ADAPTIVE KNOWLEDGE-BASED REASONING IN AUTONOMIC COMPUTING SYSTEMS

• US 20090327172A1
• Filed: 06/27/2008
• Published: 12/31/2009
• Est. Priority Date: 06/27/2008
• Status: Abandoned Application

• Current Assignee: Motorola Solutions, Inc.
• Original Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Inventors: Zhang, Jing, Jiang, Michael Zhihe Z., Liu, Yan, Strassner, John C.
• Application Number:
  US12/163,295
• Publication Number:
  US 20090327172A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A method, information processing system, and network select machine learning algorithms for managing autonomous operations of network elements. A state (404) of at least one problem (406) and at least one context associated with the problem are received as input. A machine learning algorithm (118) is selected (410) based on the problem and context of the problem that have been received. The machine learning algorithm (118) that has been selected is outputted to an autonomic controller.

Claims

1. A method for selection of a machine learning algorithm, the method comprising:

receiving as an input a state of at least one problem and at least one context associated with the problem;

selecting a machine learning algorithm based on the problem and context of the problem that have been received; and

outputting the machine learning algorithm that has been selected to an autonomic controller.

2. The method of claim 1, wherein selecting a machine learning algorithm, further comprises:

performing reinforcement learning with respect to selecting a machine learning algorithm, wherein the reinforcement learning dynamically adjusts a machine learning algorithm selection strategy used to select a machine learning algorithm.

3. The method of claim 2, wherein performing reinforcement learning, further comprises:

performing a selection at least one machine learning algorithm;

determining if the at least one machine learning algorithm results in a satisfactory state with respect to the problem; and

awarding a reinforcement value to the selection of the at least machine learning algorithm in response to the selection resulting in a satisfactory state with respect to the problem, wherein the reinforcement value increases a likelihood that the at least one machine learning algorithm is to be selected again with respect to a substantially similar problem.

4. The method of claim 1, wherein receiving as an input a state of at least one problem and at least one context associated with the problem, further comprises:

receiving a plurality of problem data information sets associate with at least one managed entity;

aggregating at least two problem data information sets in the plurality of problem data information sets; and

creating the problem based on the at least two problem data information sets that have been aggregated.

5. The method of claim 4, wherein aggregating at least two problem data information sets further comprises:

determining a relationship between the at least two problem data information sets and a context associated with each of the at least two problem data information sets.

6. The method of claim 1, further comprising:

receiving a set of policies as another input filtering the context based on at least one policy in the set of policies; and

selecting the machine learning algorithm based on the problem and the context that has been filtered.

7. The method of claim 1, wherein selecting a machine learning algorithm, further comprises:

selecting a group of machine learning algorithms; and

selecting a machine learning algorithm from within the group.

8. The method of claim 1, wherein the machine learning algorithm is one of:

a supervised machine learning algorithm;

an unsupervised machine learning algorithm; and

a hybrid machine learning algorithm comprising a combination of both the supervised machine learning algorithm and the unsupervised machine learning algorithm.

9. The method of claim 1, further comprising:

deriving, based on selecting the machine learning algorithm, at least one policy for governing a future selection of machine learning algorithms.

10. An information processing system for selecting a machine learning algorithm, the information processing system comprising:

a memory;

a processor communicatively coupled to the memory; and

an autonomic manager communicatively coupled to the memory and the processor, wherein the autonomic manager is adapted to;

receive as an input a state of at least one problem and at least one context associated with the problem;

select a machine learning algorithm based on the problem and context of the problem that have been received; and

output the machine learning algorithm that has been selected to an autonomic controller.

11. The information processing system of claim 10, wherein the autonomic manager is further adapted to select a machine learning algorithm by:

performing reinforcement learning with respect to selecting a machine learning algorithm, wherein the reinforcement learning dynamically adjusts a machine learning algorithm selection strategy used to select a machine learning algorithm.

12. The information processing system of claim 11, wherein performing reinforcement learning, further comprises:

performing a selection at least one machine learning algorithm;

determining if the at least one machine learning algorithm results in a satisfactory state with respect to the problem; and

awarding a reinforcement value to the selection of the at least machine learning algorithm in response to the selection resulting in a satisfactory state with respect to the problem, wherein the reinforcement value increases a likelihood that the at least one machine learning algorithm is to be selected again with respect to a substantially similar problem.

13. The information processing system of claim of claim 10, wherein the autonomic manager is further adapted to receive as an input a state of at least one problem and at least one context associated with the problem by:

receiving a plurality of problem data information sets associate with at least one managed entity;

aggregating at least two problem data information sets in the plurality of problem data information sets; and

creating the problem based on the at least two problem data information sets that have been aggregated.

14. The information processing system of claim of claim 10, wherein the autonomic manager is further adapted to:

receive a set of policies as another input filter the context based on at least one policy in the set of policies; and

select the machine learning algorithm based on the problem and the context that has been filtered.

15. The information processing system of claim of claim 10, wherein the autonomic manager is further adapted to:

deriving, based on selecting the machine learning algorithm, at least one policy for governing a future selection of machine learning algorithms.

16. A network for managing autonomous operations of networking elements the network comprising:

a first network element;

at least a second network element; and

at least one information processing system communicatively coupled to the first network element and the at least second network element, the at least one information processing system comprising;

a memory;

a processor communicatively coupled to the memory; and

an autonomic manager communicatively coupled to the memory and the processor, wherein the autonomic manager is adapted to;

receive as an input a state of at least one problem and at least one context associated with the problem, wherein the at least one problem and the context are further associated with at least one of the first network element and the at least second network element;

select a machine learning algorithm based on the problem and context of the problem that have been received; and

output the machine learning algorithm that has been selected to an autonomic controller.

17. The network of claim 16, wherein the autonomic manager is further adapted to select a machine learning algorithm by:

performing reinforcement learning with respect to selecting a machine learning algorithm, wherein the reinforcement learning dynamically adjusts a machine learning algorithm selection strategy used to select a machine learning algorithm; and

wherein performing reinforcement learning, further comprises;

performing a selection at least one machine learning algorithm;

determining if the at least one machine learning algorithm results in a satisfactory state with respect to the problem; and

awarding a reinforcement value to the selection of the at least machine learning algorithm in response to the selection resulting in a satisfactory state with respect to the problem, wherein the reinforcement value increases a likelihood that the at least one machine learning algorithm is to be selected again with respect to a substantially similar problem.

18. The network of claim of claim 16, wherein the autonomic manager is further adapted to receive as an input a state of at least one problem and at least one context associated with the problem by:

receiving a plurality of problem data information sets associate with at least one managed entity;

aggregating at least two problem data information sets in the plurality of problem data information sets; and

creating the problem based on the at least two problem data information sets that have been aggregated.

19. The network of claim of claim 16, wherein the autonomic manager is further adapted to:

receive a set of policies as another input

filter the context based on at least one policy in the set of policies; and

select the machine learning algorithm based on the problem and the context that has been filtered.

20. The network of claim of claim 16, wherein the autonomic manager is further adapted to:

deriving, based on selecting the machine learning algorithm, at least one policy for governing a future selection of machine learning algorithms.