a 3D space of a virtual environment associated with the scene. A virtual element is posed relative to the representation of the physical object in the virtual environment. The virtual element is displayed to users from a perspective of each user in the virtual environment. Responsive to an interaction of one user with the virtual element, an edit command is generated and the pose of the virtual element is adjusted in the virtual environment according to the edit command. The display of the virtual element to the users is then updated according to the adjusted pose. When simultaneous and conflicting edit commands are generated by collaborating users, policies to reconcile the conflicting edit commands are disclosed.

The present invention relates to a computer implemented system for animating a virtual object or digital entity. It has particular relevance to animation using biologically based models, or behavioural models particularly neurobehavioural models. There is provided a plurality of modules having a computational element and a graphical element. The modules are arranged in a required structure and have at least one variable and being associated with at least one connector. The connectors link variables between modules across the structure, and the modules together provide a neurobehavioural model. There is also provided a method of controlling a digital entity in response to an external stimulus.

An artificial intelligence system includes a first information processing module generating data related to a language based on social data of a first user registered in one or a plurality of social network services, and a second information processing module generating data related to an image based on social data of the first user registered in one or a plurality of social network services. The first information processing module and the second information processing module generate a virtual first user on a computer with respect to the first user.

An artificial intelligence system includes a first information processing module generating data related to a language based on social data of a first user registered in one or a plurality of social network services, and a second information processing module generating data related to an image based on social data of the first user registered in one or a plurality of social network services. The first information processing module and the second information processing module generate a virtual first user on a computer with respect to the first user.

An architecture for training the weights of artificial neural networks provides a global constrainer modifying the neuron weights in each iteration not only by the back-propagated error but also by a global constraint constraining these weights based on the value of all weights at that iteration. The ability to accommodate a global constraint is made practical by using a constrained gradient descent which approximates the error gradient deduced in the training as a plane, offsetting the increased complexity of the global constraint.

A vehicle can capture data that can be converted into a synthetic scenario for use in a simulator. Objects can be identified in the data and attribute data associated with the objects can be determined. Updated attribute data may be determined based on confidence values and/or distance measurements associated with the attribute data. The object and attribute data may be used to generate synthetic scenarios of a simulated environment, including simulated objects that traverse the environment and perform actions based on the attribute data associated with the simulated objects, the captured data, and/or interactions within the simulated environment. The scenarios can be used for testing and validating interactions and responses of a vehicle controller within the simulated environment.

A method for recommending a drilling target of a new well based on cognitive computing is provided, including: establishing a reservoir geological model; acquiring a dynamic parameter and a static parameter; establishing multiple fuzzy rules bases; inputting the dynamic and static parameters into the fuzzy rules base to obtain aggregated output fuzzy sets of membership values; defuzzifying the fuzzy set of the membership values to obtain crisp values of the fuzzy variables; inputting the crisp values into the fuzzy rules base to obtain a aggregated output fuzzy set of DA membership values of drilling attractiveness DA as a fuzzy variable; defuzzifying the DA to obtain a score of the DA; establishing a drilling attractiveness region with a radius R by taking each grid as a center; calculating region drilling attractiveness RDA score of the region; and determining a region with a highest score as the location of the new well.

A multi-agent simulation system performs a simulation of a target world in which a plurality of agents interacting with each other exist. The multi-agent simulation system includes: a plurality of agent simulators configured to perform simulations of the plurality of agents, respectively; and a center controller configured to communicate with the plurality of agent simulators. Operation modes of the center controller include: a first mode that does not perform message filtering; and a second mode that performs the message filtering. When the number of messages that the center controller receives per unit time is equal to or less than a threshold, the center controller selects the first mode. On the other hand, when the number of result messages that the center controller receives per unit time exceeds the threshold, the center controller selects the second mode.

A learning agent is disclosed that receives data in sequence from one or more sequential data sources; generates a model modelling sequences of data and actions; and selects an action maximizing the expected future value of a reward function, wherein the reward function depends at least partly on at least one of: a measure of the change in complexity of the model, or a measure of the complexity of the change in the model. The measure of the change in complexity of the model may be based on, for example, the change in description length of the first part of a two-part code describing one or more sequences of received data and actions, the change in description length of a statistical distribution modelling, the description length of the change in the first part of the two-part code, or the description length of the change in the statistical distribution modelling.