1. AI - Week 24 Uncertain Reasoning (quick mention) then REVISION Lee McCluskey, room 2/07 Email lee@hud.ac.uklee@hud.ac.uk http://scom.hud.ac.uk/scomtlm/cha2555/

2. Logic and Reasoning in AI Other Forms of Representation and Reasoning Throughout the course (Planning, Logic, Machine Learning, Prolog) we have used “classical” True/False logic to underlie all we're done. There are huge branches of AI which represent knowledge in other ways – usually representing uncertainty, belief or probability.

3. Logic and Reasoning in AI Example: Baysian Representations There are many techniques for reasoning with uncertainty - the most common Baysian Belief networks (Bayes was an 18th century English Vicar). P(H given Evidence) = P(Evidence given H) * P(H) / P(Evidence) Bayesian networks are nodes connected by arcs where probability Is propagated theough the network using Bayesian Inference. Baysian systems are based on statistical inference, hence applications where relationships can be modelled by probability or likelihood are appropriate eg diagnostic expert systems for medicine such as Mycin.

4. Logic and Reasoning in AI Bayes * H is a hypothesis, and E is the evidence. * P(H) is the prior probability of H: the probability that H is correct before the evidence E was seen. * P(E | H) is the conditional probability of observing evidence E given that the hypothesis H is true. * P(E) is the probability of observing the evidence E. * P(H | E) is the probability that the hypothesis is true, given the evidence and the previous state of belief about the hypothesis. Bayesian networks are structures for representing knowledge about uncertain facts; they give a way of computing the impact of evidence.