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Expert Systems and Artificial Intelligence Gerard Tel World War II as the AI Big Bang Development in 1940-45: What to do after WWII? Create Humanoid machines. • AI is concerned with programming computers to perform tasks that are presently better done by humans. (Minsky) • Games, Language, Vision, Mathematics Expert Systems 1 2 Music classification by Compression • Music interpretation problem: Assign unknown symphony to composer. • Human approach: extract style, emotion, era… • Compression by Lempel-Ziv (as in ZIP): After “training”, compactly code common patterns. • Compress file B after training with A: DAB = | Cp(AB) | - | Cp(A) | Measure for similarity between A and B. • From known musical scores Ai, select the one that minimizes DAiB. • Hmmm… Is this intelligent? Outperforms the best human music experts!! Expert Systems 1 3 Mathematics: Triangle Theorem T Isosceles triangles are equiangular: if AT = BT then <(A) = <(B). B Reasoning steps of geometry: • If Δ(xyz) = Δ(uvw) then <(y) = <(v) • Line bisection: take m on xy st xm = my. • Congruence rule SSS: if xy=uv, yz=vw, zx=wu then Δ(xyz) = Δ(uvw). A 20-30 axioms, theorems, and steps. Expert Systems 1 4 The Human Proof Given AT = BT, Prove that <(A) = <(B). Proof (Euclid): 1. Take M to bisect AB. 2. AT = BT 3. TM = TM 4. MA = MB 5. Δ(MAT) = Δ(MBT) 6. <(A) = <(B) T B (Given) (Trivial) (From 1) (SSS) (congruence) M A Found in all textbooks for 2500 years since Euclid. Expert Systems 1 5 The Computer Proof Clever trick: Use possibility of a nontrivial self-congruence. T Given AT = BT, Prove that <(A) = <(B). Proof: 1. AT = BT 2. TB = TA 3. BA = AB 4. Δ(BAT) = Δ(ABT) 5. <(A) = <(B) B (Given) (Given) (Trivial) (SSS) (congruence) Expert Systems 1 A 6 The search for a proof: State Space Exploration General Problem Solver Start state • Model problem: states and transitions. • Search in induced graph. • Search Strategies: • Depth-First • Breadth-First • Best-First • Heuristic QED Expert Systems 1 7 Where Computers and Humans differ Why couldn’t a human find the short proof? Why couldn’t the computer find the long proof? • Tendency to overlook simple and special cases • It it too complicated!! • Combinatorial Explosion Branching factor 13 5 steps: 402,234 nodes 9 steps: 11,488,207,654 • Exploring exponential spaces is intractible. • But humans dont work like this… they understand math Expert Systems 1 8 What does it mean to understand? • Jackson: The Romantic period in Artificial Intelligence Overdragen: Chinese Room argument • Understanding is INTENTIONAL: Computers and Humans work IN THE SAME WAY Argument: make the same mistakes, etc • Understanding is BEHAVIORAL: Computers and Humans produce the same results Turing test • Why is it difficult to mimic human behavior in a computer? Expert Systems 1 9 Understanding in Expert Systems? Understanding requires: • Representation and manipulation of Domain Knowledge • Perceive analogies • Learn Pragmatic view: • Intentional intelligence is not required • Programs will work the better if more human domain knowledge is encoded in them Normative Descriptive Limitive Definition of Expert System: An Expert System is a computer program that represents and reasons with knowledge of some specialist subject with a view to solving problems or giving advice Expert Systems are a subfield of Artificial Intelligence. Term: Knowledge Based System Expert Systems 1 10 Understanding Geometry: Represent knowledge Mathematician solves proof: • To conclude <(A) = <(B), find Δ(xAy) and Δ(uBv); prove congruence. • To conclude <(A) = <(B), construct <(C) = <(A); prove <(C) = <(B). • To prove congruence, use SSS, ASA, SAS, … • To construct … • Triangle 1 is better than triangle 2 because … • I usually try SAS first when… Formalization of strategy: • Consider triangle pairs xAy and uBv • Select a promising pair • Select a promising rule R • Prove antecedents of R • Conclude congruence • Conclude equality of angles Strategies can be represented in STRIPS Backtracking, Ordering of alternatives, Pruning, Expert Systems 1 … 11 STRIPS Operator Tables Robot moves object X from location Y to location Z: Operator Table push(X, Y, Z) • Pre: at(robot, Y), at(X, Y) • Post: at(X, Z) • Add list: at(robot, Z), at(X, Z) • Delete list: at(robot, Y), at(X, Y) STRIPS maintains a goal list. Action: • Pick goal G from goals • Pick OT with G in Post • Throw Pre of G in goals Planning is at higher level than individual actions. Expert Systems 1 12 Uniform representation of facts Synonyms: 1. Sam is the father of Bill 2. Sam is Bill’s father 3. Bill is Sam’s son 4. Sam is Bill’s mother’s husband Perceiving analogies requires uniform representation: • sonOf(Sam, Bill) SHRDLU World: color(block1, red) color(block2, green) supports(table, block1) supports(block1, block2) Represent many facts with simple fact structure Expert Systems 1 13 Data representation: Relational Database Define a relation in CLIPS (deftemplate errand (field name (type SYMBOL)) (field duration (type INTEGER) ) ) Relation errand with attributes name and duration. Add tuples to relation errand: (deffacts the-facts (errand (name hospital) (duration 200) ) (errand (name doctor) (duration 100) ) ) CLIPS actions and rules as Database operations. Update fact base when state is changed. Expert Systems 1 14 Explicitation of Expert knowledge Problem 1: What does it stand for in • The ball hit the vase and it broke • The vase hit the wall and it broke Problem 2: Using hammer, string, and wrench, liberate ball from tube in floor. (Solution could never be found by computer!!) Application of XS requires: • restriction to a well-understood domain (ball?) • someone who can perform the task • knows how it is performed (it?) • can explain how it is performed • is willing to cooperate Expert Systems 1 15 Characteristics of Expert Systems: Expert systems: Knowledge Based Systems • Separation of facts, knowledge, and inference knowledge is explicit, not hidden in algorithm • Simulates Human reasoning Built from approach of Human Expert • Uses approximate or heuristic search Not the only approach to solving AI problems! (Music classification, chess…) Use: Legal, medical, scientific, tech support, language, … Expert Systems 1 16 Parties in XS world • Human Expert Can solve problems; we desire to solve the problems without her. • Knowledge Engineer Can communicate with HE to obtain and model the knowledge that we need in the system • Programmer Builds and maintains all the necessary computer programs • User Wants to use expertise to solve problems (better, cheaper) Expert Systems 1 17 Explanation facilities System makes explicit why the conclusion is reached • For User: increases confidence increases transparance (legal domain!) • For Human Expert/Knowledge Engineer: see how knowledge is used, debug • For Programmer: Debug, Test, Improve • For trainee: have better understanding of expertise Expert Systems 1 18 Main challenges in Expert Systems field • Acquiring knowledge Expert is unaware, uncommunicative, busy, unwilling • Representing knowledge Facts, Relations, Conclusions, Meta-knowledge • Controlling reasoning Selection between alternatives is guided by higher order knowledge (meta rules) • Explanation • Sequence of reasoning steps? • Interpretation at higher level • Why were other steps NOT chosen? • Quality evaluation; acceptance Expert Systems 1 19 The Expert Systems course • www.cs.uu.nl/docs/vakken/exp/ • Teacher: Gerard Tel Practicals: Johan Kwisthout • One or two papers per lecture • Optional reading: Peter Jackson, Introduction to Expert Systems • Formal obligations: two exams, two computer projects in teams of four. MIN ≥ 4, WeightedAVG, 2nd chance. • Prepare for working class: read literature, try exercises (website) • Prepare for tests: Working class, previous tests (website) Expert Systems 1 20