EXPERT SYSTEMS

• What is an expert system?
• When should you build one?
• Representation of expert knowledge
• Acquisition of expert knowledge
• Reasoning under unceratinty
• Generation of Explanations

What is an Expert System?

• A program that embodies problem solving knowledge of a human expert.
• Includes procedural as well as descriptive knowledge.
• Problem solving expertise comes from knowledge rather than reasoning.
• Expert systems represented a change in direction in AI research away from general problem solvers.
• Expert systems made AI a commerical venture.

Applications of Expert Systems

• process control (eg. BHP)
• finance (eg. bank loan approvals, insurance claims, credit card authorisation)
• farming (eg. CSIRO - Sirotac, Auspig)
• Oil prospecting (eg.Schlumberger, BP)
• fault diagnosis (eg. Kodak - injection molding, HP - semiconductor manufacturing, NASA - space shuttle, NCR - preventative maintenance)
• configuring computer systems (eg. DEC, IBM)
• design (eg. Xerox - designing paper handling systems)
• assembly planning (eg. Hazeltine - PC boards)
• production sheduling (eg. Carlton and United Breweries)

Expert Systems

• Program = Knowledge + Inference
• Every program contains some knowledge from the person who wrote the specification.
• Most programs also encode how to infer new knowledge from old.
• Programs written in traditional languages, like C, have the knowledge embedded in the code.

Traditional Programs

• Once coded, the knowledge is hard to identify and hard to change.
• As more knowledge is encoded, programs become hard to manage.
• Complexity makes it impossible to add real intelligence to an ordinary program.

Knowledge is Explicit

Knowledge Base

• The systems 'long term memory'.
• It contains the rules, objects and relationships required by the system to solve a problem.
• A critical decision in designed in expert system is how knowledge should be represented

Working Memory

The short term memory which contains input, output and temporary data produced by the execution of application of knowledge in the knowledge base.

Inference Engine

• The program the decides how to apply the knowledge in the knowledge base to the information in the working memory.
• There are different types of inference mechanisms that approach problem solving in different ways.
• It is important to chose an inference mechanism appropriate to the domain.

When to build an expert system

• The problem must be well defined.
• There does not exist a known, efficient algorithm for solving the prolem.
• The domain of knowledge required to solve the problem is well bounded.
• There are heuristics that are adequate.
• The task has high rewards.
• There is an
  • articulate
  • enthusiastic
  • expert
  • who has time to spare
• The company supports the project.

The Inference Engine

Forward Chaining

• Given some facts, work forward through inference net.
• Discovers what conclusions can be derived from data.
• If more than one rule matches
  • may fire all rules and simulate parallel machine
  • may select only one rule

	loop
		match rules
		if no rule matched then stop
		resolve conflicts
		act
	end loop

Backward Chaining

• To determine if a decision should be made, work backwards looking for justifications for the decision.
• Eventually, a decision must be justified by facts.

Reasoning when knowledge is certain

• When knowledge is certain, the conclusions are also certain.
• We can use the normal rules of logic to deduce conclusions.

Uncertain Reasoning

• Often, experts can't give definite answers.
• May require an inference mechanism that derives conclusions by combining uncertainties.

Explanations

• An expert systems seeks to make problem solving knowledge explicit.
• The knowledge applied to a problem must be available to the user.
• The system must be able to explain how it arrived a conclusion and why it is performing some computation.
• It may also be required answer what if questions

Answering HOW?

• To answer how a conclusion was reached, work back through the inference chain.
• Decision 4 was made as a result of making decision 1 and decision 2.
• Decision 1 was made because Facts 1, 2 & 3 are true, etc.

Answering WHY?

• To answer why a computation is being performed, the system must state its current goal.
• The system may ask the user if fact 3 is true because it is trying to determine if decision 1 should be made.

Asking Questions

• The order in which questions are asked is important.
• Some questions can quickly eliminate unnecessary computation (and further questions).

Information from other programs

• Anyone installing a computer probably already uses computers.
• May already have:
  • data bases
  • CAD tools
  • math labraries
  • accounting systems
• Expert system may have to interact with any of these programs.

Input from Sensors

• In process control, monitoring equipment provides data on the function of the plant.

• The data gathered by sensors can be inputs to the expert system.

• If the plant is computerised then the expert system can control the plant.

• The expert system may monitor the plant for abnormal conditions and then recommend action by an operator or directly control the plant.

• Useful when traditional process control can't cope.

Building an Expert System

Expert System Shells

• Shells are good rapid prototyping tools.

• They may also be good delivery platforms if the applications does not require much 'customising'.

• Shalls may use different:
  • knowledge representation
  • inference mechanisms
  • HCI

Steps in building an expert system

1. Introductions to expert systems and to domain.
2. Feasibility study/prototypes.
3. Build production system.
   • Knowledge acquisition
   • Choosing representation for knowledge
   • Build inference mechanism
   • Build user interface
   • Field testing and revision
4. Field Testing.

Knowledge Representation: Rules

	if <condition> then <action>

		if	room is bathroom
		and	area < min
		then	increase area to min

• Condition part contains patterns that may match entries in working memory.
• Action part contains instructions for modifying the working memory.
  • add a new item to working memory
  • remove an item from working memory
  • modify an existing item

Knowledge Representation: Networks

• In a rule base system, the network is implicit from the execution of the rules.
• The network may never actually be constructed.
• Some expert systems use explicit networks rather than rules to repesent knowledge.
• Advantage of networks:
  • The chain of reasoning is explicit and can be visualised easily.
• Disadvantage:
  • Difficult to maintain a large knowledge base

Knowledge Representation: Frames

• Frames represent objects as sets of attribute/value pairs.
• Objects can contain programs as well as data.
• One of their most important features is that they have an inheritance mechanism.
• E.g. a generic house can be described.
• When instances of the house are created, they inherit all of the attributes of the generic object.
• This makes it easy to construct and manipulate a complex knowledge base.
• Inference is distributed among the demons in the slots of frames
• Inference is ad hoc.

Knowledge Acquisition

• The hardest technical problem in building an expert system is acquiring the knowledge.
• Experts are often unable to explain their expertise.
• May use knowledge elicitation aids.
• May use machine learning to acquire knowledge automatically from data.
• Experts must learn what ES can do:
  • Get rid of unrealistic expectations
  • Get rid of undue skepticism
• Knowledge engineer must learn how to talk to expert:
  • Acquire background knowledge
  • book knowledge
  • learn vocabulary.
• Protocol Anlaysis
  • Follow an expert during problem solving.
  • Think aloud.
• Task specification
• Rapid prototyping

Induction

• Building rules from large amounts of data
  • loan assessment
  • sinter plant
  • medical diagnosis
  • 2D vision
  • sound
• Allowing can expert to instruct the system through carefully chosen examples:
  • space shuttle auto-lander
  • fault diagnosis.
• At present only induction programs that represent examples as attribute lists are practical.
• More powerful induction systems are under development.

Induction Example

Information from referring doctor

• female, age 27, on t4, pregnant

Assay results:

Thyrotropin	(TSH)	30
Triiodothyronine	(T3)	3.3
Total Thyroxine	(TT4)	70
Thyroxine uptake	(T4U)	1.71
Free thyroxine index	(FTI)	41
Thyroxine binding globulin	(TBG)	-

Interpretation:

• Elevated T3 and T4U consistent with pregnancy.
• Elevated TSH and low FTI suggest under replacement.

Rules

	if	FTI < 64.5
	and	TSH > 6
	and	on antithyroid medication
	then	patient is hypothyroid