Artificial Intelligence and Lisp Lecture 7 LiU Course TDDC65 Autumn Semester,

Many aspects of decision trees Probabilistic variants Hierarchical decision trees Ordered graphs (nets) can be reduced to trees Causal nets are hierarchical decision trees (if the outcome in each node is done defined by a decision tree Trees can be interpreted directly and inversely. Inverse interpretation of probabilistic trees uses Bayes' rule: P(A|B) P(B) = P(B|A)P(A) Terminology: Bayes net = Causal net (net/network)

Many aspects of decision trees Terminology: Bayes network = Causal network Wikipedia: A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). A causal network is a Bayesian network with an explicit requirement that the relationships be causal. Note: it is often easier and more natural to express dependencies from causes to effects, rather than from effects to causes.

Reasoning about actions and logic-based planning Note: The remainder of this lecture is documented through the accompanying lecture note.