Machine Learning and Inductive Logic Programming for Multi-agent Systems
EASSS '01 Selected Tutorial Papers from the 9th ECCAI Advanced Course ACAI 2001 and Agent Link's 3rd European Agent Systems Summer School on Multi-Agent Systems and Applications
The eXtensible Rule Markup Language
Communications of the ACM - Wireless networking security
Learning in multi-agent systems
The Knowledge Engineering Review
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
Reflective-Cognitive Architecture: From Abstract Concept to Self-Adapting Agent
DIS '06 Proceedings of the IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning in BDI multi-agent systems
CLIMA IV'04 Proceedings of the 4th international conference on Computational Logic in Multi-Agent Systems
Techniques for knowledge acquisition in dynamically changing environments
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
Learning from others: Exchange of classification rules in intelligent distributed systems
Artificial Intelligence
| Hi-index | 0.00 |
Adaptability is a fundamental property of any intelligent system. In this paper, we present how adaptability in multi-agent systems can be implemented by means of collaborative logic-based learning. The proposed method is based on two building blocks: (1) a set of operations centred around inductive logic programming for generalizing agents' observations into sets of rules, and (2) a set of communication strategies for sharing acquired knowledge among agents in order to improve the collaborative learning process. Using these modular building blocks, several learning algorithms can be constructed with different trade-offs between the quality of learning, computation and communication requirements, and the disclosure of the agent's private information. The method has been implemented as a modular software component that can be integrated into the control loop of an intelligent agent. The method has been evaluated on a simulated logistic scenario, in which teams of trading agents learn the properties of the environment in order to optimize their operation.