Distributed Artificial Intelligence
Distributed Artificial Intelligence
Technical Note: \cal Q-Learning
Machine Learning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computational Theories of Interaction and Agency
Computational Theories of Interaction and Agency
Cooperative Knowledge Processing: The Key Technology for Intelligent Organizations
Cooperative Knowledge Processing: The Key Technology for Intelligent Organizations
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Printed Circuit Board Design via Organizational-Learning Agents
Applied Intelligence
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
Toward Cumulative Progress in Agent-Based Simulation
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Learning Classifier Systems Meet Multiagent Environments
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
How to Design Good Results for Multiple Learning Agents in Scheduling Problems?
PRIMA '99 Proceedings of the Second Pacific Rim International Workshop on Multi-Agents: Approaches to Intelligent Agents
PRIMA 2001 Proceedings of the 4th Pacific Rim International Workshop on Multi-Agents, Intelligent Agents: Specification, Modeling, and Applications
Interpretation by Implementation for Understanding a Multiagent Organization
Computational & Mathematical Organization Theory
Robot Formations for Area Coverage
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
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The concepts of organizational learning in organization andmanagement science cover a very wide range of organization-relatedactivities in organization. Since socially situated intelligence isone of such activities, this paper makes the concept oforganizational learning operational from the computational viewpointfor investigating socially situated intelligence. In particular, thispaper focuses on the characteristics of multiagent learning as onekind of socially situated intelligence, and analyzes them using fouroperationalized learning mechanisms in organizational learning. Acareful investigation on the characteristics of multiagent learninghas revealed the following implications: (1) there are two levels inthe learning mechanisms for multiagent learning (the individual leveland organizational level) and each mechanism is divided into twotypes (single- and double-loop learning). The integration of thesefour learning mechanisms improves socially situated intelligence; and(2) the following properties support socially situated intelligence:(a) different dimensions in learning mechanisms, (b) interactionamong various levels and types of learning mechanisms in addition tointeraction among agents, and (c) combination of exploration at anindividual level and exploitation at an organizational level.