Hrl Laboratories, LLC

The Boeing Company

Samuel D Johnson

Tsai-Ching Lu

Aruna Jammalamadaka

Rajan Bhattacharyya

Kang-Yu Ni

David J Huber

Jiejun Xu

Deepak Khosla

Charles E Martin

Soheil Kolouri

Nigel D Stepp

Rashmi N Sundareswara

Alice A Murphy

Hyun (Tiffany) J Kim

Sean Soleyman

Steve C Slaughter

Christopher R Wezdenko

Dana M Warmsley

Christian Lebiere

Hyun J Kim

Fan H Hung

Described is a system for generating and displaying decision recommendations. The system receives historical observations and courses of action corresponding to the historical observations. A conditional density between the historical observations and the courses of action is estimated, and the historical observations are clustered according to the conditional density to obtain observational clusters. Observational clusters are merged to determine a causal partition having corresponding causal features which map to various courses of action. A new observation is mapped to the causal partition and a ranking of courses of action for the new observation is computed. A visualization of causal features and a ranking of courses of actions corresponding to the new observation is provided to a display.

Decision recommendation via causal feature display

Described is a system for performing a simulated vehicle control task based on adversarial decision analysis. Empirical game theoretic analyses are performed between an evolving and an adversary population of neural network strategies. Each empirical game theoretic analysis includes using a neuroevolution procedure to perform a fitness-based selection of a strategy in the evolving population that out-performs the adversary population. Using an empirical game theory procedure, the neuroevolution procedure is iteratively run and the selected strategy is added to the adversary population with each iteration, resulting in monotonic strategy improvement with each iteration. Following the empirical game theoretic analyses, a final strategy is selected for the evolving population and the adversary population using a tournament selection procedure. The final strategy is used to train a neural network which is used to perform a simulated vehicle control task.

Empirical game theoretic system and method for adversarial decision analysis

A method for generating human-machine hybrid predictions of answers to forecasting problems includes: parsing text of an individual forecasting problem to identify keywords; generating machine models based on the keywords; scraping data sources based on the keywords to collect scraped data relevant to the individual forecasting problem; providing the scraped data to the machine models; receiving machine predictions of answers to the individual forecasting problem from the machine models based on the scraped data; providing, by the computer system via a user interface, the scraped data to human participants; receiving, by the computer system via the user interface, human predictions of answers to the individual forecasting problem from the human participants; aggregating the machine predictions with the human predictions to generate aggregated predictions; and generating and outputting a hybrid prediction based on the aggregated predictions.

System and method for human-machine hybrid prediction of events

A method for computing a human-machine hybrid ensemble prediction includes: receiving an individual forecasting question (IFP); classifying the IFP into one of a plurality of canonical question topics; identifying machine models associated with the canonical question topic; for each of the machine models: receiving, from one of a plurality of human participants: a first task input including a selection of sets of training data; a second task input including selections of portions of the selected sets of training data; and a third task input including model parameters to configure the machine model; training the machine model in accordance with the first, second, and third task inputs; and computing a machine model forecast based on the trained machine model; computing an aggregated forecast from machine model forecasts computed by the machine models; and sending an alert in response to determining that the aggregated forecast satisfies a threshold condition.

Systems and methods for forecast alerts with programmable human-machine hybrid ensemble learning

Described is a system for continuously predicting and adapting optimal strategies for attacker elicitation. The system includes a global bot controlling processor unit and one or more local bot controlling processor units. The global bot controlling processor unit includes a multi-layer network software unit for extracting attacker features from diverse, out-of-band (OOB) media sources. The global controlling processing unit further includes an adaptive behavioral game theory (GT) software unit for determining a best strategy for eliciting identifying information from an attacker. Each local bot controlling processor unit includes a cognitive model (CM) software unit for estimating a cognitive state of the attacker and predicting attacker behavior. A generative adversarial network (GAN) software unit predicts the attacker's strategies. The global bot controlling processor unit and the one or more local bot controlling processor units coordinate to predict the attacker's next action and use the prediction to disrupt an attack.

Continuously habituating elicitation strategies for social-engineering-attacks (CHESS)

Described is a system for guiding opinions of users of a social media network. The system determines a number of control agents to be inserted into a population of users of the social media network. The system also determines a rate at which an expressed opinion of each control agent should change over a period of time. A control strategy is output which includes a control schedule based on the number of control agents to be inserted into the population of users and the rate at which the expressed opinion of each control agent should change over the period of time. Finally, the control schedule is deployed in the social media network.

Controlling a bounded confidence opinion model with a dynamic population

Described is a system for determining how opinions spread through a network. Opinion dynamics are applied to a network, each node having a corresponding opinion. Each node is described by an active state or an inactive state such that inactive nodes can update their opinions, and active nodes are fixed in their opinion at the time of activation. Inactive nodes can be influenced by both active nodes and inactive nodes. The opinion dynamics proceed in discrete time steps with an influence step for updating each inactive node's opinion, and a stochastic action step for determining whether an inactive node becomes activated. The system identifies how opinions spread through the network using the applied opinion dynamics, resulting in a set of opinion dynamics data. The opinion dynamics data is used to control information that a device or account is allowed to post to social media platform.

Model for the competitive seeding of opinions in networks with hybrid gossip-threshold dynamics

Described is a system for automated event summarization. A multi-layer network representing a multimodal data set is generated, where nodes within a given layer represent information tokens in a given modality. A topically diverse set of nodes is ranked and selected from each layer to represent temporal event highlights. Temporal event highlights are linked into storylines. Using the storylines, the system monitors a progression of an event or opinions regarding a topic. A temporal summary of the progression of the event or the opinions regarding the topic is generated.

Cross-modal event summarization system based on network of networks representations

A vehicle system prognosis apparatus including sensor(s) for detecting a characteristic of a vehicle system and generating at least one time series of condition indicator values, and a processor that receives the at least one time series and generates an analysis model, for the characteristic, that is trained with one or more of the at least one time series, that are obtained from the one or more sensors with the vehicle system operating under normal conditions, extracts from the at least one time series one or more features embodying an indication of a health of the vehicle system, generates a quantified health assessment of the vehicle system by quantifying the one or more features based on a normal distribution of the one or more features from the analysis model, and communicates the quantified health assessment of the vehicle system to an operator or crew member of the vehicle.

System and method for remaining useful life determination

Described is a system for modeling strategic network formation. A network formation model is generated using a concatenation of joint strategies s of a set of N agents, such that s=sss. The N agents include a group of source agents N, a group of aggregator agents N, and a group of consumer agents N, each group of agents having a distinct joint strategy for accessing a set of information. srepresents a joint strategy of the group of source agents, srepresents a joint strategy of the group of aggregator agents, and srepresents a joint strategy of the group of consumer agents. The network formation model is operated according to the joint strategies of the set of N agents. Data relating to the set of N agents of the network formation model is output for the investigation of the formation of information diffusion networks.

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Santa Monica, CA, United States of America

Samuel D Johnson