Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Machine Learning
The tropos software development methodology: processes, models and diagrams
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Developing multiagent systems: The Gaia methodology
ACM Transactions on Software Engineering and Methodology (TOSEM)
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Hi-index | 0.00 |
Large scale multi-agent systems (MASs) in unpredictable environments must use machine learning techniques to perform their goals and improve the performance of the system. This paper presents a systematic approach to introduce machine learning in the design and implementation phases of a software agent. We also present an incremental implementation process for building asynchronous and distributed agents, which suppors the combination of machine learning strategies. This process supports the stepwise building of adaptable MASs for unknown situations, improving their capacity to scale up. We use the Trading Agent Competition (TAC) environment as a case study to illustrate the suitability of our approach.