& expert systems-general
TRANSCRIPT
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Expert Systems
KR Chowdhary, Associate Professor,
Department of Computer Science & Engineering,MBM Engineering College, JNV University, Jodhpur,
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Defining Expert Systems
Definitions:
Where human expertise is needed to solve problems,expert systems are likely candidates to solve thoseproblems.
What is an expert system (ES)?
A set of programs that manipulate encodedknowledge to solve problems in a specialized domainthat normally require human expertise.
Encoded knowledge is used in inferencing orreasoning process.
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Uses knowledge rather than data for controlthe solution.
Knowledge is maintained separate from
control program ES are capable of explaining howand whya
particular solution arrived.
Uses symbolic representation of knowledge
and performs symbolic computation whichclosely matches human beings.
Uses meta-knowledge.
Characteristic features of ES:
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Applications of Expert Systems
Medical Diagnoses
Diagnosis of complex electronic andelectromechanical systems
Diagnosis of software development projects Weather forecasting
Forecasting crop damages
Identification of chemical compound
structures Location faults in computer and
communication systems
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Applications
Scheduling of Customer orders, jobs in OS, inproductions.
Evaluation of applications for loan, eligibility
criteria, etc. VLSI Design
Military applications, like those in battle fields
Law area, like-civil laws, disputes, etc.
Teaching AI techniques
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Why to use ES?
Commercial viability: whereas there may be only a few expertswhose time is expensive and rare, you can have many expertsystems
expert systems can be used anywhere, anytime
expert systems can have more knowledge than experts expert systems can explain their line of reasoning
Weaknesses:
expert systems are as sound as their KB; errors in rules meanerrors in diagnoses
automatic error correction, learning is difficult (although machinelearning research may change this)
little common sense reasoning: idiot servants
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Rule-based Expert Systems
These are most common type of ES
Uses the knowledge encoded in the form ofproduction rules (ifthen .. rules), i.e.
1. If cond1 and cond2 and cond3
Then take action-12. If temperature > 200 deg and water level low
Then open the safety valve
Each rule represents a small chunk of knowledgerelating the given domain
A number of rules may give a chain of inferencesstarting from some known facts to usefulconclusions.
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Input
Output
UserExplanatio
n Module
I/O
Interface
Editor
InferenceEngine CaseHistory
File
KB WorkingMemory
Learning
Module
Expert System
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Expert System..
User interface:
Question answer type
Menu-driven
Natural language
Graphic interface
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KB: Contains facts and rules about some specialized domain.
Building KB using editor.
Inference Process: Inference Engine accepts user input
queries in response to questions through I/O interface.Inference process is carried out by three stages: match, select,execute.
What is match?
contents of working memory are compared with KB. Whenmatch is found, the corresponding rules are placed in theconflict set (i.e. select). Further it needs instantiations toconfirm match. This follows execution.
Expert System..
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Inferencing
KB Working
Memory
1. Match
Conflict Set
2. Select
3. Execute
Inference Cycle
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Prolog uses backward chaining (goal driven strategy),matching of sub goals for the goal to be achieved.
Rules may be tested exhaustively or selectivelydepending on the control strategy.
The chaining continues as long as matches can befound between clauses in the working memory andthe rules in the KB.
The search can be limited to few hundred rules.
Uncertainty measures can also be used
Prolog rules:
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Explaining how&why?
How did you know that? (for inference result)
Why do you need to know that? (when additionalinformation is asked)
I/O Interface: Permits the user to communicatewith the system in a more natural may by permittingthe the use of simple selection menus or use of a
restricted natural language.
Explanation
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Explanation..
When you are asking why a particular action wasperformed at a node in the tree... how questions: recite the children
why questions: recite the parent
thus backward chaining expert systems naturallysupport explanation can be done during inference: when user asked for data,
s/he can as why and a reason is given
after expert advice given, user can ask how, why, andeven why not (ie. why did some rule fail)
a great tool for KB debugging! forward chaining: explanation not as structured;
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Knowledge Acquisition andvalidation
This is also called knowledge engineeringprocess
Needs acquiring of knowledge and thenencoding it
Knowledge is derived from experts, journals,texts, reports, interviews, etc.
Job is performed by knowledge engineer.
Consolidating of the knowledge may needcollective efforts of many persons.
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Knowledge acquisition process
Domain
Expert KBSystem
Editor
Knowledge
Engineer
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1. Medical expert systemsMedical expert systems are an active area.
Humanitarian uses: remote areas can have world-classexpertise,
medical science is complex; expert systems are intelligent
advisors and assistants MYCIN
diagnose bacterial infections
uses certainty factors: ranked diagnoses are generated
EMYCIN: empty MYCIN - the shell of MYCIN for use in otherapplications
CADUCEUS: expert system with entire internal medicine KB
its been active since 1970s
Examples expert systems
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2. XCON configures VAX systems from user requirements
DEC uses it; has increased throughput of systemdesign
10,000 rules said to have replaced X system designers with 4X
expert system maintainers!
3. DELTA/CATS diesel electric locomotive trouble shooter
problem: system maintenance
Examples expert systems
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4. Jonathans Wave commodities trading
incorporates several experts approaches
runs in Prolog and C
lots of AI groups on Wall Street!
5. DENDRAL(1960): For determining the structure of chemicalcompounds given its specification.
6. PROSPECTOR:Assists the geologists in discovering theminerals deposits.
Examples expert systems
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7. Codecheck expert system to evaluate C source code
complexity, formatting, standards and portability adherence
identifies overly complex code (prime source of programproblems!)
8. CHORAL expert system for harmonizing chorales in the style of J.S.
Bach
similar system: harmonic analysis of tonal music
rules obtained from: studying composers music,
musicologists some systems used for composition (with interesting results)
Examples expert systems
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Expert systems are now ubiquitous (I.e.,present every where)
still lots of active research:
probabilistic knowledge and deduction
large system maintenance
knowledge acquisition popular domains
industrial, financial, medical
legal: interpret laws by the book
education: intelligent tutoring
Some domains remain difficult to implement those requiring artistic creativity and skill
those requiring common sense
The state of the art
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The need for a solution must justify the costsinvolved in development
Human expertise is not available in all situationswhere it is needed
The problem may be solved using symbolic reasoningtechniques
The problem is well structured and does not require(much) common sense knowledge
The problem cannot be easily solved using more
traditional computing methods Cooperative and articulate experts exist
The problem is of proper size and scope
Steps in expert system building:Choosing Problem:
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expert system building is a software engineeringtask, but with a twist: knowledge base KB will be continually refined, corrected, updated
expert system design & implementation steps
1. initial proposal - identification of problem, expert(s), benefits2. create a prototype
3. knowledge engineering: interview expert for many weeks(months)
4. implement experts expertise in KB
5. test expert system; go to 3 (let expert see results)
6. put in production ; refine errors when found
Steps in expert system building
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Knowledge engineering: the extraction of knowledge from anexpert, and encoding it into machine-inferrable form
K.E. is the most difficult part of expert system implementation
the knowledge engineer must extract detailed info from anexpert; but s/he is not expected to understand it
the expert relies on years of experience and intuition: askingthem to deconstruct knowledge is difficult and frustrating
can also be political factors
many strategies for knowledge engineering
multiple KEs
multiple experts
watch expert in the field
continual expert feedback on expert system performance
Knowledge engineering
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Other types of expert system
Called Non-production systems
These are:
Semantic Network based
Frame based
Decision tree based
Neural net based
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Problems of using expertsystems
Choice of domain
Acceptability
Uncertainty
Updating Limitation (ES do not know itself)
Testing
Behavior
Knowledge acquisition
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Advantages of using expertSystems
Availability
Consistency
Comprehensiveness (detailedknowledge)