Abstract / truncated to 115 words (read the full abstract)

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that is prevalent in the real world, namely, the one that emerges from the collective of interconnected individuals with diverse capabilities, perspectives and experiences. To explore this fact, the current dissertation utilizes mathematical models of collaborative learning and reasoning. These models are based on the following two concepts: Bayesian inference, which is used to model how agents update their beliefs in the face of uncertain data, and graphs, which represent the communication links and information exchange among individuals. Through these models, the current ... toggle 10 keywords

multi-agent networks – distributed bayesian inference – information fusion – hidden markov models – state tracking – partially observable markov decision process – social influence – causal impact – social learning – multi-agent decision making

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