famous expert systems

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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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DENDRAL

Winston

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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