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ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM]

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ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] KNOWLEDGE PROCESSING IN RULE-BASED EXPERT SYSTEMS ... Depth-First search is used in the previous example. – PowerPoint PPT presentation

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Title: ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM]


1
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

2
Knowledge Processingin Rule-Based Expert Systems
DATA-DRIVEN REASONING(FORWARD CHAINING) The start
3
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.
4
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
5
Knowledge Processingin Rule-Based Expert Systems
DATA-DRIVEN REASONING (continued) Rule 4 fires
6
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.
7
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.

8
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.

9
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.
10
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.
11
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

12
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.

13
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.

14
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.
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