Artificial Intelligence CIS 342

Artificial IntelligenceCIS 342 The College of Saint Rose David Goldschmidt, Ph.D.

I, Robot by Isaac Asimov • What do we learn about robots? • Can “see” as humans do (computer vision) • Can walk and traverse around objects • Use intuition; are skeptical • Rely on reason • Are able to explain reasoning • Are curious as to one’s own existence • Are introspective

I, Robot by Isaac Asimov • Is any of the AI in I, Robot realistic? • Computer vision • Robotics (boundary detection, learning) • Knowledge-based reasoning • Computer reasons and explains how aconclusion has been reached • Expert systems • Computer explains how a diagnosishas been determined

Expert Systems • An expert system performs at a human expert level in a narrow and specialized domain • Often a tradeoff between accuracy and speed • Use symbolic reasoning to solve problems • Symbols represent facts and rules (i.e. knowledge) • Symbols are usually human-readable as opposed to cryptic variable names, etc.

Expert Systems • Expert systems apply heuristics to guidethe reasoning process • This reduces the search space and time required toproduce one or more solutions • The goal is not always tosolve the problem exactly;often just to identifyone or more good solutions

Expert Systems • Expert systems provide explanation facilities to display reasoning (e.g. applied rules) to users • How did you come to that conclusion or diagnosis? • Expert systems make mistakes • So do human experts! • Users have to be awareof this possibility

By Comparison....

Knowledge • Knowledge is a theoretical or practical understanding of a subject or a domain • Those who possess knowledge arecalled experts (or domain experts) • Experts have deep knowledge of facts, rules, heuristics, etc.

In-Class Exercise • Using your favorite programming language, create anexpert system to interactively ask users yes/no questions and diagnose one of the following health problems: • Cold and Flu • Headaches • Shoulder Problems • Tooth Problems • i.e. implement an expert system using charts fromhttp://familydoctor.org/symptom.xml

from Every Patient Tells a Story by Lisa Sanders, M.D. ISBN 0767922476 Medical Diagnostics • Attempts to build expert systems formedical diagnosis go back to the mid-1970s • Peter Szolovits (Professor of Computer Science at MIT) • Began his quest in 1976 (Caltech) • “We thought we could identify all of the best practices in medicine, create a system that would make diagnosis faster and easier, and bring it all to doctors via a computer.”

from Every Patient Tells a Story by Lisa Sanders, M.D. ISBN 0767922476 Medical Diagnostics “It’s simply not possible.” “The emphasis and attention has shifted towardbringing below-average doctors up to current standardsand helping even good doctors avoid doing really stupid things.That turns out to provide greater benefits to patients.”

from Every Patient Tells a Story by Lisa Sanders, M.D. ISBN 0767922476 DXplain • DXplain first launched in 1986 • Users enter clinical information, then ask DXplain to provide diagnostic decision support • DXplain knowledge base (KB) covers ~2400 diseasesand over 5000 clinical findings (signs, symptoms, epidemiologic data, laboratory findings, etc.) • Demo: http://dxplain.mgh.harvard.edu/dxp/dxp.sdemo.pl • Info: http://lcs.mgh.harvard.edu/projects/dxplain.html

from Every Patient Tells a Story by Lisa Sanders, M.D. ISBN 0767922476 GIDEON • Global Infectious Disease and Epidemiology Network • Online application that supports the diagnosis and treatment of infectious diseases • Organized by country of origin • Updated weekly! • Info: http://www.gideononline.com/

from Every Patient Tells a Story by Lisa Sanders, M.D. ISBN 0767922476 Isabel • Second-generation diagnostic supportsystem with improved user interface • Freeform text entry • Modern search techniquesfor text-based matching • Info: http://www.isabelhealthcare.com/home/

Facts and Rules • A fact may be thought of as a unit of knowledge • A rule enables an artificially intelligent system to derive new facts from existing facts • Rules typically take the form of IF-THEN statements • IF is the antecedent, premise, or condition • THEN is the consequent, conclusion, or action

Categories of Rules • Relation: IF the ‘fuel gauge’ is empty THEN the car is dead

Categories of Rules • Recommendation: IF the forecast is rain AND the sky is cloudy THEN the advice is ‘bring your umbrella’

Categories of Rules • Directive: IF the car is dead AND the ‘fuel gauge’ is empty THEN the action is ‘refuel the car’

Categories of Rules • Strategy: IF the car is dead THEN the action is ‘check the fuel gauge’; step 1 is complete IF step 1 is complete AND the car is dead THEN the action is ‘check the battery’; step 2 is complete

Categories of Rules • Heuristic: IF the spill is liquid AND the ‘spill pH’ < 6 AND the ‘spill smell’ is vinegar THEN the ‘spill material’ is ‘acetic acid’

Development Team

Rule-Based Expert System • Newell and Simon, CMU (1970s)

Rule-Based Expert System

Inference Engine • New facts are discovered by anexpert system’s inference engine • The process is called inference • New facts are inferred • Facts serve as data within the knowledge base • Rules (IF-THEN) describe how new data is inferred

Inference Engine • Inference engine algorithm: • Inference engine compares each rulewith facts it already “knows” about,matching the antecedent (IF condition) • When the antecedent matches one ormore known facts, the rule fires andits consequent (THEN) is executed

Inference Engine 1 4 2 3

Inference Chain • An inference chain indicates how an expert system applies rules to reach a conclusion given: A, B, D, E

Forward Chaining • Forward chaining applies rules to knowndata to achieve a desired goal • Also called data-driven reasoning • At each iteration, topmost rule is executed • When fired, a rule adds a new factto the database • This match-fire cycle stops when the goalhas been found or when no other rulescan be fired

Forward Chaining

Forward Chaining Examples • Facts: • Rules: • Use forward chaining to prove the following: • Prove: A B C D E Z A  X Q  T A  Y A & D  Q Q  R R  S N  L T  Z

SOLUTION Forward Chaining Examples • Facts: • Rules fired: • Use forward chaining to prove the following: A B C D E A B C D E X Y Q R S T Z A B C D E X A B C D E X Y A B C D E X Y Q A B C D E X Y Q R A B C D E X Y Q R S A B C D E X Y Q R S T • Proven: A  X A  Y A & D  Q Q  R R  S Q  T T  Z Z

Forward Chaining Examples • Facts: • Rules: • Use forward chaining to prove the following: • Prove: A B C D E L A  X Q  T A  Y A & D  Q Q  R R  S N  L T  Z

SOLUTION Forward Chaining Examples • Facts: • Rules fired: • Use forward chaining to prove the following: A B C D E X Y Q R S T Z • Cannot prove: • No more rules left to fire A  X A  Y A & D  Q Q  R R  S Q  T T  Z L

Forward Chaining • Using forward chaining, many rules may be executed unnecessarily • Search space is large and ever-expanding • Inefficiently fires rules that do not lead to desired goal • Therefore, often prefer to use backward chaining....

Backward Chaining • Backward chainingstarts with a desired goal,then searches for evidence to prove the goal • Desired goal often calledthe hypothetical solution • Backward chaining alsocalled goal-driven reasoning

Backward Chaining • Backward chaining algorithm: • At the first iteration, rule(s) with thedesired goal in the consequent are selected • Stack up and attain many subgoals until.... • If the antecedent matches known data,the rule is fired and the goal is proven • Otherwise, if no rules remain,the desired goal is not proven

Backward Chaining

Backward Chaining Examples • Facts: • Rules: • Use backward chaining to prove the following: • Prove: A B C D E Z A  X Q  T A  Y A & D  Q Q  R R  S N  L T  Z

SOLUTION Backward Chaining Examples • Facts: • Stack of rules: (subgoals) • Rules fired: • Use backward chaining to prove the following: A B C D E Q T Z A B C D E • Proven: A & D  Q Q  T T  Z Z A & D  Q Q  T T  Z

Backward Chaining Examples • Facts: • Rules: • Use backward chaining to prove the following: • Prove: A B C D E L A  X Q  T A  Y A & D  Q Q  R R  S N  L T  Z

SOLUTION Backward Chaining Examples • Facts: • Stack of rules: (subgoals) • Cannot prove: • Subgoal N cannot be proven • Use backward chaining to prove the following: A B C D E N  L L

Artificial Intelligence CIS 342