Rule based expert systems
Sponsored Links
This presentation is the property of its rightful owner.
1 / 28

Rule-Based Expert Systems PowerPoint PPT Presentation


  • 90 Views
  • Uploaded on
  • Presentation posted in: General

Rule-Based Expert Systems. Expert Systems. Acknowledge that computers do not posses general knowledge (common sense) Attempt to train computer in a “limited domain” Experts have deep, often complex knowledge, but generally for a limited domain. Can A Computer Do What an Expert Does?.

Download Presentation

Rule-Based Expert Systems

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Rule-Based Expert Systems


Expert Systems

  • Acknowledge that computers do not posses general knowledge (common sense)

  • Attempt to train computer in a “limited domain”

  • Experts have deep, often complex knowledge, but generally for a limited domain


Can A Computer Do What an Expert Does?

  • Limited domain

    • The system has a finite, and relatively small number of things it needs to “know” about.

    • The processing might be complex, but computers are good at that.

  • Can the expert knowledge be extracted?

    • Knowledge Engineers do the extracting

    • Experts provide their knowledge

    • Programmers encoded the knowledge into an “expert system.”


Some History

  • In the early to mid 1980’s the desire to build “expert systems” was very high in the US and elsewhere.

    • Japanese 5th generation computing project

    • AI researchers aggressively recruited by industry

    • Expert systems were considered by many to be “the next big thing.”


Overstatement of Capabilities

  • The results of several expert systems were oversold.

    • Prospector didn’t really discover millions of dollars worth of Molybdenum.

      • The originators of the system never made this claim, but despite their efforts to stop it, the story is repeated to this day.


Frequently Cited Examples of Expert Systems

  • MYCIN

    • Infectious blood disease diagnosis

  • Dendral

    • Analyzed mass-spectra (chemistry)

  • PROSPECTOR

    • Geological analysis

  • R1

    • Configure a VAX computer


Expert knowledge can be difficult to extract

  • Experts often do not really know how they do things themselves. Although the expert can perform the tasks, he/she does not necessarily have access to the mechanism used.

  • Experts have reasons to be uncooperative in the process of disseminating their expertise.

  • Experts often disagree on both processes and conclusions.

  • The process might require judgment that is not easily codified.


Rule-based Expert Systems

  • Sets of IF-THEN rules are established to codify expert knowledge.

    • If <antecedent> then <consequent>

      • Antecedents can be combined using logical operators

        • If <a> and <b> or <c> then <consequent>

    • IF “3 enemy stones in a row” AND

      NOT “3 friendly stones in a row”

      Then “place a stone in the row with 3 enemy stones.”


Knowledge Engineers

  • Tasked with working with the expert to extract expertise and codify in a set of rules.

  • Has training in the development of expert systems, but not necessarily in the application domain.

  • Know the capabilities of the technology and knows how to apply it.


Expert System Shells

  • Separate the mechanisms for making inference from the rule base

  • Facilitate the entry of rules by non-programmers

  • Provide reuse for what would otherwise be redundant code across expert systems


Expert System Components

  • Inference Engine

    • Forward or Backward-chaining

    • Conflict resolution algorithms

  • Rule-base

    • IF-THEN rules

  • Database

    • Current state on which IF-THEN rules are applied.

  • Explanation Facilities

    • An important advantage rule-based expert systems hold over other types of AI


Inference Engines

  • Forward-chaining

    • Submit current data to all rules

    • Rules make conclusions, which in turn, generate new data

    • “Inference Chains” result from initial data and the data generated in conclusions.

  • Backward-chaining

    • Try to prove a conclusion by working backwards from ways to prove it.


Forward-chaining Example (A,B,E, and D are given)

  • If Y and D then Z

  • If X and B and E then Y

  • If A then X

  • If C then L

  • If L and M then N

A

X

B

Y

Z

L

C

D

E

Example from Negnevitsky


Backward Chaining

  • Prolog uses backward chaining

    • Work backward from the goal.

    • Check rules that can provided the desired goal.


Backward Chaining Example

  • If Y and D then Z

  • If X and B and E then Y

  • If A then X

  • If C then L

  • If L and M then N

A

X

B

Y

Z

B

C

E

D

D

E


Forward or Backward Chaining?

  • What do experts use?

  • Are we trying to prove a particular hypothesis?

    • Backward chaining

  • Are we trying to find all possible conclusions?

    • Forward chaining

  • What does the rule set look like?

    • Could be either one or a combination of both.


Conflict Resolution

  • What happens when two rules provide conflicting conclusions?

    • If it has feathers then it is a bird

    • If it can’t fly then it is not a bird

    • What if has feathers, but can’t fly?


Conflict Resolution Methods

  • Use rule-order as an implied priority

    • The first rule to provide an answer is used.

  • Assign a priority to each rule, the rule with the higher priority is sustained.

  • Longest Matching Strategy uses the rule with the most specific information.

    • If it cannot fly and has feathers then it is a bird.

  • Certainty-based conflict resolution

    • Measures of certainty are provided for data and rules. Most certain rule is sustained.


Frame-based Expert Systems

  • Frame

    • Marvin Minsky (1975)

  • Frame-based Expert Systems utilize frames to encapsulate data and methods about an entity.

  • Frames are similar to objects, but the data types and processing methods are quite different.


Frames

  • A frame is a data structure with typical knowledge about a particular object or concept [Negnevitsky].

    • Frame is a collection of attributes called “slots”

      • Example slots for a truck

        • Engine size

        • Number of wheels

      • Slots consists of attribute/value pairs called facets

      • Value/18

      • Default Value/4

      • Range/[3-18]

      • User Query/”Enter the number of wheels:”


Demons

  • Slots or facets can contain procedures that are executed with the data is accessed or changed

    • When_Changed demon is executed when new information is placed in a slot.

      • Might include forward chaining or backward chaining rules

    • When_Needed demon is executed when information is read from a slot

      • Might include code to read sensors or try to prove a goal


Inheritance

  • Frames can inherit from other frames.

    • Frame implicitly contains all the slots contained in the inherited frame unless the frame overrides the slot with its own definition.

    • Inheritance is established with the “IS-A” relationship

    • In frames, inheritance is principally used to provide default values, rather than structure and methods.


Other Frame Relationships

  • Aggregation (a-part-of)

    • An engine is a-part-of a car

    • A spark plug is a-part-of an engine

  • Association (Other semantic relationships)

    • Examples

      • Ownership (computer has-owner Joe)

      • Uses (dentist uses drill)

      • Location (Joe is-near theDesk)


No Limits on Relationships

  • Frame can employ multiple inheritance

  • Frame can have any number of relationships

  • Relationships can be of any type that is useful.


Interactions of Frames and Rules

  • Different frame-based systems use different mechanisms.

  • Rules are often invoked by demons. Some systems allow different rule sets to be applied to different frames


Why Use Frames?

  • In large systems, frames can provide the system the capability to find relevant information quickly.

  • Making inferences from the most relevant information can provide greater efficiency and allow searches to be constrained.

  • Relationships between frames can be provided at a relatively low cost.


Advantages of Expert Systems

  • Provide an explanation capability

    • What rules fired to provide the conclusion?

    • Why other conclusions were not made.

  • For simple domains, the rule-base might be simple and easy to verify and validate.

  • The system might use a method similar to what the expert uses.

  • Expert system shells provide a means to build simple systems without programming


Disadvantages of Expert Systems

  • When the number of rules is large, the effect of adding new rules can be difficult to assess.

  • Expert knowledge is not usually easily codified into rules.

  • Expert often lack access to their own analysis mechanisms.

  • Validation/Verification of large systems is very difficult.

  • Track record does not seem to contain many successes. Relatively high-risk to implement.


  • Login