Rule-Based Expert Systems

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Rule-Based Expert Systems
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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

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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.

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Some History

• In the early to mid 1980s 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.

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Overstatement of Capabilities

• The results of several expert systems were
  oversold.
• Prospector didnt 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.

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Frequently Cited Examples of Expert Systems

• MYCIN
• Infectious blood disease diagnosis
• Dendral
• Analyzed mass-spectra (chemistry)
• PROSPECTOR
• Geological analysis
• R1
• Configure a VAX computer

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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.

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Rule-based Expert Systems

• Sets of IF-THEN rules are established to codify
  expert knowledge.
• If ltantecedentgt then ltconsequentgt
• Antecedents can be combined using logical
  operators
• If ltagt and ltbgt or ltcgt then ltconsequentgt
• 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.

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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.

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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

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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

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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.

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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
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Backward Chaining

• Prolog uses backward chaining
• Work backward from the goal.
• Check rules that can provided the desired goal.

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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
Z
Y
B
C
E
D
D
E
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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.

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Conflict Resolution

• What happens when two rules provide conflicting
  conclusions?
• If it has feathers then it is a bird
• If it cant fly then it is not a bird
• What if has feathers, but cant fly?

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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.

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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.

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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

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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

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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.

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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)

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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.

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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

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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.

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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

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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.