SYSTEMS AND METHODS FOR PLANNING

• US 20080275743A1
• Filed: 05/03/2007
• Published: 11/06/2008
• Est. Priority Date: 05/03/2007
• Status: Abandoned Application

• Current Assignee: Raytheon Company (Rtx Corporation)
• Original Assignee: Raytheon Company (Rtx Corporation)
• Inventors: Kadambe, Shubha L.
• Application Number:
  US11/743,692
• Publication Number:
  US 20080275743A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change

Abstract

A computer-implemented method of identifying a preferred plan includes modeling one or more plans, each plan having a respective plurality of states and a respective plurality of transitions between the states, generating a respective state transition probability matrix associated with each one of the one or more plans, and generating a respective observation probability matrix associated with each one of the one or more plans. A respective plurality of state histories is identified and a respective quality value for is computed for each state history. A respective expected value is computed for each plan. A preferred plan is identified in accordance with the expected values. A preferred state history is identified in accordance with the quality values. A computer-readable storage medium and a system having a computer-readable storage medium are also provided, each of which is encoded with instructions for performing the method.

Claims

1. A computer-implemented method of identifying a preferred plan, comprising:

modeling one or more plans, each plan having a respective plurality of states and a respective plurality of transitions between the states;

generating a respective state transition probability matrix associated with each one of the one or more plans, each respective state transition probability matrix having respective state transition probability matrix values, each state transition probability matrix value corresponding to a respective probability that performing a respective action will result in a respective state transition;

generating a respective observation probability matrix associated with each one of the one or more plans, each respective observation probability matrix having respective observation probability matrix values, each observation probability matrix value corresponding to a respective probability of obtaining a respective observation in response to a respective action;

identifying a respective plurality of state histories associated with each one of the one or more plans, each state history having a respective plurality of states;

computing a respective quality value for each state history of the plurality of state histories;

computing a respective expected value for each plan of the one or more plans; and

identifying at least one of a preferred plan from among the one or more plans in accordance with the expected values of each plan of the one or more plans, or a preferred state history from among the respective plurality of state histories within the identified preferred plan, wherein the preferred state history is identified in accordance with the quality values.

2. The computer-implemented method of claim 1, wherein the generating a respective quality value for each state history of the plurality of state histories comprises:

identifying a respective reward value associated with each state for a respective one state history of the plurality of state histories;

identifying a respective cost value associated with each action, each action associated with a respective state for the respective one state history of the plurality of state histories; and

combining respective cost values and respective reward values for the respective one state history of the plurality of state histories to generate a respective quality value for the respective one state history of the plurality of state histories.

3. The computer-implemented method of claim 2, wherein the combining respective cost values and respective reward values comprises:

subtracting a cost value from a reward value to generate a state difference value for each state in the respective one state history of the plurality of state histories; and

summing the state difference values for each state in the respective one state history of the plurality of state histories.

4. The computer-implemented method of claim 1, wherein the generating a respective expected value for each plan of the one or more plans comprises combining respective quality values, respective state transition probability values, and respective observation probability values for each state history of the plurality of state histories associated with a respective one of the one or more plans.

5. The computer-implemented method of claim 4, wherein the combining respective quality values, respective state transition probability values, and respective observation probability values comprises:

calculating a respective probability of state history for each state history of the plurality of state histories associated with the respective one of the one or more plans;

multiplying each respective probability of state history by a respective quality value for each state history of the plurality of state histories associated with the respective one of the one or more plans to provide a respective state history product value for each state history of the plurality of state histories associated with the respective one of the one or more plans; and

summing the respective state history product values for each state history of the plurality of state histories associated with the respective one of the one or more plans.

6. The computer-implemented method of claim 5, wherein the generating a respective probability of state history comprises:

multiplying a state transition probability value associated with a selected state within a selected state history from among the plurality of state histories associated with the respective one of the one or more plans by an observation probability associated with a selected action associated with the selected state and with the selected state history from among the plurality of state histories associated with the respective one of the one or more plans to provide a probability product value; and

.

summing the probability product values for each state and each action associated with the selected state history.

7. The computer-implemented method of claim 1, further comprising:

updating at least one of the state transition probability values or at least one of the observation probability values.

8. The computer-implemented method of claim 7, wherein the updated state transition probability values or the updated observation probability value are generated using a respective past state transition probability value or a respective past observation probability value.

9. The computer-implemented method of claim 1, wherein the one or more plans correspond to real-world plans.

10. The computer-implemented method of claim 9, wherein the real-world plans comprise at least one of a construction plan, a vehicle movement plan, an equipment movement plan, or a personnel movement plan.

11. The computer-implemented method of claim 10, wherein the vehicle movement plan comprises a plan for moving a vehicle from a starting location to an ending location resulting in movement of at least one person within the vehicle from the starting location to the ending location, wherein the vehicle comprises a selected one of a ship, an automobile, a truck, or an airplane.

12. The computer-implemented method of claim 10, wherein the equipment movement plan comprises a plan for moving equipment from a starting location to an ending location.

13. The computer-implemented method of claim 10, wherein the personnel movement plan comprises a plan for moving at least one person from a starting location to an ending location.

14. A computer-readable storage medium encoded with computer-readable code, comprising instructions for:

modeling one or more plans, each plan having a respective plurality of states and a respective plurality of transitions between the states;

generating a respective state transition probability matrix associated with each one of the one or more plans, each respective state transition probability matrix having respective state transition probability matrix values, each state transition probability matrix value corresponding to a respective probability that performing a respective action will result in a respective state transition;

generating a respective observation probability matrix associated with each one of the one or more plans, each respective observation probability matrix having respective observation probability matrix values, each observation probability matrix value corresponding to a respective probability of obtaining a respective observation in response to a respective action;

identifying a respective plurality of state histories associated with each one of the one or more plans, each state history having a respective plurality of states;

computing a respective quality value for each state history of the plurality of state histories;

computing a respective expected value for each plan of the one or more plans; and

identifying at least one more of a preferred plan from among the one or more plans in accordance with the expected values of each plan of the one or more plans, or a preferred state history from among the respective plurality of state histories within the identified preferred plan, wherein the preferred state history is identified in accordance with the quality values.

15. The computer-readable storage medium of claim 14, wherein the instructions for generating a respective quality value for each state history of the plurality of state histories comprise instructions for:

identifying a respective reward value associated with each state for a respective one state history of the plurality of state histories;

identifying a respective cost value associated with each action, each action associated with a respective state for the respective one state history of the plurality of state histories; and

combining respective cost values and respective reward values for the respective one state history of the plurality of state histories to generate a respective quality value for the respective one state history of the plurality of state histories.

16. The computer-readable storage medium of claim 15, wherein the instructions for combining respective cost values and respective reward values comprise instructions for:

subtracting a cost value from a reward value to generate a state difference value for each state in the respective one state history of the plurality of state histories; and

summing the state difference values for each state in the respective one state history of the plurality of state histories.

17. The computer-readable storage medium of claim 14, wherein the instructions for generating a respective expected value for each plan of the one or more plans comprise instructions for combining respective quality values, respective state transition probability values, and respective observation probability values for each state history of the plurality of state histories associated with a respective one of the one or more plans.

18. The computer-readable storage medium of claim 17, wherein the instructions for combining respective quality values, respective state transition probability values, and respective observation probability values comprise instructions for:

computing a respective probability of state history for each state history of the plurality of state histories associated with the respective one of the one or more plans;

multiplying each respective probability of state history by a respective quality value for each state history of the plurality of state histories associated with the respective one of the one or more plans to provide a respective state history product value for each state history of the plurality of state histories associated with the respective one of the one or more plans; and

summing the respective state history product values for each state history of the plurality of state histories associated with the respective one of the one or more plans.

19. The computer-readable storage medium of claim 18, wherein the instructions for generating a respective probability of state history comprise instructions for:

multiplying a state transition probability value associated with a selected state within a selected state history from among the plurality of state histories associated with the respective one of the one or more plans by an observation probability associated with a selected action associated with the selected state and with the selected state history from among the plurality of state histories associated with the respective one of the one or more plans to provide a probability product value; and

.

summing the probability product values for each state and each action associated with the selected state history.

20. The computer-readable storage medium of claim 14, further comprising instructions for:

updating at least one of the state transition probability values or at least one of the observation probability values.

21. The computer-readable storage medium of claim 20, wherein the updated state transition probability values or the updated observation probability value are generated using a respective past state transition probability value or a respective past observation probability value.

22. The computer-readable storage medium of claim 14, wherein the one or more plans correspond to real-world plans, wherein the real-world plans comprise at least one of a construction plan, a vehicle movement plan, an equipment movement plan, or a personnel movement plan.

23. A system, comprising:

a computer processor; and

a computer-readable memory coupled to the computer processor, wherein the computer-readable memory is encoded with computer-readable code, the computer-readable code comprising instructions for;

modeling one or more plans, each plan having a respective plurality of states and a respective plurality of transitions between the states;

generating a respective state transition probability matrix associated with each one of the one or more plans, each respective state transition probability matrix having respective state transition probability matrix values, each state transition probability matrix value corresponding to a respective probability that performing a respective action will result in a respective state transition;

generating a respective observation probability matrix associated with each one of the one or more plans, each respective observation probability matrix having respective observation probability matrix values, each observation probability matrix value corresponding to a respective probability of obtaining a respective observation in response to a respective action;

identifying a respective plurality of state histories associated with each one of the one or more plans, each state history having a respective plurality of states;

computing a respective quality value for each state history of the plurality of state histories;

computing a respective expected value for each plan of the one or more plans; and

identifying at least one of a preferred plan from among the one or more plans in accordance with the expected values of each plan of the one or more plans, or a preferred state history from among the respective plurality of state histories within the identified preferred plan, wherein the preferred state history is identified in accordance with the quality values.

24. The system of claim 23, wherein the instructions for generating a respective quality value for each state history of the plurality of state histories comprise instructions for:

identifying a respective reward value associated with each state for a respective one state history of the plurality of state histories;

identifying a respective cost value associated with each action, each action associated with a respective state for the respective one state history of the plurality of state histories; and

combining respective cost values and respective reward values for the respective one state history of the plurality of state histories to generate a respective quality value for the respective one state history of the plurality of state histories.

25. The system of claim 24, wherein the instructions for combining respective cost values and respective reward values comprise instructions for:

subtracting a cost value from a reward value to generate a state difference value for each state in the respective one state history of the plurality of state histories; and

summing the state difference values for each state in the respective one state history of the plurality of state histories.

26. The system of claim 23, wherein the instructions for generating a respective expected value for each plan of the one or more plans comprise instructions for combining respective quality values, respective state transition probability values, and respective observation probability values for each state history of the plurality of state histories associated with a respective one of the one or more plans.

27. The system of claim 26, wherein the instructions for combining respective quality values, respective state transition probability values, and respective observation probability values comprise instructions for:

computing a respective probability of state history for each state history of the plurality of state histories associated with the respective one of the one or more plans;

multiplying each respective probability of state history by a respective quality value for each state history of the plurality of state histories associated with the respective one of the one or more plans to provide a respective state history product value for each state history of the plurality of state histories associated with the respective one of the one or more plans; and

summing the respective state history product values for each state history of the plurality of state histories associated with the respective one of the one or more plans.

28. The system of claim 27, wherein the instructions for generating a respective probability of state history comprise instructions for:

multiplying a state transition probability value associated with a selected state within a selected state history from among the plurality of state histories associated with the respective one of the one or more plans by an observation probability associated with a selected action associated with the selected state and with the selected state history from among the plurality of state histories associated with the respective one of the one or more plans to provide a probability product value; and

.

summing the probability product values for each state and each action associated with the selected state history.

29. The system of claim 23, wherein the computer-readable code further comprises instructions for:

updating at least one of the state transition probability values or at least one of the observation probability values.

30. The system of claim 29, wherein the updated state transition probability values or the updated observation probability value are generated using a respective past state transition probability value or a respective past observation probability value.

31. The system of claim 23, wherein the one or more plans correspond to real-world plans, wherein the real-world plans comprise at least one of a construction plan, a vehicle movement plan, an equipment movement plan, or a personnel movement plan.