kishore and anil

8/8/2019 Kishore and Anil

1/18


2/18

AbstractAbstract Agent Academy, a platform for training agents introduces

a whole new perspective on the improvement of agentintelligence .

Data mining techniques are used to extract useful patternson real high-risk and time-efficient applications .

It provides platform with rules, decisions and classes ontest case data .

These metadata are embedded into agents in order toimprove their existing intelligence .

This paper describes the Agent Academy platform andfocuses on its

issues.


3/18

Main goal of the Agent Academy (AA) framework

To develop a platform for enhancing agents

functionality and intelligence.

It uses DataMining (DM) techniques.

This framework is concerned with two major issues:

1. Focuses on the way the AA trains the agents.

2. Techniques by which AA obtains information to

train its own agents.

Introduction


4/18

Functional Description of the Agent Academy


5/18

AA operates as a multi agent system.

User request for new agent.

Agent Factory selects agent type .

Creates an Untrained agent(UA).

UA enters the Agent-Training Module.

Core of AA is AUR.


6/18

AAplatform gave rise to issues on multi-agent

system architecture.

Data mining techniques are like

Association rule extraction

Classification

Clustering

FIPA-ACL is the communication language of agents.

Specifications on Technologies:


7/18

Components of Agent Academy


8/18

Agent Factory

creates agents

supply agent tool kit

provides an interface.

AUR

develop agent tracking tools

store data of A2A transactions

description of stored data.


9/18

ATM :

support interoperability with AF implement software entities

modification of an agents source

code.

Data mining in the DM is performed on two

levels of abstraction:

i. Association rules.

ii. Classification


10/18

DM Subcomponents :

Preprocessor is a java interface that collects

data from the AUR databases.

Performer performs datamining on the data

given by the preprocessor.

Evaluator receives the extracted knowledge.


11/18

AUR-DM interoperability :

The agent retains information in the form of an ACL

message.


12/18

DM-ATM interaction :


13/18

Let us present a simple example.

At top of the hierarchy lays the Service Resource Agent (SRA), an

agent that decides if production will initiate or not.

The ACL message of the SRA


14/18

Control information on the attributes is denoted intothe ACL message in the form of:


15/18

Preprocessor parses the ACL messages and

creates an .arfffile that looks like:


16/18

Applying the ID3 through WEKA, the resulting decision tree is :


17/18

Conclusion

AA develops a framework that supports efficient &

effective generation of network and system

management applications.

The use of intelligent agent technology will make

these applications dynamically adjustable to a

changing environment.

It enables the user-oriented management

applications.


18/18

Venkata Kishore.P

AnilVenkata Kumar.G

Presented By :

kishore and anil

Documents