Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Readings in agents
Evolution and learning in multiagent systems (preface)
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Towards collaborative and adversarial learning:: a case study in robotic soccer
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Intelligent Information Agents: Agent-Based Information Discovery and Management on the Internet
Intelligent Information Agents: Agent-Based Information Discovery and Management on the Internet
Artificial Societies: The Computer Simulation of Social Life
Artificial Societies: The Computer Simulation of Social Life
Adaptation and Learning in Multi-Agent Systems: Some Remarks and a Bibliography
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Evaluating Concurrent Reinforcement Learners
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
On-Line Learning of Coordination Plans
On-Line Learning of Coordination Plans
Using multi-strategy learning techniques to improve planning efficiency and quality
Using multi-strategy learning techniques to improve planning efficiency and quality
AI and agents: state of the art
AI Magazine
Practical and theoretical innovations in multi-agent systems research
The Knowledge Engineering Review
A Manifesto for Agent Technology: Towards Next Generation Computing
Autonomous Agents and Multi-Agent Systems
Multi-behavior agent model for planning in supply chains: An application to the lumber industry
Robotics and Computer-Integrated Manufacturing
Concept of a Multi-Agent System for Assisting in Real Estate Appraisals
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
SIMBA: A simulator for business education and research
Decision Support Systems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Separating learning as an aspect in Malaca agents
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
Enhancing Malaca agents with learning
International Journal of Intelligent Information and Database Systems
A multi-agent system to assist with property valuation using heterogeneous ensembles of fuzzy models
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Collaborative learning with logic-based models
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Multi-agent system in urban traffic signal control
IEEE Computational Intelligence Magazine
Market-Inspired approach to collaborative learning
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
Enhancing the Adaptation of BDI Agents Using Learning Techniques
International Journal of Agent Technologies and Systems
Social Networked Multi-agent Negotiation in Ontology Alignment
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
| Hi-index | 0.00 |
In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.