Week 6 - Knowledge Representation, Inferencing, Knowledge Engineering and CLIPS

Assignment:

Prepare a brief (paragraph or so) description/proposal describing what you plan to do for the term project. Email this to me by Friday of week 6 or turn in a paper copy by Friday of week 6.

Review this document.

Read Chapters 3 (Methods of Inference) and 7 (Introduction to CLIPS) in the text "Expert Systems: Principles and Programming."

To try clips commands you read about in Chapter 7, type clips from the UNIX prompt on pasture or one of its workstations. Typing (exit) from the CLIPS prompt will exit CLIPS and return you to the UNIX prompt.

Important Terms and Concepts:

Follow this link to learn more about the steps commonly followed in the development of expert systems. Compare and contrast these with the model development steps and the methodology of systems analysis.

The ES development stages and brief overviews of the important issues for each stage/step are described in the following sections.

Knowledge acquisition continues to be a bottleneck in the development of ES. Learn more about recommended approaches to knowledge acquisition. A few notes concerning knowledge acquisition are also available.

Knowledge representation and reasoning notes should be examined.

Identification

requirements analysis
- external requirements
- form of input and output
- setting where ES will be used
- user
must identify
- participants
- problems
- objectives
- resources
- costs
- time frame

Conceptualization

designing the ES to ensure that specific interactions and relationships in the problem domain are understood and defined
initial knowledge acquisition
key concepts, relationships between objects and processes, and control mechanisms are determined
understanding what expert does:
- Exactly what decisions do the experts make?
- What are the decision outcomes?
- Which outcomes require greater reflection, exploration, or interaction?
- What resources or inputs are required to reach a decision?
- What conditions are present when a particular outcome is decided?
- At what point after exposure to influential inputs is a decision made?
- How consistently do these conditions predict a given outcome?
- Given the particulars of a specific case, will the outcome predictions of the knowledge engineering team be consistent with those of the expert?

Formalization

organizing the key concepts, subproblems, and information flow into formal representations
design program logic

Implementation

formalized knowledge is coded into the framework of the development tool or language
document
help for the user and explanations
how will the ES interact with other programs and databases

Testing

verification of individual relationships
validation of program performance
evaluation of utility of the software

Knowledge Acquisition

major bottleneck to building ES
3 areas central to knowledge acquisition
1. evaluation of domain to determine if the knowledge in the domain is suitable for an ES
2. source of expertise must be identified and evaluated
3. if expertise source is a person, the specific knowledge acquisition techniques and practitioners need to be identified

Knowledge Acquisition Techniques

observe expert solving real problems
identify kinds of data, knowledge, and procedures required to solve different types of problems
use scenarios with the expert
have expert solve a series of problems verbally
develop rules based on interviews
have expert review rules and general problem solving procedure
compare responses of outside experts to those of expert and ES
regularly scheduled interviews
questionnaires
decision trees

Domain Selection

must have an expert
must be general consensus among experts about the accuracy of solutions
experts must be able to communicate details
domain should be narrow and well-defined and solutions must not require common sense

Representation and Reasoning

Rule-Based Systems

pattern matching
parameter driven
forward chaining
backward chaining

Pattern Matching in a Forward Chaining System

IF condition
THEN consequent (or action).


IF the volume of a ?product-container is less than a specified
amount,
THEN increase the fill volume.


IF the ?sensor is dead,
THEN inspect ?sensor.

Forward Chaining Example

Facts:
     the volume of item29 is 21
     the volume of item17 is 18
     the temperature of item23 is -5

Rule:
       IF the volume of ?item is < 20,
       THEN the fill amount of ?item should be increased

Parameter Driven Systems

many parameter driven systems based on MYCIN
facts are represented by parameter values
parameters can have various attributes called properties

Decision Trees

information found at one step in the solution determines what information is needed at the next step
each item is represented as a node in a tree with possible values forming arcs that emanate from the node

Reasoning With Uncertainty

much of the information humans use is fuzzy or inexact

2 kinds of uncertainty

among facts and relationships of the problem (the rules)

information supplied by the user

IF the average number of cockleburs per foot 
of soybean row is <0.5,
THEN the application of herbicide to control 
cockleburs is usually
not economical.


I'm almost certain the weed is Jimson but there 
is a slight
chance it is a cocklebur.  I know it is definitely 
not foxtail.

MYCIN CF
- numerical
- range from -100 to +100

Object-Based Systems

Frames

represent declarative knowledge (answers the question What?)
represents procedural knowledge (answers the question How?)
stored as a functional quality of object
provide a mechanism for inheritance

Inheritance example


Charlotte is a member of the Landshire class.
Landshire is a member of the swine class.
An attribute of swine is the existence of a curly tail.

Through inheritance, Charlotte has a curly tail.

Hybrid knowledge-based systems might combine frames and rules.

Object-Oriented Programming

object - any data structure that has a symbolic name

objects represent things
can also represent generic concepts
have properties and behaviors
data are stored within the programs that manipulate them

messages - requests between objects

Truth Maintenance

new information exists independently from other information in the knowledge base
no logical links between facts and rules (a fact has no knowledge about how it was derived, what other facts it depends on, or what other facts depend on it)
useful for hypothetical reasoning, planning, scheduling, and design
dependency networks, Justification-based Truth Maintenance, Assumption-based Truth Maintenance