b. ross cosc 4f79 1 famous expert systems before expert systems ("in the beginning...")...
TRANSCRIPT
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B. Ross Cosc 4f79 1
Famous Expert Systems
Before expert systems ("in the beginning...")
• Detailed Operation Procedures (DOP's): used by aeronautics industry and NASA, they are expert knowledge codified in written form.
- Not implemented on a computer. However, using a DOP is like manually following an algorithm by hand
(ignizio p.49)
• Heuristic programming: use heuristics to solve large, complex computational problems (early 1960's)
- Controversy whether expert systems are just examples of heuristic programming
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1. DENDRAL
• First expert system
• Project began at Stanford in mid 1960's, and is still being used.
• Domain: Organic chemistry - mass spectrometry
• Task: identify molecular structure of unknown compounds from mass spectra data
• Input: Histogram giving mass number/intensity pairs
• Output: Description of structure of the compound
• Architecture: plan-generate-test with constrained heuristic search
• Tools: production rules implemented in Lisp
• Results: "Discovery" of knowledge engineering. Many published results.
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B. Ross Cosc 4f79 3
DENDRAL
Winston
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B. Ross Cosc 4f79 4
DENDRAL
Winston
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DENDRAL
Winston p. 200
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DENDRAL
Procedure:
1. Spectra data given as input
2. Preliminary analysis determines
- necessary compounds -- spectra data - forbidden compounds -- spectra data, expert knowledge
3. Generate and test:
a) structure enumerator: can generate all possible compounds - Takes necessary and forbidden lists, and creates a new possible compound - output is formula
b) spectra synthesizer: generates spectra data for this compound
c) matcher - matches synthesized spectra with actual one - compound with best fit is the one
• Note: all compounds checked. Complexity reduced because of the pruning done in step 2
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DENDRAL
• Example rule for analyzer:
Winston 201
• Matcher is involved: needs expert knowledge in knowing when some peaks are more important than others
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2. MACSYMA
• Developed at MIT since 1968 onwards
• Domain: high-performance symbolic math (algebra, calculus, differential equations,...)
• Task: carry out complex mathematical derivations
• Input: formulae and commands (interactive)
• Output: Solutions to tough problems
• Method: Brute force (expert techniques are encoded as algorithm)
• Architecture: programmed in Lisp (300,000 lines of code)
• Results: Widely used, powerful system.
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MACSYMA
p.136-7 Harmon
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3. Hearsay I and II
• Developed at Carnegie-Mellon in late 1960's
• Domain: speech understanding for simple database query
• Task: Using specific vocabulary and grammar criteria, generate correct speech recognition
• Input: Speech wave
• Output: Ordered list of hypotheses of what was said, plus database query based on best guess
• Architecture: Opportunistic, agenda-based reasoning, using "blackboard" to record hypotheses from multiple independent knowledge sources
• (Definition: Blackboard: common working memory for independent systems)
• Tools: Programmed in SAIL
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HEARSAY
• Results: - proved feasibility of automated speech recognition
- pioneering effort in system architecture techniques - blackboard for multiple knowledge sources
- power of symbolic computation over purely statistical ones
- Spawned other expert system projects.
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HEARSAY
Harmon 138
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HEARSAY
Harmon 139
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4. INTERNIST/CADUCEUS
• Developed at U of Pittsburgh in early 1970's, and used ever since
• Domain: diagnostic aid for all of internal medicine
• Task: medical diagnosis given interactive input
• Input: Answers to interactive queries
• Output: ordered set of diagnoses
• Architecture: forward chaining with with "scores" for diseases
• Tools: programmed in Lisp
• Results: widely used, still being developed.
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INTERNIST
p.141-144 Harmon
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INTERNIST
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5. MYCIN
• Stanford U in mid 70's
• Domain: Medical diagnosis for bacterial and meningitis infections
• Task: interview physician, make diagnosis and therapy recommendations
• Input: Answers to queries
• Output: Ordered set of diagnoses and therapies
• Architecture: rule-based exhaustive backward chaining with uncertainty
• Tools: programmed in LISP (shell called EMYCIN -- empty MYCIN)
• Results: not in general use, but was ground-breaking work in diagnostic consultation systems
•
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MYCIN
p.16-20 Harmon
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6. Prospector
• Developed at SRI international in late 1970's
• Domain: exploratory geology
• Task: evaluate geological sites
• Input: geological survey data
• Output: maps and site evaluations
• Architecture: rule-like semantic net with uncertainty
• Tools: programmed in LISP, and is a descendant of MYCIN
• Results: In one blind test, the program identified a previously undiscovered site, thus showing commercial viability of expert systems.
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PROSPECTOR
p. 146 Harmon
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PROSPECTOR
p. 145 Harmon
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7. PUFF
• Developed at Stanford in 1979
• Domain: Diagnosis of obstructive airway diseases using MYCIN's inference engine and a new knowledge base
• Task: Take data from instruments and dialog, and diagnose type and severity of disease
• Input: instruments, queries
• Output: Written report for physician to review and annotate
• Architecture: rule-based, exhaustive backward chaining with uncertainty
• Tools: EMYCIN (Empty MYCIN)
• Results: Reports correct 86% of the time. A 55-rule system is in daily use, running in Basic!
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PUFF
p.150 Harmon
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PUFF
p. 151 Harmon
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8. XCON (R1)
• Originally called R1, developed at Carnegie Mellon and DEC in late 70's
• Domain: configure computer hardware
• Task: configure VAX systems by projecting the need for subassemblies given a high-level description of the system
• Input: Vax system description
• Output: list of parts, accessories, and a plan for assembly
• Architecture: forward-chained, rule-based, with almost no backtracking
• Tools: OPS5, a production system tool
• Results: In use by DEC and performs better than previous experts (since fired)
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XCON
p. 156 Harmon
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XCON
• as of 1991, XCON has 8000 (!) production rules
• a serious problem has developed: maintenance
• has been said that XCON replaced 75 experts with 150 XCON maintainers
• shows the need for developing better maintenance systems for large expert systems (and other large software systems)
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Some other famous systems
• DELTA/CATS:
- diagnose and repair diesel locomotives - developed in LISP, but ported to FORTRAN (a common phenomena)
• DRILLING ADVISOR:
- diagnose oil drilling problems - rule-based, exhaustive backward chaining with uncertainty, frames
• GENESIS:
- designs molecular genetics experiments and procedures - used by over 500 research scientists
• GATES:
- airline gate assignment and tracking system - used by TWA at JFK airport - implemented in Prolog on microcomputers - access database for 100 daily flights, and creates gate assignment in 30 seconds (experts took between 10 and 15 hours, with 1 hour per modification)
( possible extension: lost luggage!)
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Conclusion
p. 170 Harmon
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A typical industrial system
• (Byte, Oct 1994) Picker International
• Problem domain:
– Picker produce sophisticated medical diagnostic machines
– needed a system for use by their service technicians
– tasks:• intelligent service expert system: full explanation, graphical UI,
hypertext user manual• onsite access to main service DB of user site data• capture site data: feedback for knowledge base improvements• use site data to improve products, service effectiveness in future
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System
• Built with Carnegie Group’s TestBuilder system– shell system geared towards diagnostic systems– systems are typically: hierarchical, rule-based, object-oriented
(frames)– multi-level explanation important
• rule-level: how, why• deeper level: hypertext manuals (interactive, graphical)
– TestBuilder is interactive KB editor and tester– Final system is compiled into DOS executable form– TestView is run-time system
• Compared with general-purpose shells, this system is specialized– inference focusses on problem right away, via menu’s or natural
language input– completeness sacrificed for efficient focus on possible problem
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Conclusions from Pickers system
• Incremental design of system
– get prototype running on initial problems
– build onto it
• Can help if Knowledge engineer has domain knowledge
– caveat: here, KE is already “computer-oriented”
– caveat:problem domain well-adapted to Testbuilder paradigm
• On-site capture of new data permits continual update of system for “free”
• empirical data capture and DB useful for KB, as well as products themselves
• integrated standalone systems (eg. laptops) very handy!
– CD ROM’s also can prevent need to download data