SENSOR BASED TRUTH MAINTENANCE METHOD AND SYSTEM

• US 20120075067A1
• Filed: 09/23/2010
• Published: 03/29/2012
• Est. Priority Date: 09/23/2010
• Status: Active Grant

• Current Assignee: Kyndryl Incorporated (Kyndryl Holdings, Inc.)
• Original Assignee: International Business Machines Corporation
• Inventors: Iqbal, Mickey, Attaluri, Prabhakar, Lawrence, Calvin D.
• Granted Patent: US 8,494,999 B2
• Time in Patent Office: Days
• US Class Current: 340/10.1
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: H04Q 2209/47 using RFID associated with sensors
• CPC Class Codes: H04Q 9/00 Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom

Abstract

A truth maintenance method and system. The method includes receiving by a computer processor from RFID tags embedded in sensors, event data associated with events detected by said sensors. The computer processor associates portions of the event data with associated RFID tags and derives assumption data associated with each portion of the portions. The computer processor retrieves previous assumption data derived from and associated with previous portions of previous event data retrieved from the RFID tags and executes non monotonic logic with respect to the assumption data and the previous assumption data. In response, the computer processor generates and stores updated assumption data associated with the assumption data and the previous assumption data.

Claims

1. A method comprising:

receiving, by a computer processor of a computing device from RFID tags embedded in sensors, first event data associated with a first plurality of events detected by said sensors, said computer processor controlling a cloud hosted mediation system comprising an inference engine software application, a truth maintenance system database, and non monotonic logic;

associating, by said computer processor, first portions of said first event data with associated RFID tags of said RFID tags;

deriving, by said computer processor executing said inference engine software application, first assumption data associated with each portion of said first portions of said first event data;

retrieving, by said computer processor from said truth maintenance system database, previous assumption data derived from and associated with previous portions of previous event data retrieved from said RFID tags embedded in said sensors, said previous assumption data derived at a time differing from a time of said deriving, said previous event data associated with previous events occurring at a different time from said first plurality of events;

executing, by said computer processor, said non monotonic logic with respect to said first assumption data and said previous assumption data;

generating, by said computer processor executing said non monotonic logic and said inference engine software application, first updated assumption data associated with said first assumption data and said previous assumption data; and

storing, by said computer processor in said truth maintenance system database, said first assumption data and said first updated assumption data.

2. The method of claim 1, further comprising:

executing, by said computer processor based on said first updated assumption data, an action associated with objects detected by said sensors.

3. The method of claim 2, wherein said action comprises implementing a pay by usage cloud metering model associated with said objects.

4. The method of claim 1, wherein said previous assumption data, said first assumption data, and said first updated assumption data each comprise assumptions associated with objects detected by said sensors.

5. The method of claim 1, further comprising:

receiving, by said computer processor from said RFID tags embedded in said sensors, second event data associated with a second plurality of events detected by said sensors, said second plurality of events occurring at a time differing from said first plurality of events;

associating, by said computer processor, first portions of said second event data with associated RFID tags of said RFID tags;

deriving, by said computer processor executing said inference engine software application, second assumption data associated with each portion of said first portions of said second event data;

retrieving, by said computer processor from said truth maintenance system database, said previous assumption data, said first assumption data, and said first updated assumption data;

executing, by said computer processor, said non monotonic logic with respect to said first updated assumption data, said first assumption data, said second assumption data, and said previous assumption data;

generating, by said computer processor executing said non monotonic logic and said inference engine software application, second updated assumption data associated with first updated assumption data, said first assumption data, said second assumption data, and said previous assumption data; and

storing, by said computer processor in said truth maintenance system database, said second assumption data.

6. The method of claim 1, wherein said generating first updated assumption data comprises retracting portions of said first assumption data and said previous assumption data.

7. The method of claim 1, wherein said non monotonic logic comprises code for enabling the Dempster Shafer theory with respect to said first assumption data and said previous assumption data.

8. The method of claim 1, further comprising:

providing at least one support service for at least one of creating, integrating, hosting, maintaining, and deploying computer-readable code in said computing system, wherein the code in combination with the computing system is capable of performing;

said receiving, said associating, said deriving, said retrieving, said executing, said generating, and said storing.

9. A computer program product, comprising a computer readable storage medium having a computer readable program code embodied therein, said computer readable program code comprising an algorithm adapted to implement a method within a computing device, said method comprising:

receiving, by a computer processor of said computing device from RFID tags embedded in sensors, first event data associated with a first plurality of events detected by said sensors, said computer processor controlling a cloud hosted mediation system comprising an inference engine software application, a truth maintenance system database, and non monotonic logic;

associating, by said computer processor, first portions of said first event data with associated RFID tags of said RFID tags;

deriving, by said computer processor executing said inference engine software application, first assumption data associated with each portion of said first portions of said first event data;

retrieving, by said computer processor from said truth maintenance system database, previous assumption data derived from and associated with previous portions of previous event data retrieved from said RFID tags embedded in said sensors, said previous assumption data derived at a time differing from a time of said deriving, said previous event data associated with previous events occurring at a different time from said first plurality of events;

executing, by said computer processor, said non monotonic logic with respect to said first assumption data and said previous assumption data;

generating, by said computer processor executing said non monotonic logic and said inference engine software application, first updated assumption data associated with said first assumption data and said previous assumption data; and

storing, by said computer processor in said truth maintenance system database, said first assumption data and said first updated assumption data.

10. The computer program product of claim 9, wherein said method further comprises:

executing, by said computer processor based on said first updated assumption data, an action associated with objects detected by said sensors.

11. The computer program product of claim 10, wherein said action comprises implementing a pay by usage cloud metering model associated with said objects.

12. The computer program product of claim 9, wherein said previous assumption data, said first assumption data, and said first updated assumption data each comprise assumptions associated with objects detected by said sensors.

13. The computer program product of claim 9, wherein said method further comprises:

receiving, by said computer processor from said RFID tags embedded in said sensors, second event data associated with a second plurality of events detected by said sensors, said second plurality of events occurring at a time differing from said first plurality of events;

associating, by said computer processor, first portions of said second event data with associated RFID tags of said RFID tags;

deriving, by said computer processor executing said inference engine software application, second assumption data associated with each portion of said first portions of said second event data;

retrieving, by said computer processor from said truth maintenance system database, said previous assumption data, said first assumption data, and said first updated assumption data;

executing, by said computer processor, said non monotonic logic with respect to said first updated assumption data, said first assumption data, said second assumption data, and said previous assumption data;

generating, by said computer processor executing said non monotonic logic and said inference engine software application, second updated assumption data associated with first updated assumption data, said first assumption data, said second assumption data, and said previous assumption data; and

storing, by said computer processor in said truth maintenance system database, said second assumption data.

14. The computer program product of claim 9, wherein said generating first updated assumption data comprises retracting portions of said first assumption data and said previous assumption data.

15. The computer program product of claim 9, wherein said non monotonic logic comprises code for enabling the Dempster Shafer theory with respect to said first assumption data and said previous assumption data.

16. A computing system comprising a computer processor coupled to a computer-readable memory unit, said memory unit comprising instructions that when enabled by the computer processor implements a method comprising:

receiving, by said computer processor from RFID tags embedded in sensors, first event data associated with a first plurality of events detected by said sensors, said computer processor controlling a cloud hosted mediation system comprising an inference engine software application, a truth maintenance system database, and non monotonic logic;

associating, by said computer processor, first portions of said first event data with associated RFID tags of said RFID tags;

deriving, by said computer processor executing said inference engine software application, first assumption data associated with each portion of said first portions of said first event data;

retrieving, by said computer processor from said truth maintenance system database, previous assumption data derived from and associated with previous portions of previous event data retrieved from said RFID tags embedded in said sensors, said previous assumption data derived at a time differing from a time of said deriving, said previous event data associated with previous events occurring at a different time from said first plurality of events;

executing, by said computer processor, said non monotonic logic with respect to said first assumption data and said previous assumption data;

generating, by said computer processor executing said non monotonic logic and said inference engine software application, first updated assumption data associated with said first assumption data and said previous assumption data; and

storing, by said computer processor in said truth maintenance system database, said first assumption data and said first updated assumption data.

17. The computing system of claim 16, wherein said method further comprises:

executing, by said computer processor based on said first updated assumption data, an action associated with objects detected by said sensors.

18. The computing system of claim 17, wherein said action comprises implementing a pay by usage cloud metering model associated with said objects.

19. The computing system of claim 16, wherein said previous assumption data, said first assumption data, and said first updated assumption data each comprise assumptions associated with objects detected by said sensors.

20. The computing system of claim 16, wherein said method further comprises:

receiving, by said computer processor from said RFID tags embedded in said sensors, second event data associated with a second plurality of events detected by said sensors, said second plurality of events occurring at a time differing from said first plurality of events;

associating, by said computer processor, first portions of said second event data with associated RFID tags of said RFID tags;

deriving, by said computer processor executing said inference engine software application, second assumption data associated with each portion of said first portions of said second event data;

retrieving, by said computer processor from said truth maintenance system database, said previous assumption data, said first assumption data, and said first updated assumption data;

executing, by said computer processor, said non monotonic logic with respect to said first updated assumption data, said first assumption data, said second assumption data, and said previous assumption data;

generating, by said computer processor executing said non monotonic logic and said inference engine software application, second updated assumption data associated with first updated assumption data, said first assumption data, said second assumption data, and said previous assumption data; and

storing, by said computer processor in said truth maintenance system database, said second assumption data.