1. Dealing with Uncertainty  The need to deal with uncertainty arose in “expert systems”  Code expertise into a computer system Example:  Medical diagnosis: MYCIN  Equipment failure diagnosis in a factory  Sample from MYCIN:  IF  The infection is primary-bacteremia AND  The site of the culture is one of the sterile sites AND  The suspected portal of entry is the gastrointestinal tract  THEN  There is suggestive evidence (70%) that the infection is bacteroid  Expert systems often have long chains  IF X THEN Y … IF Y THEN Z … IF Z THEN W …  If uncertainty is not handled correctly, errors build up, wrong diagnosis  Also, there may be dependencies, e.g. X and Y depend on each other  Leads to more errors…  Need a proper way to deal with uncertainty

2. How do Humans Deal with Uncertainty?  Not very well…  Consider a form of cancer which afflicts 0.8% of people (rare)  A lab has a test to detect the cancer  The test has a 98% chance to give an accurate result  Mr. Bloggs goes for the test  The result comes back positive  i.e. the test says he has cancer  What is the chance that he has the cancer?  28%  Afflicts experts too  Studies have shown: human experts thinking of likelihoods do not reason like mathematical probability

3. A BC DE Increased total serum count Metastatic cancer Brain Tumour Severe headaches Coma No Link

4. A BC DE Increased total serum count Metastatic cancer Brain Tumour Severe headaches Coma Serum count Brain tumour Coma Yes 95% YesNo94% NoYes29% No 0.1% Brain tumour headache Yes70% No1% …… …… …… …… …… …… …… …… ……

5. Inference in Belief Networks  Questions for a belief network:  Diagnosis  Work backwards from some evidence to a hypothesis  Causality  Work forwards from some hypothesis to likely evidence  Test a hypothesis, find likely symptoms  In general – mixed mode  Give values for some evidence variables  Ask about values of others  No other approach handles all these modes  Reasoning can take some time  Need to be careful to design network  Local structure: few connections

6. How Good are Belief Networks?  Relieves you from coding all possible dependencies  How many possibilities if full network?  Tools are available  Build network graphically  System handles mathematical probabilities  Case study:  Pathfinder a medical expert system  Assists pathologists with diagnosis of lymph-node diseases  Pathfinder is a pun  User enters initial findings  Pathfinder lists possible diseases  User can  Enter more findings  Ask pathfinder which findings would narrow possibilities  Pathfinder refines the diagnosis  Pathfinder version based on Belief Networks performs significantly better than human pathologists