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One Definition of Expert System
A computing system capable of representing andreasoning about some knowledge rich domain,
which usually requires a human expert, with aview toward solving problems and/or giving
advice.
the level of performance makes it expert
Some also require it to be capable of explaining its
reasoning. Does not have a psychological model of how the
expert thinks, but a model of the experts model of
the domain.
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Categories of Expert Systems
Category Problem AddressedPrediction Inferring likely consequences of given situations
Diagnosis Inferring system malfunctions from
observations, a type of interpretationDesign Configuring objects under constraints, such as
med orders
Planning Developing plans to achieve goals (care plans)
Monitoring Comparing observations to plans, flaggingexceptions
Debugging Prescribing remedies for malfunctions
(treatment)Repair Administer a prescribed remedy
Instruction Diagnosing, debugging, and correcting studentperformance
Control Interpreting, predicting, repairing, andmonitoring system behavior
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Knowledge in a Knowledge Base
Knowledge specific to the domain + facts specific to the problembeing solved
A medical KB is defined in HANDBOOK of MEDICAL INFORMATICS as:
a systematically organized collection of medical knowledge that is accessible
electronically and interpretable by the computer. They note a medical KB usually:
includes a lexicon (vocabulary of allowed terms) and
specifies relationships between terms in the lexicon.
For example, in a diagnostic KB, terms might include: patient findings (e.g., fever or pleural friction rub),
disease names (e.g., nephrolithiasis or lupus cerebritis) and
diagnostic procedure names (e.g., abdominal auscultation or chest computedtomography).
Knowledge Representation is the key issue Aim is usually to present the knowledge in as "declarative" a fashion as possible
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Traditional Feature Comparisons:
E/KBS versus ANNE/KBS Symbolic
Logical Mechanical
Serial
Rule Based
Needs Rules
Much Programming Requires Reprogramming
Needs an Expert
Neural Networks
Numeric
Associative Biological
Parallel
Example Based
Finds Rules
Little Programming Adaptive System
Needs a Database
But much of this too simple, KBS are not really logical and can use examples etc.
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Medical Expert and KB systems
are designed to give expert-level, problem-specific advice inthe areas of :
medical data interpretation, patient monitoring,
disease diagnosis,
treatment selection, prognosis, and
patient management.
Research in medical expert and knowledge-based systemsand the development of such systems has been mostsignificant to the broad realm of quality assurance and cost
containment in medicine.
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One Distinction Between an Expert System
and a Knowledge-Based System
To be classified as an expert system the systemmust be able to explain the reasoning process.
This is often accomplished by displaying the rules
that were applied to reach a conclusion.
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Some Basic Concepts
Knowledge representation deals with the formal modeling ofexpert knowledge in a computer program. Important questions in this respect concern the given degree of
structuralization of the medical domain under consideration, the necessity to
include vagueness of medical terms and uncertainty of medical conclusionsinto the chosen formal representation, as well as the extent and completionof the respective knowledge domain.
Reasoning mechanisms are inference methods which draw
medical conclusions from given patient data by means of thestored medical knowledge. Most important is the selection of the appropriate formal approach with
respect to the given medical domain.
One differentiates methods to infer logical conclusions (e.g., propositionaland predicate logic, three-valued logic, fuzzy logic, non-monotonic logic)and to combine medical evidence (e.g., Bayes theorem, certainty factors,Dempster-Shafer theory of evidence).
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Assertional Knowledge
It might be a detailed description of a complex domain
like a disease, a linguistic structure, etc.
This type of knowledge is used to describe a givenclinical situation usually in an object structure.
This is done by associating the different elements or
objects characterizing the context inside the same
framework with the consideration of the relationships
between these objects.
Example: an exhaustive description of a specific disease
organized following: the set of its symptoms, its possible
treatments, medicines, etc.
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Alternative KB Approaches
Rule-based approach
Events trigger firing of rules (condition/action pattern)
e.g. Arden Syntax and Medical Logic Modules (MLM)
Case & Model-based approach
Create a model (template) of clinical guidelines e.g. PRODIGY, EON, PROforma, GLIF
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But AI is a broad field - a tree representation
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Knowledge-base may really include many things
Knowledge-base
HeuristicsHypothesis Rules
Facts
Processes
Events
DefinitionsRelationships
Attributes
Objects
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user
KBS Editor
Inference Engine
Explanation System
GeneralKnowledge-Base
Case SpecificKnowledge-Base
User Interface
may employ:
question &
answer
menu-driven
natural
language, or
GUI styles
KBS architecture and components
Knowledge Acquisition
Module
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Knowledge representation formalisms
& Inference
KR Inference* Logic Resolution principle
* Production rules backward (top-down, goal directed)
forward (bottom-up, data-driven)
* Semantic nets &
Frames Inheritance & advanced reasoning
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A Representation: First-Order Logic
Constants: Mr_Smith, Dr._Jones, anemia
Variables: X, Y
Functions: Address(X), Age(Y)
Predicates: Diagnosis(X, anemia); Male(Y); Patient(Z)
Negation: Male(X); Name(X, Smith)
Connectors:
Conjunction (AND): Patient(X) Male(X) Disjunction (OR): Doctor(X) Nurse(X)
Logical implication: Female(X) Male(X)
Quantifiers: Universal quantifier: X (Patient(X) Doctor(X))
Existential quantifier: Y (Patient(Y) Name(Y, Jones))
From Yuval Shahar, Frame-Based Representations and Description Logics
Temporal Reasoning and Planning in Medicine
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Relationship Of this K to a DB
Representing patient X has Diabetes in a table:
Diabetes (x)
A table called Diabetes with column (s) identifyingpatient x and a column of the value of Diabetes (x)
Has_Diagnosis (x, Diabetes) A table called Diagnosis with column (s) identifying
patient x, and diagnosis y and a column of the value of
Has_Diagnosis (x, y) Has (x, Diagnosis, Diabetes)
A table called observation with column (s) identifying
patient x, observation type y and observation value z
and a column of the value of Has x z
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Experts typically form sets of rules to apply to a givenproblem
Set of rules reflects the skill of the expert on a topic; usedifferent rule sets to reflect problem-solving competenceof expert
Need a strategy to know when to apply them ie use metarules
Rule sets often represented in a tree-like structure withmost general, strategic rules at the top of the tree; mostspecific rules at leaf nodes
Adopts a top-down approach to problem-solving, whererule sets only used when appropriate; reflects human approach divide and conquer
eases modular development each module may use different representation and reasoning
techniques (say for body system)
Production Rule sets
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21
Rules & Decision Tree Example
Q1: Test is
Q3: the
Cost is
Q2: the
Panel cost
is
Q3: the
cost is
Q3: the
cost is
Q3: the panel
cost is
c1: 30%
chance
Rule 1
c2: 70%chance
Rule 2
c3: 10%
chance
Rule 7
c2: 70%
chance
Rule 8
c1: 30%
chance
Rule5
c2: 70%
chance
Rule6
c1: 30%
chance
Rule3
c2: 70%
chance
Rule 4
included
2K 3K 4K
=x24hrs) to determine bacterial species
Classified as a "production- rule" system, depth-first, backwardchaining.
Given patient data (incomplete & inaccurate) MYCIN gives interimindication of organisms that are most likely cause of infection &drugs to control disease
Uses certainty factors to handle incomplete and uncertain information, includedthe "how" and "why" capabilities that are now considered essential, definingcharacteristics of Expert Systems.
Drug interactions & already prescribed drugs taken into account
Able to provide explanation of diagnosis (limited) Thoroughly documented in Buchanan and Shortliffe Rule Based Expert
Systems, Addison- Wesley, Reading, Mass., 1984.
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Top-level goal rule
IF there is an organism which
requires therapy, and
consideration has been given
to the possibility of additional
organisms requiring therapy
THEN compile a list of possibletherapies, and determine the
best therapy in this list.
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THERAPY rule
IF the identity of the organism
is Pseudomonas
THEN I recommend therapy fromamong the following drugs:
1 - COLISTIN (.98)2 - POLYMYXIN (.96)
3 - GENTAMICIN(.96)
4 - CARBENICILLIN (.65)
5 - SULFISOXAZOLE (.64)
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THERAPY rule
The number with each drug is the akin to the
probability that a Pseudomonas will be sensitive tothe named drug.
To select the actual therapy, the drugs on the list are
screened for contra-indications and to minimize the
number of drugs administered, while maximizing
sensitivity.
T i l RB E i
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Typical RB Exercise:
Write Rules by Diagnosis
Write rules for patients with the following
diagnoses (one at a time):
diabetes mellitus
heart failure
myocardial infarction benign prostatic hyperplasia
K Engineer compares notes and leads discussion on integration.
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Evaluation of MYCIN
In 1974, an initial study of MYCIN was conducted
where five experts approved 72% of MYCIN's
recommendations on 15 actual cases.
The system was improved and in 1979 MYCIN was
again compared to experts.
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MYCINs Performance Compared to
Human ExpertsMYCIN 52 Actual
Therapy
46
Faculty-1 50 Faculty-4 44
Faculty-2 48 Resident 36Inf. Dis
fellow
48 Faculty-5 34
Faculty-3 46 Student 24
Ratings by 8 experts on 10 cases
Perfect score = 80
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MYCIN is not currently in use:
Knowledge base is incomplete, does not cover a fullspectrum of infectious diseases.
computing power was not available in most hospitalwards.
MYCIN's development lead to the development of
"EMYCIN" - for "Empty MYCIN". To demonstrate this capability, they developed "EMYCIN",
the first shell.
The developers of MYCIN believed that the programmingapproaches they used in MYCIN could be applied to otherdomains.
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1 The need justifies cost.
2 The (human) expertise* is not available in allsituations where it is needed.
3 The problem may be solved using symbolicreasoning techniques.
4 The domain is well structured and does not requirecommon sense reasoning.
5 The problem may not be (better) solved using other(traditional) computing methods.
6 Cooperative and articulate experts exist.
7 The problem is of proper size and scope. This isrelative to resources and evolving technology.
What makes an ES feasiblefeasible ?
Life Cycle for Developing Expert
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Life Cycle for Developing Expert
Systems
Problem Definition
Knowledge Acquisition
Knowledge Representation
Prototype system
Operational system
Knowledge base maintenance
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Knowledge Acquisition
" the transfer and transformation of potential
problem-solving expertise from some knowledge
source to a program.
- Buchanan 1983.
machine learning - building capabilities into the
system that allow it to learn from what it is doing. the problem of induction - how many instances must be
observed before it can be added to the knowledge base
as "true"
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knowledge elicitation - extract the
knowledge from the human expert, throughsome means
direct - interaction with the human expert
interviews, protocol analysis, direct
observation, etc.
indirect - utilize statistical techniques to analyzeof data and draw conclusions about the
structure of the data.
Knowledge Acquisition (cont.)
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Knowledge Representation
A method to represent the knowledge you are
eliciting and/or learning. Several major methods rules, bayes nets, frames
Strengths and weaknesses for each. None is completely dominant.
Trent is to build heterogeneous systems, that s whatexperts are.
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Knowledge Representation
A method to represent the knowledge about thedomain
Three major symbolic methods: rules
semantic objects
logic Although a shell contains a way to representknowledge, shell selection should be influenced
by the matching the representation to theknowledge in the domain.
Knowledge must be coordinated, so that the
knowledge base is consistent.
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Prototype system
Typically use an "incremental" development
approach to an expert system.
Build an initial prototype and adjust and expand Allow the expert to interact with the prototype to
get feedback
Reevaluate if the project should be continued,
if major redesign (knowledge representation)
is necessary, or to go ahead.
Build Operational System & Knowledge base
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Build Operational System & Knowledge base
maintenance
Once The actual system is built
New rules can be continually added and old ones
refined/ removed.
This is a tricky process, but there does not
seem to be much literature on it. One characteristic of an Expert system should
be maintainability, so the ability to
add/change/delete rules is essential.
M di l K l d ( dj i i i )
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Medical Knowledge (Adjusting to Situations)
Biochemical lab rulesGo from simple, modular to confusing complications
From Toward Situated Knowledge AcquisitionTim Menzies,
Int. J of Human Computer Studies, 1998
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Disadvantages of Production Knowledge
Difficult to maintain for Very Large-KB- One reason is addition of new, contradictory knowledge. Consider
Rule 1. IF it is raining
THEN not (weather is sunny)Rule 2. IF location is Florida
THEN not (weather is cloudy)
Rule 3. IF it is late afternoon
THEN weather is sunny or weather is cloudy
FACTS: it is late afternoon location is Florida
Conclude?????
Maintenance is to ADD Rule 4. IF it is late afternoon AND location isFlorida
THEN it is raining
Some observe that RB development never ends.KE is a continuousprocess..
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KBS as real-world problem solvers
- Problem-solving power does not lie with smart reasoning
techniques nor clever search algorithms butdomain dependent real-world knowledge
- Real-world problems do not have a well-defined
solutions in literature- Expertise not laid down in algorithms but are domain
dependent rules-of-thumb orheuristics (cause-and-effect)
- KBS allow this knowledge to be represented incomputer & solution explained
These are not logical
A Semantic Network beyond the ERA
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A Semantic Network beyond the ERA
model for real world problems
A directed graph of vertices (V)and edges (E)
where Vi are concepts and Ei,j are relations
Jim
PersonIS-A
Disease
5 Days
Mumps
Has
Duration
DiagnosisPatient
27 years
Age
MamalAKA
Focus is on
categories of objects
relations between those obj
Semantic Networks:
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Semantic Networks:
Arity of Relations
Unary relations
Person(Jim): IS-A link
Binary relations
Age(Jim, 27 years): Age link
N-ary relations Disease(Jim, Mumps, 5 days): By creating a reified
disease-relation object with several cases (patient,diagnosis, duration)
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Frames
(Minksy, 1975)
A type of Semantic network
Both can be used to represent logic systems
Used to graphically represent taxonomies of
objects and their properties Concepts have roles, or properties, (also
known in OOLs as slots), such as age
Frames encapsulate more meaningful
chunks of knowledge (e.g., birthday party)
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Representing Knowledge in Frames
1. Frame Architecture
- A record-like data structure for representing stereotypical
knowledge about some concept or object (or a class of objects)
- A frame name represents a stereotypical situation/object/process
- Attributes or properties of the object also called slot
- Values for attributes called fillers,facetsprovide additional
control over fillers.
Frame Name:
Class:
Properties: Property 1 Value 1
Object 1
Object 2
Property 2 Value 2
...
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(1) Class Frame
- Represents general characteristics of common objects- Define properties that are common to all objects within class
- Static & dynamic property
Static: describes an object feature whose value does not changeDynamic: feature whose value is likely to change during operation
Frame Name:
Class:
Properties: Color Unknown
Bird
Animal
Eats Worms
No._Wings 2Flies True
Hungry Unknown
Activity Unknown
Types of Frames
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Subclass Frame- Represents subsets of higher level classes or categories
- Creates complex frame structures
- Class relationships
Bird
Robins Canaries Sparrows
Bird1 Bird2 Tweety Bird3 Bird4
Class
Subclass
Instance
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Anything
AbstractObjects Events
Sets NumbersRepresentational
Objects
Intervals
Places
Physical
Objects
Processes
Categories
Sentences Measurements
Moments
Times Weights
Things Stuff
Animals Agents
Humans
A Quick Ontological View
M di l E titi Di ti (MED) St t
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Medical Entities Dictionary (MED) Structure
MedicalEntity
Substance LaboratorySpecimen Event
LaboratoryTest
LaboratoryProcedure
CHEM-7PlasmaGlucose
Plasma
Specimen
Anatomic
Substance
Bioactive
Substance
Glucose
Plasma
Chemical
Carbo-
hydrate
Substa
nce
Sample
d
Part of
HasS
pecimen
SubstanceMeasur
ed
Diagnostic
Procedure
Multiple hierarchy
SynonymsTranslations
Semantic links
Attributes
60,000 concepts
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1. Generalizations ---- Kind of relationship
Bird
Robins Canaries Sparrows
Kind of links
2. Aggregation ---- Part of relationship
Bird
Wings Feather Eyes
Part of links
3. Association ---- Semantic relationship
Bird owns
Nest Food
Semantic links
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(3) Instance Frame
- Represents specific instance of a class frame- Inherits properties & values from the class
- Able to change values of properties & add new
propertiesFrame Name:
Class:
Properties: Colour Yellow
Tweety
Bird
Eats Worms
No._Wings 1
Flies FalseHungry Unknown
Activity Unknown
Lives Cage
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3. Frame Inheritance
Color Unknown
BirdClass Animal
Eats Worms
No._Wings 2Flies True
Hungry Unknown
Activity Unknown
Color Black-white
Penguin
Class Bird
Eats Fish
No._Wings 2
Flies False
Hungry Unknown
Activity Unknown
Lives South_pole
Color Unknown
Canary
Class Bird
Eats WormsNo._Wings 2
Flies True
Hungry Unknown
Activity Unknown
- Instance frame inheritsinformation from its subclass
frame and also its class.
- Inheritance of behavior, facet
-Ease coding & modificationOf information
A Frame Representation-types and
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A Frame Representation types and
instances and defaults.
Mammals
Humans
Jim
AKA
IS-A
Legs: 2
Age:27
Legs: 4
John
Age:16
IS-A
Lions
AKA
Bats
AKA
Legs: 2
Bibi
IS-A
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Implications of Inheritance
Determination of properties of instances
involves a search of the semantic-network
graph
Default reasoning is enabled
high-level nodes can have values that areinherited by many lower-level nodes unless these
values are overridden
Exceptions imply a nonmonotonic logic
Multiple inheritance is possible, but might be
ambiguous when conflicts occur
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- Exception handling- Frame has property value unique to itself must be explicitly encoded
-Multiple inheritance
- It is natural to discuss objects as they relate to different worlds
- An instance can inherit information from different parent- Frame structure takes form of a network
MenAge Unknown
Weight Unknown
EmployeePhone Unknown
Salary Unknown
Jack
Age 30
Weight 78kg
Phone 123456
Salary 12345
4. Facets
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- Provide additional control over property values & operation of
the system- Constraint on property values
limit a numeric property value to a range
restrict data type to Boolean, string or numeric
- Instruction to a property how to obtain value or make reaction tochanged value
- Types of facets
- Type: defines the type of value that can be associated with the property- Default: defines a default value
- Documentation: provides a documentation of the property
- Constraint: defines the allowable values
- Minimum cardinality: establishes the minimum number of values aproperty can have
- Maximum cardinality:
- If-needed: specifies action to be taken if the propertys value is needed
- If-changed: ... changed
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6. Rule interaction
- Hybrid system: combine frames and rules for KR- Pattern matching, variables are used for locating matching
conditions among all frames, ?X, ?Age
Humans
Jack
Legs 1
Age 35
Sex Male
Residence Belfast
Sports Swim
Likes Unknown
Lucy
Legs 2
Age 30
Sex Female
Residence Belfast
Sports Hiking
Likes Unknown
Bob
Legs 2
Age 33
Sex Male
Residence Dublin
Sports Hiking
Likes Unknown
Frame ?X
instance-of HUMANSWITH Residence = Belfast
WITH Age = ?Age
Frame: JACK
instance-of HUMANSResidence = Belfast
Age = 35
Frame: Lucy
instance-of HUMANSResidence = Belfast
Age = 30
Populating a frame :Example
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Populating a frame :Example
Frame: patient
Attribute1: Patient Name.Associated action: ifthe name is unknown
then create a new folder if not, take the
already existing folder.Attribute2: current date.
Associated action: if the patient is known
then calculate the time interval from the lastvisit.
Etc.
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Advantages of Frames
Classes and instances organize a flat
knowledge base (unlike FOL) by introducing
structure on an epistemological level E.g., specialization of subclasses through
restriction of a range of values for a property
Simple; easy to understand
Inheritance is captured in a natural, modular
fashion
Efficient inference (e.g., for validation) by
following links, compared to standard logics
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Problems with Frames
Negation cannot be represented
Jim does not have pneumonia
Disjunction cannot be represented naturally
Jim has Mumps or Rubella
Qualification is not a part of the language
All of Jims diseases are infectious
=> Thus, procedural attachments are often added
The semantics of the links are often not well
defined [Whats in a Link, Woods, 1975]
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- Disadvantages:
Departures from prototypesAccommodation of new situations
Detailing heuristic knowledge
Rule-based Frame-based
Rule 1 Frame - BoilerIF Boiler pressure < 50 Temperature
AND Boiler water level < 3 Water level
THEN Add water to boiler Condition
Rule 2 IF Boiler:Temperature>300
IF Boiler temperature > 300 AND Boiler:Water_level >5
AND Boiler water level > 5 THEN Boiler:Condition = normal
THEN Boiler condition normal
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7. Summarizing Advantages & Disadvantages
(Frames vs. Rules)
Features Rule-based Frame-based
Organization of facts scattered in KB related facts collectedandknowledge (but easy to add) represented within a single frame
Inheritance no inheritance Yes-a frame trade-mark
Inference process general rules & PM general rules & pattern matching
can be slow PM fast
Objects Facets & Message-Passingcommunication
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The Advanced Course: Description
Logics
A subset of FOL designed to focus on
categories and their definitions in terms of
existing relations
More expressive than semantic networks
Major inference tasks:
Subsumption (is category C1 a subset of C2?) Classification (Does Object O belong to C?)
C iti l F t i M DDS F il
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Critical Factors in M-DDS Failures
(after Berner, Luger and Stubblefield) Impossibility of developing an adequate database
Lack of an effective set of decision rules (no end)
Lack of deep (causal) knowledge of the domain (i.e.
systems do not understand physiology)
- Lack of robustness & flexibility. If the knowledge base is
unable to deal with a problem or query not contained
within it, it is unable to resolve or adapt a strategy.
- Unable to provide deep explanations
- Problems in verification
- DSS, in general (unless allied in a hybrid to a CBR, classifier
or neural networks) do not learn from their experience
Arden Syntax and Medical Logic Module
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Arden Syntax and Medical Logic Module
EMYCIN has been used successfully to develop other systems.
But has been overtaken by other approaches such as MedicalLogic Modules. An industry standard maintained by Health Level 7 Org
Organize decision knowledge as a collection of procedural rules(MLMs) that can be triggered by events
Each MLM designed to model knowledge required to make asingle medical decision such as:
Contraindication alerts, management suggestions, data interpretations,treatment protocols, and diagnosis scores
Complex things such as guidelines represented as a collection ofMLMs
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Summary
There are multiple representation formalisms
Frames are a type of semantic networks
A fundamental tradeoff exists in all
formalisms [Levesque and Brachman, 1984],between:
1. Expressive power of a representation language 2. computational tractability of inference with it
References
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References
Yuval Shahar, Frame-Based Representations and Description Logics Temporal Reasoning and Planning in Medicine
http://www.ise.bgu.ac.il/courses/trp/1,
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