Uncertain Reasoning: Bayesian Networks

This lecture covers uncertain reasoning, Bayesian networks, and stochastic resolution. It discusses the limitations of logic due to insufficient information, the representation of uncertainty through plausibility, and the use of Bayesian reasoning for updating plausibility. The lecture also explores formalisms for uncertainty, such as fuzzy logic and probabilistic reasoning, and provides examples like a burglar alarm system. It delves into the challenges of modeling context influence and avoiding inadmissible chains of reasoning. The significance of probabilistic logic and the interpretation of probabilities are also explained, along with the concept of abduction for causal and probabilistic reasoning.