Proceedings of the seventh international conference (1990) on Machine learning
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Technical Note: \cal Q-Learning
Machine Learning
Creating advice-taking reinforcement learners
Machine Learning - Special issue on reinforcement learning
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Implicit Imitation in Multiagent Reinforcement Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Mutually Supervised Learning in Multiagent Systems
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
SA-Prop: Optimization of Multilayer Perceptron Parameters Using Simulated Annealing
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
Imitation in animals and artifacts
Reinforcement learning of coordination in cooperative multi-agent systems
Eighteenth national conference on Artificial intelligence
Believing Others: Pros and Cons
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
On partially controlled multi-agent systems
Journal of Artificial Intelligence Research
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning and interacting in human-robot domains
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Learning in behavior-based multi-robot systems: policies, models, and other agents
Cognitive Systems Research
Learning from Multiple Sources
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
An evidential and genetic method for cooperative learning systems
Multiagent and Grid Systems
Communication during learning in heterogeneous teams of learning agents
Intelligent Decision Technologies
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
An evidential cooperative multi-agent system
Expert Systems with Applications: An International Journal
Multi-agent learning: how to interact to improve collective results
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Parallel reinforcement learning with linear function approximation
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-criteria expertness based cooperative Q-learning
Applied Intelligence
| Hi-index | 0.00 |
One of the main questions concerning learning in a Multi-Agent System's environment is: "(How) can agents benefit from mutual interaction during the learning process?" This paper describes a technique that enables a heterogeneous group of Learning Agents (LAs) to improve its learning performance by exchanging advice. This technique uses supervised learning (backpropagation), where the desired response is not given by the environment but is based on advice given by peers with better performance score. The LAs are facing problems with similar structure, in environments where only reinforcement information is available. Each LA applies a different, well known, learning technique. The problem used for the evaluation of LAs performance is a simplified traffic-control simulation. In this paper the reader can find a summarized description of the traffic simulation and Learning Agents (focused on the advice-exchange mechanism), a discussion of the first results obtained and suggested techniques to overcome the problems that have been observed.