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What is an expert system? ● A computer system whose performance is guided by specific, expert knowledge in solving problems. ● A computer system that simulates the decisionmaking process of a human expert in a specific domain. ● One of the early (large-scale) successes of artificial intelligence. Artificial Intelligence Expert Systems CS 4633/6633 Artificial Intelligence CS 4633/6633 Artificial Intelligence Early examples ● Early examples cont. MACSYMA - a large, interactive mathematics expert system developed in 1968 by Engleman, Martin, and Moses at MIT to manipulate mathematical expressions symbolically. One of the first expert systems. Written in LISP. Evolved into a widely used commercial product. HEARSAY I and II - speech understanding systems developed at CMU that accept a speech wave as input and produces a list of hypotheses. In 1975, it reached 75% accuracy in interpreting a 1,000-word vocabulary. CS 4633/6633 Artificial Intelligence ● MYCIN - the best-known expert system in use today. Developed in the mid 1970’s by Ed Feigenbaum at Stanford to diagnose infectious blood diseases and prescribe an antibiotic treatment. Reached human level precision. ● ● DENDRAL - one of the first expert systems was developed by Ed Feigenbaum at Stanford University. Heuristic Dendral establishes the structure of a molecule given: (a) its atomic formula (e.g. H2O), and (b) its mass spectrogram (the result of bombarding a sample with electrons, CS 4633/6633 Artificial Intelligence Modern examples ● ● ● PROSPECTOR - a commercial expert system ● developed in the late 1970’s at SRI by Duda, Hart, and Barnett. The LISP-based system locates valuable ore deposits and produces maps of geological site evaluations. ● CS 4633/6633 Artificial Intelligence INTERNIST - an internal medicine expert system that is now called CADUCEUS. Developed at the Univ of Pittsburgh in the early 1970’s to analyze hundreds of clinical problems. Written in LISP. breaking its molecules into pieces, putting them through a magnetic field and measuring their mass.) Early examples cont. ● ● ● control (air traffic) debugging design (computer configuration) medical diagnosis instruction/training interpretation (speech) ● ● ● ● ● monitoring (nuclear plant) planning (mission planning) Factory scheduling prediction (weather) repair (telephone) CS 4633/6633 Artificial Intelligence Advantages and disadvantages Human Expert Expert System Pro Expert perishable System unpredictable slow reproduction expensive slow processing permanent consistent quick replication affordable fast processing Pro Human creative adaptive broad focus common sense CS 4633/6633 Artificial Intelligence lacks inspiration needs instruction narrow focus machine knowledge Classes of expert systems ● Type of problem (classification, configuration, diagnosis, planning, etc.) ● Type of KR and reasoning (rule-based, first-order logic, probabilistic, fuzzy-logic, etc.) ● Type of domain (mostly deterministic, mostly stochastic). CS 4633/6633 Artificial Intelligence Components of a Rule-based Expert System Condition-action rules: if <condition> then <action> Facts Results How/Why Queries Facts Inference Engine Rules Rule-based systems User Interface CS 4633/6633 Artificial Intelligence 1. Working memory: data structures representing the current state of the system. (Facts = positive literals with no variables) 2. Rule memory: set of condition action rules <WM pattern> −−−> <WM changes> (Rules can add and delete facts from working memory.) 3. Rule interpreter: applies production rules to the working memory. CS 4633/6633 Artificial Intelligence Production rules in DENDRAL Pattern Matching Example: IF [the spectrum of the molecule has two peaks at masses x1 and x2 such that a) x1 + x2 = M + 28; AND b) x1 - 28 is a high peak; AND c) x2 - 28 is a high peak; AND d) at least one of x1 or x2 is high] THEN [the molecule contains a ketone group] Find all rules that are eligible to be “fired” by matching left-hand side of rules to facts in working memory ● If rules have variables, matching requires unification (the RETE algorithm can do this efficiently) CS 4633/6633 Artificial Intelligence ● CS 4633/6633 Artificial Intelligence Forward Chaining Conflicts and conflict resolution Match phase: Find the Conflict Set When several rules issue contradictory recommendations: ● ● ● ● ● Is there one Fire rule with the highest priority Fire rule with the most specific condition Fire the most recently used rule Fire rule with the most recently used variable Fire the most recently added rule CS 4633/6633 Artificial Intelligence Good for complex problems where humans have expert knowledge ● Expressiveness and intuitiveness – Rules can be understood by a non-programmer Simplicity (uniform KR) ● Modularity and modifiability ● – Individual rules can be changed and added – But it is hard to analyze the effect of each new rule on performance. CS 4633/6633 Artificial Intelligence STOP Yes Conflict resolution phase: Choose an applicable rule Act phase: Fire the Rule CS 4633/6633 Artificial Intelligence Advantages of rule-based expert systems ● No Difficulties ● “Knowledge acquisition bottleneck” – Difficulty in acquiring expert knowledge and putting it in system Not good at common-sense reasoning ● Traditional systems not good at handling uncertainty ● – Recent techniques have overcome this CS 4633/6633 Artificial Intelligence