Abstract: Navigation based on internal state inference and interactivity estimation may include training a policy for autonomous navigation by extracting spatio-temporal features from one or more historical observations of one or more agents within a simulation environment including an ego-agent, analyzing the spatio-temporal features to infer one or more internal states of one or more of the agents, predicting one or more future behaviors for one or more of the one or more of the agents in a first scenario including an existence of the ego-agent within the simulation environment and in a second scenario excluding the existence of the ego-agent within the simulation environment, and calculating one or more interactivity scores for one or more of the agents based on a difference between the first scenario and the second scenario. The trained policy may be implemented to control an autonomous vehicle.

Abstract: Systems and methods for path planning with latent state inference and spatial-temporal relationships are provided. A system includes an inference module, a policy module, a graphical representation module, and a planning module. The inference module receives sensor data associated with a plurality of agents. The inference module also maps the sensor data to a latent state distribution to identify latent states of the plurality of agents. The latent states identify agents of the plurality of agents as cooperative or aggressive. The policy module predicts future trajectories of the plurality of agents at a given time based on sensor data and the latent states of the plurality of agents. The graphical representation module generates a graphical representation based on the sensor data and a graphical representation neural network. The planning module generates a motion plan for the ego agent based on the predicted future trajectories and the graphical representation.

Abstract: Systems and methods for path planning with latent state inference and spatial-temporal relationships are provided. In one embodiment, a system includes an inference module, a policy module, a graphical representation module, and a planning module. The inference module receives sensor data associated with a plurality of agents. The inference module maps the sensor data to a latent state distribution to identify latent states of the plurality of agents. The latent states identify agents as cooperative or aggressive. The policy module predicts future trajectories of the plurality of agents at a given time based on sensor data and the latent states of the plurality of agents. The graphical representation module generates a graphical representation based on the sensor data and a graphical representation neural network. The planning module generates a motion plan for the ego agent based on the predicted future trajectories and the graphical representation.

Abstract: Embodiments described herein provide a timeline-based decumulation management mechanism for wealth management in retirement. The mechanism incorporates a plurality of factors and constraints including a desired consumption and bequeath, investment in qualified and nonqualified accounts, account disbursement and conversion rules, income and capital gains taxes, tracking of tax lots, estate tax rules such as basis step-up, Social Security, pensions, real estate, mortgages, SPIA and deferred income annuities, term life insurance, and alternatives. Specifically, the mechanism translates various account information into one or more convex constraints over a consumption variable, an income variable, a taxable income variable, and a bequest variable at the current year. Subject to these convex constraints, the mechanism is able to formulate a convex optimization problem that maximizes a utility representing the lifetime expectation of assets.

Abstract: A system and method for utilizing a recursive reasoning graph in multi-agent reinforcement learning that includes receiving data associated with an ego agent and a target agent that are traveling within a multi-agent environment and utilizing a multi-agent central actor-critic framework to analyze the data associated with the ego agent and the target agent. The system and method also include performing level-k recursive reasoning based on the multi-agent actor-critic framework to calculate higher level recursion actions of the ego agent and the target agent. The system and method further include controlling at least one of: the ego agent and the target agent to operate within the multi-agent environment based on at least one of: an agent action policy that is associated with the ego agent and an agent action policy that is associated with the target agent.

Abstract: Systems and methods for path planning with latent state inference and spatial-temporal relationships are provided. In one embodiment, a system includes an inference module, a policy module, a graphical representation module, and a planning module. The inference module receives sensor data associated with a plurality of agents. The inference module also maps the sensor data to a latent state distribution to identify latent states of the plurality of agents. The latent states identify agents of the plurality of agents as cooperative or aggressive. The policy module predicts future trajectories of the plurality of agents at a given time based on sensor data and the latent states of the plurality of agents. The graphical representation module generates a graphical representation based on the sensor data and a graphical representation neural network. The planning module generates a motion plan for the ego agent based on the predicted future trajectories and the graphical representation.

Abstract: Systems and methods are presented that enable logical reasoning even in the presence of noisy (inconsistent) data. The knowledge base is processed in order to make it consistent and is also compiled. This processing includes checking and correcting spelling, removing stopwords, performing, grouping words of similar and related meaning, and compacting the knowledge base. A robot can use the processed knowledge base to perform many different types of tasks, such as answering a query, determining a course of action that is designed to achieve a particular goal, and determining its own location.