Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Machine learning: paradigms and methods
Machine learning: paradigms and methods
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Online learning about other agents in a dynamic multiagent system
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Editor's introduction: stigmergy
Artificial Life
Artificial Life
Machine Learning
Physicists Attempt to Scale the Ivory Towers of Finance
Computing in Science and Engineering
Modelling Bounded Rationality Using Evolutionary Techniques
Selected Papers from AISB Workshop on Evolutionary Computing
Dispersion games: general definitions and some specific learning results
Eighteenth national conference on Artificial intelligence
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Wayward Agents in a Commuting Scenario (Personalities in the Minority Game)
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Towards Understanding the Role of Learning Models in the Dynamics of the Minority Game
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Evaluating the Minority Game strategy in agent role assignments
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
The Minority Game Strategy in Team Competition: How and When?
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Learning to compete, compromise, and cooperate in repeated general-sum games
ICML '05 Proceedings of the 22nd international conference on Machine learning
An information-theoretic analysis of memory bounds in a distributed resource allocation mechanism
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning against opponents with bounded memory
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Heterogeneous populations of learning agents in the minority game
ALA'11 Proceedings of the 11th international conference on Adaptive and Learning Agents
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The use of resources in multiagent learning systems is a relevant research problem, with a number of applications in resource allocation, communication and synchronization. Multiagent distributed resource allocation requires that agents act on limited, localized information with minimum communication overhead in order to optimize the distribution of available resources. When requirements and constraints are dynamic, learning agents may be needed to allow for adaptation. One way of accomplishing learning is to observe past outcomes, using such information to improve future decisions. When limits in agents' memory or observation capabilities are assumed, one must decide on how large should the observation window be. We investigate how this decision influences both agents' and system's performance in the context of a special class of distributed resource allocation problems, namely dispersion games. We show by using several numerical experiments over a specific dispersion game (the Minority Game) that in such scenario an agent's performance is non-monotonically correlated with her memory size when all other agents are kept unchanged. We then provide an information-theoretic explanation for the observed behaviors, showing that a downward causation effect takes place.