Particle Methods for Nonlinear Control
First Claim
1. A method comprising:
- receiving, by a sensor, a current observation about a real or simulated world;
maintaining, by a computational unit, an objective;
representing the objective using an incremental cost of a plurality of potential actions;
maintaining, by the computational unit, a current uncertainty about an unknown state of the world, wherein the current uncertainly is updated using the current observation;
determining, by the computational unit, one or more optimal actions to achieve the objective with a minimum expected total future cost, wherein said determining comprises performing both backward induction on the minimum expected total future cost and forward induction on the uncertainty about the unknown state of the world, wherein said determining makes approximations based on an unstructured sample of the plurality of unknown states of the world; and
effecting, via an actuator, the one or more optimal actions.
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Abstract
Aspects herein describe new methods of determining optimal actions to achieve high-level goals with minimum total future cost. At least one high-level goal is inputted into a user device along with various observational data about the world, and a computational unit determines, through particle methods, an optimal course of action as well as emotions. The particle method comprises alternating backward and forward sweeps and tests for convergence to determine said optimal course of action. In one embodiment a user inputs a high-level goal into a cell phone which senses observational data. The cell phone communicates with a server that provides instructions. The server determines an optimal course of action via the particle method, and the cell phone then displays the instructions and emotions to the user.
18 Citations
18 Claims
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1. A method comprising:
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receiving, by a sensor, a current observation about a real or simulated world; maintaining, by a computational unit, an objective; representing the objective using an incremental cost of a plurality of potential actions; maintaining, by the computational unit, a current uncertainty about an unknown state of the world, wherein the current uncertainly is updated using the current observation; determining, by the computational unit, one or more optimal actions to achieve the objective with a minimum expected total future cost, wherein said determining comprises performing both backward induction on the minimum expected total future cost and forward induction on the uncertainty about the unknown state of the world, wherein said determining makes approximations based on an unstructured sample of the plurality of unknown states of the world; and effecting, via an actuator, the one or more optimal actions. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A method of simulating an emotional response, comprising:
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receiving, by a sensor, a current observation about a real or simulated world; maintaining, by a computational unit, an objective; representing the objective using an incremental cost of a plurality of potential actions; maintaining, by the computational unit, a current uncertainty about an unknown state of the world, wherein the current uncertainly is updated using the current observation; determining, by the computational unit, one or more optimal actions to achieve the objective with a minimum expected total future cost; determining, via the computational unit, a significant statistic of the computation determining the optimal actions, corresponding to a simulated emotion; and communicating, via an actuator, a representation of the emotion to the world. - View Dependent Claims (15, 16, 17)
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18. A system comprising:
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a sensor configured to receive input defining a current observation about a real or simulated world; a computational unit configured to; maintain an objective, represent the objective using an incremental cost of a plurality of potential actions, maintain a current uncertainty about an unknown state of the world, wherein the current uncertainly is updated using the current observation, and determine one or more optimal actions to achieve the objective with a minimum expected total future cost, wherein said determining comprises performing both backward induction on the minimum expected total future cost and forward induction on the uncertainty about the unknown state of the world, wherein said determining makes approximations based on an unstructured sample of the plurality of unknown states of the world; and an actuator configured to effect the one or more optimal actions based on instructions from the computational unit.
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Specification