Self-Assembling Learning System and Apparatus

• US 20160342907A1
• Filed: 08/07/2016
• Published: 11/24/2016
• Est. Priority Date: 01/10/2006
• Status: Active Grant

• Current Assignee: Manyworlds Incorporated
• Original Assignee: Manyworlds Incorporated
• Inventors: Flinn, Steven Dennis, Moneypenny, Naomi Felina
• Granted Patent: US 10,062,037 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/045 Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A self-assembling learning system and apparatus determines self-assembling actions that are expected to reduce uncertainties that are embodied as probabilities and then updates the probabilities in accordance with information that results from the performing of the self-assembling actions. The updated probabilities inform the determination of subsequent self-assembling actions. Neural networks, simulations of multiple potential self-assembling actions, and expected values of information may be applied in determining the self-assembling actions that are to be performed. Sensors may be applied to receive information that inform the updating of probabilities, and the self-assembling apparatus may be a robotic device. Self-assembling actions may comprise modifications to relationships between elements of a computer-implemented system.

Claims

1. A computer-implemented method comprising:

determining automatically a first self-assembling action that is expected to result in a reduction of an uncertainty that is embodied by one or more probabilities;

performing automatically the first self-assembling action;

updating automatically the one or more probabilities based upon an assessment of information that results from the performing of the first self-assembling action;

determining automatically a second self-assembling action in accordance with the updated one or more probabilities; and

performing automatically the second self-assembling action.

2. The method of claim 1, further comprising:

determining automatically the first self-assembling action, wherein the first self-assembling action is determined by application of a computer-implemented neural network.

3. The method of claim 1, further comprising:

determining automatically the second self-assembling action, wherein the second self-assembling action is determined by application of a computer-implemented neural network.

4. The method of claim 1, further comprising:

performing automatically the first self-assembling action, wherein the first self-assembling action comprises modifying a relationship between two elements of a computer-implemented system.

5. The method of claim 1, further comprising:

updating automatically the one or more probabilities based upon the assessment of the information that results from the first self-assembling action, wherein the assessment of the information comprises performing an inference from user behavioral information associated with use of the computer-implemented system.

6. The method of claim 1, further comprising:

determining automatically the second self-assembling action in accordance with the updated one or more probabilities and an expected value of information.

7. The method of claim 6, further comprising:

determining automatically the second self-assembling action in accordance with the updated one or more probabilities, the expected value of information, and an expected cost.

8. A system comprising one or more processor-based devices configured to:

determine a first self-assembling action that is expected to result in a reduction of an uncertainty that is embodied by one or more probabilities;

perform the first self-assembling action;

update the one or more probabilities based upon an assessment of information that results from the performing of the first self-assembling action;

determine automatically a second self-assembling action in accordance with the updated one or more probabilities; and

perform automatically the second self-assembling action.

9. The system of claim 8, further comprising the one or more processor-based devices configured to:

determine the first self-assembling action, wherein the first self-assembling action is determined by application of a computer-implemented neural network.

10. The system of claim 9, further comprising the one or more processor-based devices configured to:

determine the second self-assembling action, wherein the second self-assembling action is determined by application of a computer-implemented neural network.

11. The system of claim 8, further comprising the one or more processor-based devices configured to:

determine the first self-assembling action, wherein the first self-assembling action is determined by simulating a plurality of self-assembling actions and an expected associated result of each of the plurality of self-assembling actions.

12. The system of claim 8, further comprising the one or more processor-based devices configured to:

perform the first self-assembling action, wherein the first self-assembling action comprises modifying a relationship between two elements of the one or more processor-based devices.

13. The system of claim 12, further comprising the one or more processor-based devices configured to:

update the one or more probabilities based upon the assessment of the information that results from the performing of the first self-assembling action, wherein the assessment of the result comprises performing an inference from user behavioral information associated with use of the one or more processor-based devices.

14. The system of claim 8, further comprising the one or more processor-based devices configured to:

determine the second self-assembling action in accordance with the updated one or more probabilities and an expected value of information.

15. The system of claim 14, further comprising the one or more processor-based devices configured to:

determine the second self-assembling action in accordance with the updated one or more probabilities, the expected value of information, and an expected cost.

16. An apparatus comprising:

one or more sensors;

one or more processors configured to;

determine a first self-assembling action that is expected to result in a reduction of an uncertainty that is embodied by one or more probabilities;

perform the first self-assembling action;

update the one or more probabilities by applying the one or more sensors to gather information that results from the performing of the first self-assembling action;

determine automatically a second self-assembling action in accordance with the updated one or more probabilities; and

perform automatically the second self-assembling action.

17. The apparatus of claim 16, wherein the apparatus comprises a robotic device.

18. The apparatus of claim 16, further comprising the one or more processors configured to:

determine the first self-assembling action, wherein the first self-assembling action is determined by application of a computer-implemented neural network.

19. The apparatus of claim 16, further comprising the one or more processors configured to:

determine the first self-assembling action, wherein first self-assembling action is determined by simulating a plurality of self-assembling actions and an expected associated result of each of the plurality of self-assembling actions.

20. The apparatus of claim 16, further comprising the one or more processors configured to:

determine the second self-assembling action, wherein the second self-assembling action is determined in accordance with the updated one or more probabilities and a value of information.