Publication Type

Journal Article

Version

publishedVersion

Publication Date

6-2009

Abstract

Dealing with changing situations is a major issue in building agent systems. When the time is limited, knowledge is unreliable, and resources are scarce, the issue becomes more challenging. The BDI (Belief-Desire-Intention) agent architecture provides a model for building agents that addresses that issue. The model can be used to build intentional agents that are able to reason based on explicit mental attitudes, while behaving reactively in changing circumstances. However, despite the reactive and deliberative features, a classical BDI agent is not capable of learning. Plans as recipes that guide the activities of the agent are assumed to be static. In this paper, an architecture for an intentional learning agent is presented. The architecture is an extension of the BDI architecture in which the learning process is explicitly described as plans. Learning plans are meta-level plans which allow the agent to introspectively monitor its mental states and update other plans at run time. In order to acquire the intricate structure of a plan, a process pattern called manipulative abduction is encoded as a learning plan. This work advances the state of the art by combining the strengths of learning and BDI agent frameworks in a rich language for describing deliberation processes and reactive execution. It enables domain experts to specify learning processes and strategies explicitly, while allowing the agent to benefit from procedural domain knowledge expressed in plans.

Keywords

Abduction, Autonomous agents, BDI agent architecture, Machine learning, Plans

Discipline

Databases and Information Systems | Systems Architecture

Research Areas

Data Science and Engineering

Publication

Autonomous Agents and Multi-Agent Systems

Volume

18

Issue

3

First Page

417

Last Page

470

ISSN

1387-2532

Identifier

10.1007/s10458-008-9066-5

Publisher

Springer Verlag (Germany)

Additional URL

https://pure.mpg.de/rest/items/item_3020423_4/component/file_3046017/content

Download

Share

COinS