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Content archived on 2024-05-27

AGENT ACADEMY: A Data Mining Framework for Training Intelligent Agents


Intelligent Agent technology, coupled with Data Mining and Knowledge Discovery promise to dramatically affect the way humans interact with computers. The main goal of this project is to develop Agent Academy, an integrated environment for embedding intelligence in newly created agents through the use of Data Mining techniques. We plan a) to develop the tools for assembling and maintaining a large repository of data on agent use and behavior, b) to provide an integrated framework for the systematic study of agent intelligence, and c) to develop a well-defined, well-specified model for an agent-training facility. Agent Academy will adhere to a common set of primitive specifications, which are compliant with widely accepted efforts for standardization, such as the ones undertaken by FIPA, OMG, and others. The successful outcome of this project is expected to propagate the use of agent related technologies into business practices and personal use.

The overall objective of the proposed project is the development of an integrated framework for training Intelligent Agents (IA) based on Data Mining (DM) techniques. Agent Academy aims to improve the abilities of IAs to learn, create inferences, and make decisions on their own. The successful outcome of this effort is expected to propagate the use of IA-related technologies into both business practices and personal use. Specific objectives include:
a) the development of novel methodologies to enhance IA intelligence;
b) the development of tools for assembling and maintaining a large repository of data on IA use and behavior, as well as DM techniques for knowledge extraction about IA?s behavioral characteristics;
c) the improvement of the quality of services provided by enterprises solutions based on IA applications;
d) the exploitation and early adoption of project results across Europe, and;
e) the dissemination of the project results to European and international research and industrial communities.

The Agent Academy (AA) framework operates as a multi agent system, which can train new agents or re-train its own agents in a recursive mode. A user (a physical or virtual entity) issues a request for a new agent as a set of functional specifications. The request is handled by the Agent Factory, which is a module responsible for selecting the most appropriate agent type and supplying the base code for it. This untrained agent (UA) comprises a minimal degree of intelligence, defined by the software designer. It enters the Agent Training (AT) module, where its world perception increases substantially during a virtual interactive session with an agent master (AM). Based on the encapsulated knowledge, acquired in the knowledge extraction phase, an AM can take part in long agent-to-agent transactions with the UA. This process may include modifications in the agent's decision path traversal and application of augmented adaptively in real transaction environments. The core of the agent academy is the Agent Use Repository (AUR), a collection of statistical data on prior agent behavior and experience. Building AUR will be a continuous process performed by a large number of mobile agents and controlled by the data acquisition module. It is on the contents of AUR that the Data Miner, the data mining implementation module, performs agent type classification and association rules ex-traction for the decision making process, in order to augment the intelligence of the AM in the training module. A large part of an agent's intelligence handles the knowledge acquired by the agent since the beginning of its social life through the interaction with the environment it acts upon. After the training is complete, the now intelligent agent, armed with tools for reporting its behavior to the AA, is released to the world. The AA is continuously integrated, as it receives feedback from mobile agents roaming the web, updating its agent use repository and refining its data mining and AT techniques.

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Participants (7)