Chapter 9Rules and Expert Systems

Definition of Expert SystemAn expert system is a computer program that represents and reasons with knowledge of some specialist subject with a view to solve problems or giving advice.

Chapter 9 Contents (1)Rules for Knowledge RepresentationRule Based Production SystemsForward ChainingConflict ResolutionMeta RulesBackward ChainingThe Architecture of Expert SystemsExpert System Shells

Chapter 9 Contents (2)The Rete AlgorithmKnowledge EngineeringCLIPSBackward Chaining in Expert SystemsCYC

Rules for Knowledge RepresentationIF THEN Rules can be used to represent knowledge:IF it rains, then you will get wetIF A THEN B Can be expressed as: A BHere, A is called antecedent, and B is called consequent.Rules can also be recommendations:IF it rains, then you should wear a coat

Rule Based Production SystemsA production system is a system that uses knowledge in the form of rules to provide diagnoses or advice on the basis of input data.The system consists of:1. A database of rules (knowledge base),2. A database of facts, and 3. An inference engine which reasons about the facts using the rules.

Forward Chaining(Data Driven System)Forward chaining is a reasoning model that works from a set of facts and rules towards a set of conclusions, diagnoses or recommendations.When a fact matches the antecedent of a rule, the rule is triggered, then the rule is fired, and the conclusion of the rule is added to the database of facts.

ExampleConsider the rules that is used to control an elevator in a 3-story building:Rule 1:IF on the first floor and button is pressed on first floorTHEN open doorRule 2:IF on first floorAND button is pressed on second floorTHEN goto second floor

ExampleRule 3:IF on first floorAND button is pressed on third floorTHEN go to third floorRule 4:IF on second floorAND button is pressed on first floorAND already going to third floorTHEN remember to go to first floor later ,

The example only listed a subset of the rules (partial rules), we can keep doing the list of the rules to finish the control.

How do you finish the list?

Conflict ResolutionSometimes more than one rule will fire at once, and a conflict resolution strategy must be used to decide which conclusions to use.One strategy is to give rules priorities and to use the conclusion that has the highest priority.Other strategies include applying the rule with the longest antecedent, or applying the rule that was most recently added to the database.

ExampleConsider the following rules:IF it is coldTHEN wear a coatIF it is cold THEN stay at homeIF it is coldTHEN turn on the heatObviously, one antecedent can trigger three rules here. We might need to give the rules priorities to decide which one to pick first. Or, we can add more conditions to make some antecedents longer than others.

ExampleBy applying priorities:

IF patient has painTHEN prescribe painkillers priority 10IF patient has chest painTHEN treat for heart disease priority 100

Make Differences in Antecedents According Their LengthIF patient has painTHEN prescribe painkillerIF patient has chest painAND patient is over 60AND patient has history of heart conditionsTHEN take to emergency room

Meta RulesThe rules that determine the conflict resolution strategy are called meta rules.Meta rules define knowledge about how the system will work.For example, meta rules might define that knowledge from Expert A is to be trusted more than knowledge from Expert B.This kind of knowledge is called meta knowledge.It is knowledge about knowledge. Meta rules are treated by the system like normal rules, but are given higher priority.

Backward ChainingIn cases where a particular conclusion is to be proved, backward chaining can be more appropriate.Works back from a conclusion towards the original facts.When a conclusion matches the conclusion of a rule in the database, the antecedents of the rule are compared with facts in the database.

Comparing Forward and Backward ChainingChoose forward or backward chaining will depend on the specific problems.Sometimes backward chaining is much faster and sometimes forward chaining is much faster.Sometimes, they are about the same.

ExampleRules:1. A B C2. A B3. C D E4. B E F G5. A E H6. D E H IFacts: 1. A 2. B 3. FGoal: to prove H.

By using Forward ChainingFacts Rules Triggered Rule FiredA, B, F 1, 2 1A, B, C, F 2 2A, B, C, D, F 3 3A, B, C, D, E, F 4, 5 4A, B, C, D, E, F, G 5 5A, B, C, D, E, F, G, H 6 Stop

Backward ChainingFacts Goal Matching RulesA, B, F H 5A, B, F E 3A, B, F C, D 1A, B, C, F D 2A, B, C, D, F StopThis specific example has made us use 1 rule less than forward chaining.

The Architecture of Expert Systems (1)An expert system uses expert knowledge derived from human experts to diagnose illnesses, provide recommendations and solve other problems.

The Architecture of Expert Systems (2)Knowledge base: database of rules (domain knowledge).Explanation system: explains the decisions the system makes.User Interface: the means by which the user interacts with the expert system.Knowledge base editor: allows the user to edit the information in the knowledge base.

Expert System ShellsThe part of an expert system that does not contain any domain specific or case specific knowledge is the expert system shell.A single expert system shell can be used to build a number of different expert systems.An example of an expert system shell is CLIPS.

The Rete AlgorithmA rete is a directed, acyclic, rooted graph (a tree).A path from the root node to a leaf represents the left hand side of a rule.Each node stores details of which facts have been matched so far.As facts are changed, the changes are propagated through the tree.This makes an efficient way for expert systems to deal with environments which change often.

Knowledge EngineeringA knowledge engineer takes knowledge from experts and inputs it into the expert system.A knowledge engineer will usually choose which expert system shell to use.The knowledge engineer is also responsible for entering meta-rules.

CLIPSCLIPS is C Language Integrated Production System an expert system shell.CLIPS uses a LISP-like notation to enter rules.

Backward Chaining in Expert SystemsBackward chaining is often used in expert systems that are designed for medical diagnosis:For each hypothesis, H:If H is in the facts database, it is proved. Otherwise, if H can be determined by asking a question, then enter the users answer in the facts database. Hence, it can be determined whether H is true or false, according to the users answer.Otherwise, find a rule whose conclusion is H. Now apply this algorithm to try to prove this rules antecedents.If none of the above applies, we have failed to prove H.Usually backward chaining is used in conjunction with forward chaining.

CYCA frame based production system.Uses a database of over 1,000,000 facts and rules, encompassing all fields of human knowledge.CYC can answer questions about all kinds of knowledge in its database, and can even understand analogies, and other complex relations.

What is CYC?CYC is the name of a very large, multi-contextual knowledge base and inference engine, the development of which started at the Microelectronics and Computer Technology Corporation (MCC) in Austin, Texas during the early 1980s.

CYCOver the past eleven years the members of the CYC team have added to the knowledge base a huge amount of fundamental human knowledge: facts, rules of thumb, and heuristics for reasoning about the objects and events of modern everyday life.

CYCCYC is an attempt to do symbolic AI on a massive scale. It is not based on numerical methods such as statistical probabilities, nor is it based on neural networks or fuzzy logic. All of the knowledge in CYC is represented declaratively in the form of logical assertions. CYC presently contains approximately 400,000 significant assertions, which include simple statements of fact, rules about what conclusions to draw if certain statements of fact are satisfied (true), and rules about how to reason with certain types of facts and rules. New conclusions are derived by the inference engine using deductive reasoning.

CYCWho is doing CYC?Much of the CYC work has been done at the Microelectronics and Computer Technology Corporation in Austin, Texas. As of the First of January in 1995, a new independent company named Cycorp has been created to further the work done on the CYC project. Cycorp continues to be based in Austin, Texas. The development of CYC has been supported by several organizations, including Apple, Bellcore, DEC, DoD, Interval, Kodak, and Microsoft.