COMPOSITE TASK EXECUTION

• US 20190324795A1
• Filed: 04/24/2018
• Published: 10/24/2019
• Est. Priority Date: 04/24/2018
• Status: Active Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: GAO, Jianfeng, LI, Xiujun, LI, Lihong, TANG, Da, WANG, Chong, JEBARA, Tony
• Application Number:
  US15/960,809
• Publication Number:
  US 20190324795A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/90332 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 2209/5017 Task decomposition
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06F 9/4843 by program, e.g. task dispatcher, supervisor, operating system
• CPC Class Codes: G06F 9/5066 Algorithms for mapping a plurality of inter-dependent sub-tasks onto a plurality of physical CPUs mappping at compile time, see G06F8/451
• CPC Class Codes: G06N 3/006 based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 2015/223 Execution procedure of a spoken command
• CPC Class Codes: Y02D 10/00 Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

A system for executing composite tasks can include a processor to detect a composite task from a user. The processor can also detect a plurality of subtasks corresponding to the composite task based on unsupervised data without a label, wherein the plurality of subtasks are identified by a top-level dialog policy. The processor can also detect a plurality of actions, wherein each action is to complete one of the subtasks, and wherein each action is identified by a low-level dialog policy corresponding to the subtasks identified by the top-level dialog policy. The processor can also update a dialog manager based on a completion of each action corresponding to the subtasks and execute instructions based on a policy identified by the dialog manager, wherein the executed instructions implement the policy with a lowest global cost corresponding to the composite task provided by the user.

Claims

1. A system for executing composite tasks based on computational learning techniques comprising:

a processor to;

detect a composite task from a user;

detect a plurality of subtasks corresponding to the composite task based on unsupervised data without a label, wherein the plurality of subtasks are identified by a top-level dialog policy;

detect a plurality of actions, wherein each action is to complete one of the subtasks, and wherein each action is identified by a low-level dialog policy corresponding to the subtasks identified by the top-level dialog policy;

update a dialog manager based on a completion of each action corresponding to the subtasks, wherein the dialog manager stores an intrinsic value indicating a sub-cost to execute each action corresponding to each subtask, and an extrinsic value indicating a global cost to execute a plurality of actions that perform the composite task; and

execute instructions based on a policy identified by the dialog manager, wherein the executed instructions implement the policy with a lowest global cost corresponding to the composite task provided by the user.

2. The system of claim 1, wherein the action is a multi-step action.

3. The system of claim 2, wherein the processor is to detect a number of the plurality of subtasks based on a predetermined upper limit on a maximum number of allowed segmentations.

4. The system of claim 1, wherein the processor is to select each action corresponding to each subtask based on the extrinsic value corresponding to previous identified actions executed in previous states.

5. The system of claim 1, wherein the processor is to:

calculate a probability that each of the subtasks is to output a termination symbol; and

terminate at least one of the subtasks in response to detecting the probability of outputting the termination symbol is above a threshold value.

6. The system of claim 1, wherein the processor is to determine an order of the subtasks based on temporal constraints for each of the subtasks.

7. The system of claim 1, wherein the processor is to generate a first neural network for the high level dialog and a second neural network for the low level dialog.

8. The system of claim 1, wherein the processor is to detect the composite task from a natural language dialog request.

9. The system of claim 8, wherein the plurality of actions comprise transmitting data to a plurality of databases corresponding to the subtasks of the composite task.

10. A method for executing composite tasks based on computational learning techniques comprising:

detecting a composite task from a user;

detecting a plurality of subtasks corresponding to the composite task based on unsupervised data without a label, wherein the plurality of subtasks are identified by a top-level dialog policy;

detecting a plurality of actions, wherein each action is to complete one of the subtasks, and wherein each action is identified by a low-level dialog policy corresponding to the subtasks identified by the top-level dialog policy;

updating a dialog manager based on a completion of each action corresponding to the subtasks, wherein the dialog manager stores an intrinsic value indicating a sub-cost to execute each action corresponding to each subtask, and an extrinsic value indicating a global cost to execute a plurality of actions that perform the composite task; and

executing instructions based on a policy identified by the dialog manager, wherein the executed instructions implement the policy with a lowest global cost corresponding to the composite task provided by the user.

11. The method of claim 10, wherein the action is a multi-step action.

12. The method of claim 10, further comprising detecting a number of the plurality of subtasks based on a predetermined upper limit on a maximum number of allowed segmentations.

13. The method of claim 10, further comprising selecting each action corresponding to each subtask based on the extrinsic value corresponding to previous identified actions executed in previous states.

14. The method of claim 10, further comprising:

calculating a probability that each of the subtasks is to output a termination symbol; and

terminating at least one of the subtasks in response to detecting the probability of outputting the termination symbol is above a threshold value.

15. The method of claim 10, further comprising determining an order of the subtasks based on temporal constraints for each of the subtasks.

16. The method of claim 10, further comprising generating a first neural network for the high level dialog and a second neural network for the low level dialog.

17. The method of claim 10, further comprising detecting the composite task from a natural language dialog request.

18. The method of claim 17, wherein the plurality of actions comprise transmitting data to a plurality of databases corresponding to the subtasks of the composite task.

19. One or more computer-readable storage media for executing composite tasks based on computational learning techniques comprising a plurality of instructions that, in response to execution by a processor, cause the processor to:

detect a composite task from a user;

detect a plurality of subtasks corresponding to the composite task based on unsupervised data without a label, wherein the plurality of subtasks are identified by a top-level dialog policy;

detect a plurality of actions, wherein each action is to complete one of the subtasks, and wherein each action is identified by a low-level dialog policy corresponding to the subtasks identified by the top-level dialog policy;

update a dialog manager based on a completion of each action corresponding to the subtasks, wherein the dialog manager stores an intrinsic value indicating a sub-cost to execute each action corresponding to each subtask, and an extrinsic value indicating a global cost to execute a plurality of actions that perform the composite task; and

execute instructions based on a policy identified by the dialog manager, wherein the executed instructions implement the policy with a lowest global cost corresponding to the composite task provided by the user.

20. The one or more computer-readable storage media of claim 19, wherein the processor is to detect a number of the plurality of subtasks based on a predetermined upper limit on a maximum number of allowed segmentations.