1. Knowledge Component 6: Knowledge Utilization
Systems for Decision
Support
2nd Edition

2. Module Information Intended audience Key words Author
Novice
Key words
Decision support, knowledge, representation, reasoning, maintenance, user interfaces
Author
Ian Smith, EPFL, Switzerland
Reviewer (1st Edition)
Ni-Bin Chang, U of Central Florida 2

3. What there is to learn
The quizzes at the end summarize important aspects. General ideas are:
Knowledge systems may help engineers reason with knowledge
Due to knowledge maintenance challenges, rules sets should be kept small and causal directions should be maintained.
A review of the knowledge in the knowledge base is necessary at regular intervals

4. Outline Knowledge Systems Important Characteristics
Representation of Knowledge
Reasoning with Knowledge
Importance of User Interfaces
Knowledge Maintenance

5. Introduction
A Knowledge System (KS) is broadly defined as a computer-based system that aids the process of decision making. KSs are most useful in situations where traditional algorithms have exponential complexity.
Knowledge Systems can also be of assistance when dealing with ill-structured tasks (Module 1.3).

6. Introduction (cont’d.)
KSs are used as stand alone systems and also in combination with other components such as object oriented programs, data bases (Module 4.1) and search algorithms (Module 5.2.3).
Knowledge systems have evolved within the context of successes and failures with expert systems since the 1970s.

7. KS ≠ Engineer
For important tasks, KSs cannot replace engineers. Their usefulness depends on the abbilities of engineers who use them. They can make good engineers perform better and vice versa (bad engineers may become worse!).
The engineer bears the legal responsibility for the decisions. KSs are similar to books in that they can only guide the engineer.

8. Important Characteristics
Outline
Knowledge Systems
Important Characteristics
Representation of Knowledge
Reasoning with Knowledge
Importance of User Interfaces
Knowledge Maintenance

9. Characteristics of a KS
Provides support for tasks with many possible solutions
Helps users identify good solutions. More generally, a good KS encourages user participation.
Has an explicit representation of knowledge
Provides greater separation between knowledge and control than with traditional approaches

10. Diagram Engineer developer Chunks of knowledge and data
Control
Human-Computer Interface
Algorithms
Engineer User

11. Representation of Knowledge
Outline
Knowledge Systems
Important Characteristics
Representation of Knowledge
Reasoning with Knowledge
Importance of User Interfaces
Knowledge Maintenance

12. What is Knowledge?
There are many definitions of ‘knowledge’. Some have broad coverage with vague sociological definitions while others are very restrictive.
For the purposes of this course, we will use a rather restrictive definition that is sufficiently general from a computer science perspective.
Note that dictionary definitions may not be completely compatible with our definitions.

13. Information
Knowledge is part of information. Information is a “class” that includes data and knowledge.
Data are facts about something. For example, this car is red; that building has five stories; the temperature is 20°C; his name is Paul, etc.
These are attribute-value pairs. In relational databases, attributes are columns and values are the entries (numbers, symbols) in tables (Module 4.1).

14. Knowledge
Knowledge is created by expressing relationships between data. Following are examples of possible links.
classification (this car is a kind of vehicle)
compositional (this building is part of a school)
empirical (the exchange rate is CHF 1.50/$)
physical principles (F=m∙a)

15. Knowledge (cont'd.) Knowledge can also be
semantic (Paul is the name of a boy)
heuristic (very long bridges are suspension bridges)
causal (if the temperature is 100°C then water boils)

16. Causal Knowledge
Causal knowledge, or rules, are important elements of engineering reasoning.
Rules may represent links such as physical principles, empirical knowledge and heuristics.
Causal knowledge is also easy to implement in computers because most computer languages have if … then … statements.

17. Control Knowledge
Knowledge is also concerned with linking procedures, rather than data, together.
This is sometimes called control knowledge. An example of control knowledge is a set of steps to study for an exam.
The topic of control knowledge is out of the scope of this course.

18. Knowledge Engineering
Knowledge Engineering (KE) refers to the building, maintaining and development of knowledge systems.
In civil engineering, much knowledge is already organized in codes, standards and other conventions.
Also, experience has shown that successful knowledge systems are built and maintained exclusively by people who are experts in their civil engineering sub-field.
Therefore, this course does not cover aspects related to knowledge elicitation and also, strategies related to knowledge representation are only summarized.

19. Explicit Representation
In computer science, the some fields of artificial intelligence deal with explicit representation and use of knowledge.
Many researchers believe that any intelligence must include knowledge as defined here.
This requirement is currently a topic of debate.

20. Representing Knowledge
How is knowledge represented in knowledge systems?
The most common frameworks for representing knowledge are
Data bases – relations and functional dependencies (Module 4.1)
Object hierarchies and other types of semantic networks

21. Representing Knowledge (cont'd.)
Rules (if...then... statements)
Models – models can be mathematical formulas, numerical representations (such as finite-element models) and simulations.
Models are used to transform causes into effects through explicit use of geometry, physical principles, empirical relationships and other relationships between data.

22. Representing Knowledge (cont'd.)
Traditional knowledge systems (1970s) employed data and rules. This approach is still useful for small systems in well-defined domains where knowledge seldom changes.
Since implementation of rules often requires a transformation of knowledge into if… then… statements, important information may be lost.
Also, unwanted dependencies may be added (See Module 4.1).

23. Representing Knowledge (cont'd.)
Current systems use data, objects, rules, models and cases. The number of rules is kept to a minimum.
Models are often the most natural and robust way to represent knowledge. They are usually easier to modify and control than rules.

24. Reasoning with Knowledge
Outline
Knowledge Systems
Important Characteristics
Representation of Knowledge
Reasoning with Knowledge
Importance of User Interfaces
Knowledge Maintenance

25. Reasoning with Knowledge
In this section, we limit our discussion to reasoning with rules.
More sophisticated methods exist to support reasoning with other representations of knowledge such as models and semantic networks. However, this is out of the scope of this course.

26. Reasoning with Rules
There are many ways to use rules to support engineering reasoning.
The two principal methods are
Forward chaining
Backward chaining
Bi-directional chaining is employed to have rules operate in both directions. This is not in the scope of this course.

27. Forward Chaining
Forward chaining uses the data that are currently available to satisfy the conditions of the rules (if…).
If conditions are satisfied, the rule is fired. This means that consequences (then…) are added to the currently available data.
This new augmented data set is then compared with other conditions in other rules to see if they can further augment the data set.
Chaining stops when no more rules can be fired.

28. Backward Chaining
Backward chaining requires an additional element. This is a goal or a question regarding a fact.
For example, a question may be whether a data value is “true”. In backward chaining, a rule is identified that has as its consequence the affirmation of the question.
The conditions of this rule are examined to see if they satisfy the data set. If they don’t, the conditions of this rule are taken as sub-goals for subsequent iterations.
Chaining stops when there are no more rules or when a pre-defined stopping condition is reached.

29. Forward or Backward Chaining?
Both strategies often provide useful results for the same task.
From the viewpoint of computational efficiency, forward chaining is best when rule antecedents (the “if” part) are simpler than the rule consequents (the “then” part). This is typically the case for control tasks, process monitoring and some configuration activities.
When rule antecedents are longer than the rule consequents, backward chaining is best. This occurs for tasks such as diagnosis, evaluation and certification.

30. Exercise
Task
You are a building inspector for a large insurance company. You are responsible for evaluating their buildings through on-site inspections. This insurance company has hundreds of buildings scattered around the country. You cannot visit each one on a regular basis.
A knowledge system has been created to help you decide which buildings require on-site inspections.
Apply forward and backward chaining to the following rules.

31. Exercise (cont'd.) Rules
R1: If water damage is possible and if the building
has not been renovated for 30 years
Then on-site inspection is necessary
R2: If the plumbing system is suspect
Then water damage is possible
R3: If the building is more than 40 years old
Then the plumbing system is suspect

32. Exercise (cont'd.)
Data
Building ‘D450’ is more than 40 years old and it has not been renovated for 30 years.
Question
Is an on-site inspection necessary?
Fill in the following tables for this exercise.

33. Exercise (cont'd.) Forward chaining Rules examined Rules fired
Data used
Building is more than 40 years old
Building has not been renovated for 30 years

34. Exercise (cont'd.) Backward chaining Rules examined Rules fired
New goal

35. Exercise (cont'd.) Forward chaining: Solution Rules examined
Rules fired
Data used
R1, R2, R3 R3
Building is more than 40 years old
R1, R2 R2
The plumbing system is suspect R1
Water damage is possible
Building has not been renovated for 30 years
On-site inspection is necessary

36. Exercise (cont'd.) Backward chaining: Solution Rules examined
Rules fired
New Goal R1
Water damage is possible R2
The plumbing system is suspect R3

37. Number of combinations
Discussion
The number of combinations of buildings to visit in a given time period is exponential in terms of the total number of buildings.
Therefore, this is a good task for the application of knowledge systems for decision support.
Number of combinations
Total number of buildings

38. Discussion (cont'd.)
The information in the data set has to be compatible with the conditions and consequences of the rules.
For example if the building has never been renovated, this information has to be transformed a priori into the fact that the building has not been renovated for 30 years.
When such transformations are performed, there may be a loss of information.

39. Discussion (cont'd.)
Addition of rules may require modifications to existing rules.
For example, a rule R4 is added.
R4: If the building was built by XYZ contractor
Then the plumbing is ok
With this new rule R4, rule R3 should be modified to add the condition “not built by XYZ contractor” in order to avoid inconsistencies.
Thus, the addition of a rule created an unwanted dependency that lead to modifications elsewhere.

40. Review Quiz - 1 What are the main characteristics of KSs?
What is the difference between forward chaining and back ward chaining? List the ideal applications.

41. Answers to Review Quiz - 1
What are the main characteristics of KSs?
Provides support for tasks with many possible solutions
Helps users identify good solutions. More generally, a good KS encourages user participation.
Has an explicit representation of knowledge
Provides greater separation between knowledge and control than with traditional approaches

42. Answers to Review Quiz - 1
What is the difference between forward chaining and back ward chaining?
Forward chaining approach starts with some facts and applies rules to find all possible conclusions, whereas backward chaining starts with the desired conclusions and works backwards to find supporting facts.
These approaches can be viewed as two variations on search.

43. Answers to Review Quiz - 1
List the ideal applications
Forward chaining are better for control tasks, process monitoring and some configuration activities.
Backward chaining systems are better for diagnosis, evaluation and certification tasks.

44. Importance of User Interfaces
Outline
Knowledge Systems
Important Characteristics
Representation of Knowledge
Reasoning with Knowledge
Importance of User Interfaces
Knowledge Maintenance

45. How should the multiple solutions be presented to the user?
User Interfaces
Many solutions are possible for most engineering tasks and often, there is no unique best solution, especially when it is known that information is incomplete. This is where user interfaces are useful to help the user choose the appropriate solution. A well-conceived user interface (UI, User Interface) is essential for engineering applications.
How should the multiple solutions be presented to the user?
It depends on the task, the user and the context of the task.

46. Well-conceived UI
Following are example guidelines for a well conceived user interface (UI).
Introduce information that is specific to the task
Present multiple solutions to the user in an understandable fashion
Allow exploration of solutions within the space of good solutions
Provide for addition of knowledge

47. Well-conceived UI (cont'd.)
Explain reasoning so that the user is better able to understand why certain solutions have been proposed and why others have been eliminated.
Proactively “cue” users to make good choices
Indicate inconsistent information
Evaluate different combinations of choices
Teach users how to use the system

48. Well-conceived UI (cont'd.)
Different combinations of task characteristics, user needs and contexts result in a wide range of appropriate user interfaces.
Formal models of engineer-computer interaction are emerging. However, in many engineering areas, developers are currently crafting solutions without explicit development frameworks.

49. Evaluating Usability of a UI
The quality of a UI may be measured according to many factors. For example,
Ease of learning
Efficiency of use
Error frequency, severity, and recovery
Ability to propose multiple solutions
Subjective satisfaction
The importance of each factor depends on the goal of the application.

50. Knowledge Maintenance
Outline
Decision Support Systems
Important Characteristics
Representation of Knowledge
Reasoning with Knowledge
Importance of User Interfaces
Knowledge Maintenance

51. Maintenance
Difficulties associated with knowledge maintenance contributed to the demise of early expert systems.
As rule sets grew, the necessity to check different combinations of fired rules grew exponentially.
The maintenance costs also grew in the same manner.

52. Difficulties
It has already been discussed that rule sets may not be able to include all information.
Moreover, they may create unwanted dependencies that complicate knowledge maintenance.
While such difficulties cannot be avoided altogether, other representations of knowledge require less maintenance.

53. Causal Inversion
Much of the maintenance difficulties that were associated with early knowledge systems arose out of a lack of understanding of task analysis and fundamental logic (Module 1.3).
Often, abductive tasks (for example, design and diagnosis) were supported by rules that did not maintain the correct causal direction. The correct rules were transformed using causal inversion.

54. Causal Inversion (cont'd.)
More specifically, the causal direction
If cause Then effect
was inverted to
If effect Then cause

55. Causal Inversion (cont'd.)
Developers often did not realize that this inversion hid the importance of the closed world hypothesis for abductive tasks (all rules and data are known).
Although rule chaining implements deduction, it is deduction only when rules are expressed correctly.

56. Causal Inversion (cont'd.)
Stated otherwise, rules were formulated for abductive tasks so that deductive mechanisms (chaining) could process them.
Such formulation makes an implicit closed world assumption.
Since any modification or addition to the rule set means that all rules are not known, difficulties with knowledge maintenance is an obvious consequence.

57. Modern Approaches
Modern approaches to knowledge representation involve formulations that maintain the correct causal direction.
In this way, when abductive tasks are supported, the close world assumption is explicit.
In general, the number of rules are kept to a minimum. This facilitates knowledge maintenance as fewer unwanted dependencies are created.

58. Examples Examples of correct formulations are:
if environment then behavior
if loads then deformations
if deterioration then less strength

59. Review Quiz - 2
How can the usability of a user interface be evaluated?
What approaches can help minimize difficulties in knowledge maintenance?

60. Answers to Review Quiz - 2
How can the usability of a user interface be evaluated?
The quality of a UI may be measured according to many factors. For example,
Ease of learning
Efficiency of use
Error frequency, severity, and recovery
Ability to propose multiple solutions
Subjective satisfaction
The importance of each factor depends on the goal of the application.

61. Answers to Review Quiz - 2
What approaches can help minimize difficulties in knowledge maintenance?
Difficulties in knowledge maintenance arise due to causal inversion and because of unwanted dependencies being created.
Maintaining the correct causal direction helps keep the number of rules to a minimum and avoids the risk of unwanted dependencies.

62. Further Reading
M. Stefik. Introduction to Knowledge Systems, Morgan Kaufman Publishers, San Fransisco, California, 1995
M. Ginsberg. Essentials of Artificial Intelligence, Morgan Kaufman Publishers, San Fransisco, California, 1993
Raphael, B. and Smith, I.F.C. Fundamentals of Computer-Aided Engineering, Wiley, 2003