Enhancing Malaca agents with learning

Current Object-Oriented (OO) frameworks provided with Multi-Agent Systems (MASs) development toolkits incorporate core abstractions to implement the agent. However, these OO designs do not provide proper abstractions to modularise other extra-functional concerns (e.g., the learning property), which are normally intermingled with the agent functionality and spread over different classes or components The reusability of agent architectural components is drastically reduced, so agents are difficult to maintain, extend or adapt. Aspect-oriented technologies overcome these problems by modelling such concerns as aspects. This work proposes to separate and modularise the learning of software agents following the aspectoriented solution of the Malaca model. Copyright © 2010 Inderscience Enterprises Ltd.