Abstract: Described is a system for predicting the behavior of an autonomous system. The system identifies a taxonomic state of a motion condition of an autonomous vehicle based on a spatiotemporal location of the autonomous vehicle and elements of a driving scenario. Behavior of the autonomous vehicle is predicted based on the taxonomic state of the motion condition. The autonomous vehicle makes and implements a driving operation decision based on the predicted behavior.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving sensor data sensed from an environment associated with the vehicle; processing, by a processor, the sensor data to determine observation data, the observation data including differential features associated with an agent in the environment; determining, by the processor, a context associated with the agent based on the observation; selecting, by the processor, a first probability model associated with the context; processing, by the processor, the observation data with the selected first probability model to determine a set of predictions; processing, by the processor, the set of predictions with a second probability model to determine a final prediction of interaction behavior associated with the agent; and selectively controlling, by the processor, the vehicle based on the final prediction of interaction behavior associated with the agent.

Abstract: Described is a system for explaining how the human brain represents conceptual knowledge. A semantic model is developed, and a behavioral exam is performed to assess a calibration subject into a cohort and reveal semantic relationships to modify a personalized semantic space developed by the semantic model. Semantic features are extracted from the personalized semantic space. Neural features are extracted from neuroimaging of the human subject. A neuroceptual lattice is created having nodes representing attributes by aligning the semantic features and the neural features. Structures in the neuroceptual lattice are identified to quantify an extent to which the set of neural features represents a target concept. The identified structures are used to interpret conceptual knowledge in the brain of a test subject.

Abstract: The present application generally relates to a method and apparatus for generating an action policy for controlling an autonomous vehicle. In particular, the method is operative to receive an input indicative of a training event, segmenting the driving episode into a plurality of time steps, generate a parse tree in response to each time step, and generate a most probable parse tree from a combination of the generated parse trees.

Abstract: A system and method is taught for data processing in an autonomous vehicle control system. Using information is acquired from the vehicle, network interface, and sensors mounted on the vehicle, the system can perceive situations around it with much less complexity in computation without losing crucial details, and then make navigation and control decisions. The system and method are operative to generate situation aware events, store them, and recall to predict situations for autonomous driving.

Abstract: Described is a system for generating a semantic space based on the lexical relations between words. The system determines synonym and antonym relations between a set of words. A lexical graph is generated based on the synonym and antonym relations. Manifold embedding of the lexical graph is determined, and Laplacian coordinates of the manifold embedding are assigned as semantic features of the set of words. A quantitative representation of the set of words is generated using the semantic features.

Abstract: Described is a system for personalizing a human-machine interface (HMI) device based on a mental and physical state of a user. During performance of a task in a simulation environment, the system extracts biometric features from data collected from body sensors, and extracts brain entropy features from electroencephalogram (EEG) signals. The brain entropy features are correlated with the biometric features to generate a mental-state model. The mental-state model is deployed in a HMI device during performance of the task in an operational environment for continuous adaptation of the HMI device to its user's mental and physical states.

Abstract: The system classifies data using formal concept analysis (FCA). In a training phase, the system generates a FCA classification lattice, having a structure, using a set of training data. The set of training data comprises training presentations and classifications corresponding to the training presentations. In a classification phase, a set of test data having classes that are hierarchical in nature is classified using the structure of the FCA classification lattice.

Abstract: Described is a system for consolidation of specific memories of events. A sleep state detector assesses a subject's sleep state from neural recordings obtained from a high-density electroencephalogram (HD-EEG) device. During a memory encoding phase, a high-definition transcranial current stimulation (HD-tCS) system simultaneously applies a spatiotemporal amplitude-modulated pattern (STAMP) tag and a transcranial direct current stimulation (tDCS) signal to the subject as an event is experienced by the subject. During a memory consolidation phase, the HD-tCS system applies a transcranial alternating current stimulation (tACS) signal to the subject during a sleep or quiet waking state of the subject.

Abstract: Described is a system for modeling probability matching in human subjects. Features related to probability matching are extracted from a set of human subject responses from behavioral tasks. Neural network model instances are trained on the set of features, resulting in a set of trained neural network model instances. A set of model parameters are derived from the set of trained neural network instances, and the set of derived model parameters are used to emulate human performance on novel data.

Abstract: Described is a system for the therapeutic modification of human behavior and, more specifically, a system for the transcranial control of procedural memory reconsolidation for the purposes of enhanced skill acquisition. During operation the system records a practice pattern. The practice pattern is a recording of a sensed brain activity from a sensor array when a subject is performing a skill. The practice pattern is converted to brain activity voxels and stored as both an uncompressed practice pattern and a compressed practice pattern.

Abstract: Described is a system for determining the current state of a drill using downhole sensors. The system includes a sensor suite mounted on a drill string proximate a drill bit and a computer mounted on the drill string proximate the sensor suite. The computer includes a trained classifier and is operable for performing operations of receiving online sensor data from the sensor suite; and classifying the drill bit as being in one of a plurality of pre-trained drill states based on the online sensor data. A drill bit controller can then be used to modify the operation of the drill bit based on the drill state classification.

Abstract: Described is system and method for cognitive recognition. The system receives a multi-dimensional scene array as input data. A foveation module divides the multi-dimensional scene array into a plurality of sub-arrays and outputs contents of a currently selected sub-array. The contents are clustered with a hierarchical clustering module to generate a spatially invariant hierarchical cluster of the contents comprising a plurality of components which are based on a statistical distribution of co-occurrence of features across the currently selected sub-array. Task-relevant components are selectively gated and robustly maintained into a component memory location of a pattern bank with a working memory module with an input gating module. If the task-relevant components activate an abstract category module based on pattern matching, then a category recognition label is generated for the contents of the currently selected sub-array with an executive control module.

Abstract: Described is system and method for visual media reasoning. An input image is filtered using a first series of kernels tuned to represent objects of general categories, followed by a second series of sparse coding filter kernels tuned to represent objects of specialized categories, resulting in a set of sparse codes. Object recognition is performed on the set of sparse codes to generate object and semantic labels for the set of sparse codes. Pattern completion is performed on the object and semantic labels to recall relevant meta-data in the input image. Bi-directional feedback is used to fuse the input data with the relevant meta-data. An annotated image with information related to who is in the input image, what is in the input image, when the input image was captured, and where the input image was captured is generated.

Abstract: Described is a system for patient-specific rehabilitation that can be performed outside a clinic. The system monitors a patient in real-time to generate a quantitative assessment of a physical state and a motivational state of the patient using sensor data obtained from sensors. Predictions related to the patient are generated utilizing patient-specific biomechanical and neurocognitive models implemented with predictive simulations. Video feed of the patient is registered with sensor data and a set of simulation data. Rehabilitation guidance instructions are conveyed to the patient through dialog-based interactions.

Abstract: Described is system for modeling probability matching and loss sensitivity among human subjects. A set of features related to probability matching and loss sensitivity is extracted from collected human responses. The set of features are processed with a genetic algorithm to fit the collected human responses with a set of neural network model instances. A set of model parameters are generated from the genetic algorithm and used to generate at least one of an explanatory and predictive model of human behavior.

Abstract: Described is a system for predicting multi-agent movements. A Radon Cumulative Distribution Transform (Radon-CDT) is applied to pairs of signature-formations representing agent movements. Canonical correlation analysis (CCA) components are identified for the pairs of signature-formations. Then, a relationship between the pairs of signature formations is learned using the CCA components. A counter signature-formation for a new dataset is predicted using the learned relationship and a new signature-formation. Control parameters of a device can be adjusted based on the predicted counter signature-formation.

Abstract: Described is a system for predicting the behavior of an autonomous system. The system identifies a taxonomic state of a motion condition of an autonomous vehicle based on a spatiotemporal location of the autonomous vehicle and elements of a driving scenario. Behavior of the autonomous vehicle is predicted based on the taxonomic state of the motion condition. The autonomous vehicle makes and implements a driving operation decision based on the predicted behavior.

Abstract: Described is a system for prediction of adversary movements. In an aspect, the system includes one or more processors and a memory, the memory being a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations of computing relative positions of multiple objects of interest, generating a feature representation by forming a matrix based on the relative positions, predicting movement of the multiple objects of interest by applying clustering to the feature representation and by performing canonical correlation analysis, and controlling a device based on the predicted movement of the multiple objects of interest.

Abstract: Described is a system for implicitly predicting movement of an object. In an aspect, the system includes one or more processors and a memory, the memory being a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations of providing an image of a first trajectory to a predictive autoencoder, and using the predictive autoencoder, generating a predicted tactical response that comprises a second trajectory based on images of previous tactical responses that were used to train the predictive autoencoder, and controlling a device based on the predicted tactical response.