Intelligent multi-agent system by learning engine and method for operating the same

• US 20070136219A1
• Filed: 12/04/2006
• Published: 06/14/2007
• Est. Priority Date: 12/08/2005
• Status: Abandoned Application

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: Kim, Hyun, Kim, Minkyoung
• Application Number:
  US11/633,177
• Publication Number:
  US 20070136219A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

An intelligent multi-agent system by a learning engine and method for operating the same is provided. In the method, user state information is generated corresponding to a task at a plurality of zone agents. The user state information is received and a user behavior pattern is learned through the learning engine. The behavior pattern learned by the learning engine is outputted in the form of a rule. A task generator generates a task corresponding to the output rule. By the operating method, the present invention can be employed in all applications which intend to provide service suitable for a condition by adapting services positioning at different zones to user'"'"'s behavior pattern.

Claims

1. An intelligent multi-agent system by a learning engine, comprising:

a plurality of zone agents existing in each zone, managing user state information and performing a service corresponding to an event occurrence;

the learning engine observing and learning a user behavior pattern of each of the zone agents and outputting the learned behavior pattern in the form of a rule; and

a task generator generating a task in the zone agent when the rule is newly generated.

2. The intelligent multi-agent system of claim 1, further comprising an environment agent providing the task generator with environment information of the zone agent such that zone information of the zone agent can be referred.

3. The intelligent multi-agent system of claim 1, further comprising a rule DB provided between the learning engine and the task generator, for storing a rule outputting from the learning engine and an invariable definition rule.

4. The intelligent multi-agent system of any of claims 1 to 3, wherein the zone agent comprises:

an event listener sensing an event generated from the zone agent;

a task controller managing a task generated according to a rule of behavior patterns of the learning engine and giving a priority in a user'"'"'s present selection to perform a control corresponding to the task;

state information recording a condition generated from the corresponding zone agent; and

a communicator transferring the state information as an input value to the learning engine.

5. The intelligent multi-agent system of claim 4, wherein the task comprises:

an event setting a timing when the event is generated;

a condition setting a condition which should be satisfied at the time when the event is generated; and

an action setting a corresponding service performance if the event and the condition are all satisfied.

6. A method for operating an intelligent multi-agent system by a learning engine, the method comprising the steps of:

(a) generating user state information corresponding to a task at a plurality of zone agents;

(b) receiving the user state information and learning a user behavior pattern through the learning engine;

(c) outputting the behavior pattern learned by the learning engine in the form of a rule; and

(d) generating a task corresponding to the output rule at a task generator.

7. The method of claim 6, prior to the step (a), further comprising the step of providing environment information of the zone agent to the task generator such that the generated task can be applied.

8. The method of claim 6 or 7, wherein the step (a) comprises the steps of:

sensing an event generated from a corresponding zone agent;

managing the task generated according to a rule of the behavior pattern of the learning engine and giving a priority in a user'"'"'s present selection to perform a control corresponding to the task;

recording a condition generated from the corresponding zone agent; and

transferring the state information as an input value to the learning engine.

9. The method of claim 6, wherein the rule outputted in the step (c) is stored in a rule DB, and is subject to a management including creation, deletion and modification.

10. The method of claim 9, wherein an invariable definition rule is separately stored in the rule DB.