Open information systems semantics for distributed artificial intelligence
Artificial Intelligence
Social conceptions of knowledge and action: DAI foundations and open systems semantics
Artificial Intelligence
Technical Note: \cal Q-Learning
Machine Learning
Case-based reasoning
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Naming the Unnamable: Socionics or the Sociological Turn of/to Distributed Artificial Intelligence
Autonomous Agents and Multi-Agent Systems
Interaction is meaning: a new model for communication in open systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Acquiring and adapting probabilistic models of agent conversation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
An empirical semantics approach to reasoning about communication
Engineering Applications of Artificial Intelligence
Multiagent learning for open systems: a study in opponent classification
Adaptive agents and multi-agent systems
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This paper introduces “micro-scalability” as a novel design objective for social reasoning architectures operating in open multiagent systems. Micro-scalability is based on the idea that social reasoning algorithms should be devised in a way that allows for social complexity reduction, and that this can be achieved by operationalising principles of interactionist sociology. We first present a formal model of InFFrA agents called m2InFFrA that utilises two cornerstones of micro-scalability, the principles of social abstraction and transient social optimality. Then, we exemplify the usefulness of these concepts by presenting experimental results with a novel opponent classification heuristic AdHoc that has been developed using the InFFrA social reasoning architecture. These results prove that micro-scalability deserves further investigation as a useful aspect of socionic research.