Template knowledge models

1
Template knowledge models

• Reusing knowledge model elements

2
Lessons

• Knowledge models partially reused in new
  applications
• Type of task main guide for reuse
• Catalog of task templates
• small set in this book
• see also other repositories

3
The need for reuse

• prevent "re-inventing the wheel"
• cost/time efficient
• decreases complexity
• quality-assurance

4
Task template

• reusable combination of model elements
• (provisional) inference structure
• typical control structure
• typical domain schema from task point-of-view
• specific for a task type
• supports top-down knowledge modeling

5
A typology of tasks

• range of task types is limited
• advantage of KE compared to general SE
• background cognitive science/psychology
• several task typologies have been proposed in the
  literature
• typology is based on the notion of system

6
The term system

• abstract term for object to which a task is
  applied.
• in technical diagnosis artifact or device being
  diagnosed
• in elevator configuration elevator to be
  designed
• does not need to exist (yet)

7
Analytic versus synthetic tasks

• analytic tasks
• system pre-exists
• it is typically not completely "known"
• input some data about the system,
• output some characterization of the system
• synthetic tasks
• system does not yet exist
• input requirements about system to be
  constructed
• output constructed system description

8
Task hierarchy
9
Structure of template description in catalog

• General characterization
• typical features of a task
• Default method
• roles, sub-functions, control structure,
  inference structure
• Typical variations
• frequently occurring refinements/changes
• Typical domain-knowledge schema
• assumptions about underlying domain-knowledge
  structure

10
Classification

• establish correct class for an object
• object should be available for inspection
• "natural" objects
• examples rock classification, apple
  classification
• terminology object, class, attribute, feature
• one of the simplest analytic tasks many methods
• other analytic tasks sometimes reduced to
  classification problem especially diagnosis

11
Classification pruning method

• generate all classes to which the object may
  belong
• specify an object attribute
• obtain the value of the attribute
• remove all classes that are inconsistent with
  this value

12
Classificationinference structure
13
Classification method control

• while new-solution generate(object -gt candidate)
  do
• candidate-classes candidate union
  candidate-classes
• while new-solution specify(candidate-classes -gt
  attribute)
• and length candidate-classes gt 1 do
• obtain(attribute -gt new-feature)
• current-feature-set new-feature union
  current-feature-set
• for-each candidate in candidate-classes do
• match(candidate current-feature-set -gt
  truth-value)
• if truth-value false
• then candidate-classes candidate-classes
  subtract candidate

14
Classification method variations

• Limited candidate generation
• Different forms of attribute selection
• decision tree
• information theory
• user control
• Hierarchical search through class structure

15
Classification domain schema
16
Rock classification
17
Nested classification
18
Rock classification prototype
19
Assessment

• find decision category for a case based on
  domain-specific norms.
• typical domains financial applications (loan
  application), community service
• terminology case, decision, norms
• some similarities with monitoring
• differences
• timing assessment is more static
• different output decision versus discrepancy

20
Assessment abstract match method

• Abstract the case data
• Specify the norms applicable to the case
• e.g. rent-fits-income, correct-household-size
• Select a single norm
• Compute a truth value for the norm with respect
  to the case
• See whether this leads to a decision
• Repeat norm selection and evaluation until a
  decision is reached

21
Assessmentinference structure
case
abstract
abstracted
norms
select
specify
case
evaluate
norm
norm
decision
match
value
22
Assessment method control

• while new-solution abstract(case-description -gt
  abstracted-case) do
• case-description abstracted-case
• end while
• specify(abstracted-case -gt norms)
• repeat
• select(norms -gt norm)
• evaluate(abstracted-case norm -gt norm-value)
• evaluation-results norm-value union
  evaluation-results
• until has-solution match(evaluation-results -gt
  decision)

23
Assessment control UML notation
24
Assessment method variations

• norms might be case-specific
• cf. housing application
• case abstraction may not be needed
• knowledge-intensive norm selection
• random, heuristic, statistical
• can be key to efficiency
• sometimes dictated by human expertise
• only acceptable if done in a way understandable
  to experts

25
Assessment domain schema
26
Claim handling for unemployment benefits
27
Decision rules for claim handling
28
Diagnosis

• find fault that causes system to malfunction
• example diagnosis of a copier
• terminology
• complaint/symptom, hypothesis, differential,
  finding(s)/evidence, fault
• nature of fault varies
• state, chain, component
• should have some model of system behavior
• default method simple causal model
• sometimes reduced to classification task
• direct associations between symptoms and faults
• automation feasible in technical domains

29
Diagnosis causal covering method

• Find candidate causes (hypotheses) for the
  complaint using a causal network
• Select a hypothesis
• Specify an observable for this hypothesis and
  obtain its value
• Verify each hypothesis to see whether it is
  consistent with the new finding
• Continue this process until a single hypothesis
  is left or no more observables are available

30
Diagnosisinference structure
31
Diagnosis method control

• while new-solution cover(complaint -gt hypothesis)
  do
• differential hypothesis add differential
• end while
• repeat
• select(differential -gt hypothesis)
• specify(hypothesis -gt observable)
• obtain(observable -gt finding)
• evidence finding add evidence
• foreach hypothesis in differential do
• verify(hypothesis evidence -gt result)
• if result false then differential
  differential subtract hypothesis
• until length differential lt 1 or no observables
  left
• faults hypothesis

32
Diagnosis method variations

• inclusion of abstractions
• simulation methods
• see literature on model-based diagnosis
• library of Benjamins

33
Diagnosis domain schema
34
Monitoring

• analyze ongoing process to find out whether it
  behaves according to expectations
• terminology
• parameter, norm, discrepancy, historical data
• main features
• dynamic nature of the system
• cyclic task execution
• output "just" discrepancy gt no explanation
• often coupling monitoring and diagnosis
• output monitoring is input diagnosis

35
Monitoringdata-driven method

• Starts when new findings are received
• For a find a parameter and a norm value is
  specified
• Comparison of the find with the norm generates a
  difference description
• This difference is classified as a discrepancy
  using data from previous monitoring cycles

36
Monitoring inference structure
37
Monitoring method control

• receive(new-finding)
• select(new-finding -gt parameter)
• specify(parameter -gt norm)
• compare(norm finding -gt difference)
• classify(difference historical-data -gt
  discrepancy)
• historical-data finding add historical-data

38
Monitoring method variations

• model-driven monitoring
• system has the initiative
• typically executed at regular points in time
• example software project management
• classification function treated as task in its
  won right
• apply classification method
• add data abstraction inference

39
Prediction

• analytic task with some synthetic features
• analyses current system behavior to construct
  description of a system state at future point in
  time.
• example weather forecasting
• often sub-task in diagnosis
• also found in knowledge-intensive modules of
  teaching systems e.g. for physics.
• inverse retrodiction big-bang theory

40
Synthesis

• Given a set of requirements, construct a system
  description that fulfills these requirements

41
Ideal synthesis method

• Operationalize requirements
• preferences and constraints
• Generate all possible system structures
• Select sub-set of valid system structures
• obey constraints
• Order valid system structures
• based on preferences

42
Synthesisinference structure
43
Design

• synthetic task
• system to be constructed is physical artifact
• example design of a car
• can include creative design of components
• creative design is too hard a nut to crack for
  current knowledge technology
• sub-type of design which excludes creative design
  gt configuration design

44
Configuration design

• given predefined components, find assembly that
  satisfies requirements obeys constraints
• example configuration of an elevator or PC
• terminology component, parameter, constraint,
  preference, requirement (hard soft)
• form of design that is well suited for automation
• computationally demanding

45
Elevator configuration knowledge base reuse
46
Configurationpropose revise method

• Simple basic loop
• Propose a design extension
• Verify the new design,
• If verification fails, revise the design
• Specific domain-knowledge requirements
• revise strategies
• Method can also be used for other synthetic tasks
• assignment with backtracking
• skeletal planning

47
Configuration method decomposition
48
Configuration method control

• operationalize(requirements -gt hard-reqs
  soft-reqs)
• specify(requirements -gt skeletal-design)
• while new-solution propose(skeletal-design
  design soft-reqs -gt extension) do
• design extension union design
• verify(design hard-reqs -gt truth-value
  violation)
• if truth-value false then
• critique(violation design -gt action-list)
• repeat select(action-list -gt action)
• modify(design action -gt design)
• verify(design hard-reqs -gt truth-value
  violation)
• until truth-value true
• end while

49
Configuration method variations

• Perform verification plus revision only when for
  all design elements a value has been proposed.
• can have a large impact on the competence of the
  method
• Avoid the use of fix knowledge
• Fixes are search heuristics to navigate the
  potentially extensive space of alternative
  designs
• alternative chronological backtracking

50
Configuration domain schema
51
Types of configuration may require different
methods

• Parametric design
• Assembly is largely fixed
• Emphasis on finding parameter values that obey
  global constraints and adhere to preferences
• Example elevator design
• Layout
• Component parameters are fixed
• Emphasis on constructing assembly (topological
  relations)
• Example mould configuration
• Literature Motta (1999), Chandrasekaran (1992)

52
Assignment

• create mapping between two sets of objects
• allocation of offices to employees
• allocation of airplanes to gates
• mapping has to satisfy requirements and be
  consistent with constraints
• terminology
• subject, resource, allocation
• can be seen as a degenerative form of
  configuration design

53
Assignmentmethod without backtracking

• Order subject allocation to resources by
  selecting first a sub-set of subjects
• If necessary group the subjects into
  subject-groups for joint resource assignment
• requires special type of constraints and
  preferences
• Take an subject(-group) and assign a resource to
  it.
• Repeat this process until all subjects have a
  resource

54
Assignmentinference structure
55
Assignmentmethod control

• while not empty subjects do
• select-subset(subjects -gt subject-set)
• while not empty subject-set do
• group(subject-set -gt subject-group)
• assign(subject-group resources
  current-allocations -gt resource)
• current-allocations lt subject-group,
  resource gt union
• current-allocations
• subject-set subject-set/subject-group
• resources resources/resource
• end while
• subjects subjects/subject-set
• end while

56
Assignmentmethod variations

• Existing allocations
• additional input
• subject-specific constraints and preferences
• see synthesis and configuration-design

57
Planning

• shares many features with design
• main difference "system" consists of activities
  plus time dependencies
• examples travel planning planning of building
  activities
• automation only feasible, if the basic plan
  elements are predefined
• consider use of the general synthesis method (e.g
  therapy planning) or the configuration-design
  method

58
Planning method
59
Scheduling

• Given a set of predefined jobs, each of which
  consists of temporally sequenced activities
  called units, assign all the units to resources
  at time slots
• production scheduling in plant floors
• Terminology job, unit, resource, schedule
• Often done after planning ( specification of
  jobs)
• Take care use of terms planning and
  scheduling differs

60
Schedulingtemporal dispatching method

• Specify an initial schedule
• Select a candidate unit to be assigned
• Select a target resource for this unit
• Assign unit to the target resource
• Evaluate the current schedule
• Modify the schedule, if needed

61
Schedulinginference structure
62
Schedulingmethod control

• specify(jobs -gt schedule)
• while new-solution select(schedule -gt
  candidate-unit) do
• select(candidate-unit schedule -gt
  target-resource)
• assign(candidate-unit target-resource -gt
  schedule)
• evaluate(schedule -gt truth-value)
• if truth-value false then
• modify(schedule -gt schedule)
• end while

63
Scheduling method variations

• Constructive versus repair method
• Refinement often necessary
• see scheduling literature
• catalog of Hori (IBM Japan)

64
Scheduling typical domain schema
65
Modeling

• included for completeness
• "construction of an abstract description of a
  system in order to explain or predict certain
  system properties or phenomena"
• examples
• construction of a simulation model of nuclear
  accident
• knowledge modeling itself
• seldom automated gt creative steps
• exception chip modeling

66
In applications typical task combinations

• monitoring diagnosis
• Production process
• monitoring assessment
• Nursing task
• diagnosis planning
• Troubleshooting devices
• classification planning
• Military applications

67
Example apple-pest management
68
Comparison with O-O analysis

• Reuse of functional descriptions is not common in
  O-O analysis
• notion of functional object
• But see work on design patterns
• strategy patterns
• templates are patterns of knowledge-intensive
  tasks
• Only real leverage from reuse if the patterns are
  limited to restricted task types