Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
2006 Special issue: Goals and means in action observation: A computational approach
Neural Networks - 2006 Special issue: The brain mechanisms of imitation learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Coordinating with the Future: The Anticipatory Nature of Representation
Minds and Machines
SILENT AGENTS: from observation to tacit communication
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
E4MAS'04 Proceedings of the First international conference on Environments for Multi-Agent Systems
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A crucial part of the intelligence that smart environments should display is a specific form of social intelligence: the ability to read human behavior and its traces in terms of underlying intentions and assumptions. Such ability is crucial to enable human users to tacitly coordinate and negotiate with smart and proactive digital environments. In this paper, the authors argue that the necessary tool for this ability is behavioral and stigmergic implicit (i.e. non-conventional) communication. The authors present a basic theory of such a fundamental interactive means-the theory of Behavioral Implicit Communication (BIC).