ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM]

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ARTIFICIAL INTELLIGENCEINTELLIGENT AGENTS
PARADIGM
KNOWLEDGE PROCESSINGIN RULE-BASED EXPERT SYSTEMS

• Professor Janis Grundspenkis
• Riga Technical University
• Faculty of Computer Science and Information
  Technology
• Institute of Applied Computer Systems
• Department of Systems Theory and Design
• E-mail Janis.Grundspenkis_at_rtu.lv

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Knowledge Processingin Rule-Based Expert Systems
DATA-DRIVEN REASONING(FORWARD CHAINING) The start
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Knowledge Processingin Rule-Based Expert Systems
DATA-DRIVEN REASONING (continued) Askable premise
for initialization Q The engine does not turn
over? A false
Rule 2 fails consideration moves to Rule 3,
where the first premise fails.
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Knowledge Processingin Rule-Based Expert Systems
DATA-DRIVEN REASONING (continued) At Rule 4 both
premises are askable. Q1 Is there gas in the
fuel tank? A true Q2 Is there gas in the
carburator? A true
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Knowledge Processingin Rule-Based Expert Systems
DATA-DRIVEN REASONING (continued) Rule 4 fires
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Knowledge Processingin Rule-Based Expert Systems
DATA-DRIVEN REASONING (continued) Rule 1 fires
Rules 2 and 3 fails. The process terminates with
no further rules matching.
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Knowledge Processingin Rule-Based Expert Systems

• DATA-DRIVEN REASONING (continued)
• Breadth-First search strategy is used in the
  previous example.
• Opportunistic search strategy is whenever a rule
  fires to conclude new knowledge, control moves to
  consider those rules which have that new
  knowledge as a premise.
• In data-driven reasoning goal orientation does
  not exist. As a result, the progress of search
  often is diffuse and unfocused.

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Knowledge Processingin Rule-Based Expert Systems

• DATA-DRIVEN REASONING (continued)
• Consequently, the explanation available is quite
  limited.
• When user asks why some information is required,
  the current rule under consideration can be
  presented.
• When a goal is achieved it is difficult to get
  full how explanation, because contents of the
  working memory or a list of rules fired can be
  presented, but these will not offer the
  consistent focused accountability.

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Knowledge Processingin Rule-Based Expert Systems
GOAL-DRIVEN REASONING The top-level goal is
placed in working memory.
Three rules match with the working memory. So,
the conflict set contains Rules 1, 2, and 3.
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Knowledge Processingin Rule-Based Expert Systems
GOAL-DRIVEN REASONING (continued) Conflicts are
resolved in favor of Rule 1. X is bound to the
value spark plugs and Rule 1 fires.
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Knowledge Processingin Rule-Based Expert Systems

• GOAL-DRIVEN REASONING (continued)
• The problem is decomposed in two subproblems
• The engine is getting gas
• The engine will turn over

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Knowledge Processingin Rule-Based Expert Systems

• GOAL-DRIVEN REASONING (continued)
• There are three entries in working memory that do
  not match with any rule conclusions.
• The expert system will query the user directly
  about these subgoals
• Q1 Will the engine turn over? A true
• Q2 Is gas in the carburator? A true
• Q3 Is gas in the fuel tank? A true
• The expert system determines that the car will
  not start because the spark plugs are bad.

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Knowledge Processingin Rule-Based Expert Systems

• GOAL-DRIVEN REASONING (continued)
• Depth-First search is used in the previous
  example.
• In goal-driven reasoning goal orientation is
  maintained.
• As a result, reasoning is in pursuit of a
  particular goal.
• That goal is decomposed into subgoals that
  support the top-level goal and these subgoals may
  be even further broken down. The search is always
  directed through this goal and subgoal hierarchy.

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Knowledge Processingin Rule-Based Expert Systems

• GOAL-DRIVEN REASONING (continued)
• Consequently, the production system uses a trace
  of the search to answer user queries why and how.
• When user asks why some knowledge is required,
  the expert system responds with a restatement of
  the current rule that the production system is
  attempting to fire.
• When user asks how the expert system get the
  result, the response is a trace of the reasoning
  that led to this conclusion working back from a
  goal along the rules that support it to the user
  responses.