System and method for externalized inferencing components

• US 20060143143A1
• Filed: 12/21/2002
• Published: 06/29/2006
• Est. Priority Date: 12/21/2002
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Chan, Hoi Yeung, Degenaro, Louis Ralph, Rouvellou, Isabelle M.
• Application Number:
  US10/537,571
• Publication Number:
  US 20060143143A1
• Time in Patent Office: Days
• US Class Current: 706/47
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/042 Backward inferencing

Abstract

A technique is provided for managing (1210) externalizable inference components. The technique allows for dynamic construction of inferences from separate components, and externalization of data for controlling dynamic constructable inferences. One key benefit realized is the capability to mix and match various externalized inference components to form new inferences; or stated in a different way, the ability to deduce new knowledge by combining (reusing) and exercising various components in new ways. Provisions are made for pluggable inference components that can be combined in many distinct ways to fit the needs of different applications. This allows inference components to be developed independently and to be highly portable.

Claims

1. A method for deploying computer infrastructure, comprising integrating computer-readable code into a computing system, wherein the code in combination with the computing system is capable of performing:

identifying inferencing aspects for a program; and

providing the identified inferencing aspects as inferencing components, wherein the inferencing components are externalizable.

2. The method of claim 1, wherein the providing step includes associating an externalized algorithm and data with each of the inferencing components.

3. The method of claim 2, wherein the data is stored in persistent memory.

4. The method of claim 1, wherein the identified inferencing aspects include at least one of a trigger point, a short term fact, an inference rule, an inference engine, a static variable mapping, a sensor, an effector, a long term fact, and a conclusion.

5. The method of claim 1, wherein the inferencing components include at least one of a trigger point component, a short term fact component, an inference rule set component, an inference engine component, a static mapping component, a sensor component, an effector component, a long term fact component, and a conclusion component.

6. The method of claim 2, wherein each of the inferencing components is one of a consumer of data provided by an inferencing component, a supplier of data provided by an inferencing component, and a combination thereof.

7. The method of claim 1, further comprising the step of associating at least one trigger point inferencing component with at least one application.

8. The method of claim 4, wherein trigger points operate either synchronously or asynchronously.

9. The method of claim 1, wherein at least one of the inferencing components is a master inferencing component that employs at least one other inferencing component.

10. The method of claim 1, wherein at least one of the inferencing components employs an inferencing engine.

11. The method of claim 1, wherein at least one of the inferencing components is organized into at least one inferencing subcomponent.

12. The method of claim 11, wherein the organization is one of an array, a collection, a hashtable, an iterator, a list, a partition, a set, a stack, a tree, a vector, and a combination thereof.

13. The method of claim 1, wherein at least one of the inferencing components is composed of at least one inferencing subcomponent.

14. The method of claim 13, wherein the composition is one of an array, a collection, a hashtable, an iterator, a list, a partition, a set, a stack, a tree, a vector, and a combination thereof.

15. The method of claim 2, wherein each of the inferencing components has at least one of an unique identifier, an intention, a name, a location, a folder, a start time, an end time, a priority, a classification, a reference, a description, a firing location, a firing parameter, and initialization parameter, an implementor, a ready flag, and free form data.

16. The method of claim 1, wherein at least one of the inferencing components is shared by reference with at least one other inferencing component.

17. The method of claim 2, wherein at least one of the algorithms perform at least one of inferencing component creation, inferencing component retrieval, inferencing component update, and inferencing component deletion.

18. The method of claim 2, wherein at least one of the algorithms is shared by a plurality of inferencing components.

20. The method of claim 1, wherein the providing step uses an inference component management facility to administer inferencing components, the administration including operations to create, retrieve, update, and delete.

21. The method of claim 1, wherein at least one of the inferencing components is composed of a plurality of inferencing subcomponents.

22. The method of claim 21, wherein the composition occurs one of statically, dynamically, and a combination thereof.

23. The method of claim 21, wherein the composition occurs using an inference component management facility.

19. The method of 2, wherein each of the algorithms is one of an execute trigger point algorithm, a return data algorithm, a join data algorithm, a filter data algorithm, a translate data algorithm, a choose by classification algorithm, a choose randomly algorithm, a choose round robin algorithm, an inference engine pre-processor, and inference engine post-processor, an inference engine launcher, a receive data algorithm, a send data algorithm, a store data algorithm, and a fetch data algorithm.

24. A system for providing externalized business logic, comprising:

an identification component configured to identify at least one point of variability within an application program; and

an externalization component for providing the identified at least one point of variability with externalized business logic, the externalized business logic including an inferencing component.

25. The system of claim 24, wherein the inferencing component includes an externalized algorithm and data.

26. The system of claim 25, further including a persistent memory component configured to persistently store the data.

27. The system of claim 24, further including an execution component for executing the externalized algorithm using at least one virtual machine.

28. The system of claim 24, wherein the inferencing component is composed of a plurality of inferencing subcomponents.

29. The system of claim 28, wherein the composition occurs dynamically.

30. The system of claim 28, wherein the composition occurs statically.

31. The system of claim 28, wherein the composition occurs in part dynamically and the remainder statically.

32. The system of claim 24, wherein the identified at least one point of variability includes at least one of a trigger point, a short term fact, an inference rule, an inference engine, a static variable mapping, a sensor, an effector, a long term fact, and a conclusion.

33. A program storage device readable by a machine, tangibly embodying a program of instructions executable on the machine to perform method steps for managing a plurality of inferencing components, the method steps comprising:

identifying inferencing aspects for a program; and

providing the identified inferencing aspects as inferencing components, wherein the inferencing components are externalizable.