Semantic adaptive framework (SAF) for enabling system self selection of actions by reasoning about self knowledge

• US 20050256819A1
• Filed: 04/28/2005
• Published: 11/17/2005
• Est. Priority Date: 04/28/2004
• Status: Abandoned Application

• Current Assignee: Clark Tibbs, John A. Yanosy Jr
• Original Assignee: Clark Tibbs, John A. Yanosy Jr
• Inventors: Tibbs, Clark, Yanosy, John A. Jr.
• Application Number:
  US11/117,073
• Publication Number:
  US 20050256819A1
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation

Abstract

A system and method of defining system behavior is described. The invention is directed to selecting optimal actions by a process of reasoning about a systems self knowledge. One or more of the following parameters are considered in the process, the environmental state, system state, user situation, available system capabilities and network accessible functions and services and knowledge represented as ontologies.

Claims

1. A method of defining system behavior comprising

selecting optimal actions by a process of reasoning about a systems self knowledge wherein one or more of the following parameters are considered;

the environmental state, system state, user situation, available system capabilities and network accessible functions and services and knowledge represented as ontologies.

2. The method according to claim 1 wherein the system is a self contained system.

3. The method according to claim 1 wherein said system is a Network Environment where the system is distributed across multiple nodes, each of said nodes being capable of sharing knowledge with each other node.

4. The method according to claim 1 wherein meta knowledge in the form of ontologies is used for the purpose of defining system responses, considering problem context, user context, environment knowledge, and available responses.

5. The method according to claim 1 wherein knowledge is shareable among a set of nodes through a Web Service and wherein where each node makes its knowledge and reasoning results available to other nodes thereby enabling a networked system with local reasoning appropriate to the system capabilities of each node.

6. The method according to claim 5 wherein the use of standard Web Services standard enables a common platform independent network within a heterogeneous network.

7. The method according to claim 6 wherein said Web Service is UDDI

8. The method according to claim 6 wherein said Web Service is WSDL

9. The method according to claim 1 wherein explicit self knowledge enables reasoning to guide system behavior.

10. The method according to claim 1 wherein Network Accessible Knowledge enables reasoning to guide system behavior

11. The method according to claim 1 wherein environment data is analyzed by a classification learning algorithm to determine a candidate set of contexts and wherein context knowledge is not only the self knowledge of a single node but also the shared knowledge across nodes comprising a heterogeneous network.

12. The method according to claim 11 wherein a classification algorithm is used to select the most appropriate problem context hypothesis.

13. The method according to claim 12 wherein the specific ontology for a problem context is queried to determine the shared knowledge state.

14. The method according to claim 13 wherein a Context-Response-Relation is specified to link context knowledge to a set of appropriate system responses, workflows or actions, or information content.

15. The method according to claim 14 wherein a selected context and Context-Response-Relation expressions are evaluated to select responses, workflows, or content.

16. The method according to claim 15 wherein the results of selection are shared with other nodes

17. The method according to claim 16 wherein knowledge of a user/client and an environment and problem context enables a system to provide more relevant response to user/client requests.

18. The method according to claim 17 wherein self knowledge can be represented as a set of domain ontology specifications, which are defined as containing <

domain vocabulary, grammar rules, inference rules, and a'"'"'priori axioms>

.

19. The method according to claim 18 wherein reasoning and deliberation in said system has a specific goal of selecting the most relevant system response.

20. The method according to claim 19 wherein one or more machine learning algorithms can be used to train the system to select more optimal system responses with experience.

21. A system for defining behavior comprising

a means for selecting optimal actions by a process of reasoning about self knowledge wherein one or more of the following parameters are considered;

the environmental state, system state, user situation, available system capabilities and network accessible functions and services and knowledge represented as ontologies.