ARTIFICIAL INTELLIGENCE
[INTELLIGENT AGENTS PARADIGM]
KNOWLEDGE PROCESSING
IN 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@rtu.lv
Knowledge Processing
in Rule-Based Expert Systems
DATA-DRIVEN REASONING
(FORWARD CHAINING)
The start
Working Memory
Knowledge
Base
Rule 1
Rule 2
Rule 3
Rule 4
Knowledge Processing
in Rule-Based Expert Systems
DATA-DRIVEN REASONING (continued)
Askable premise for initialization:
Q: “The engine does not turn over?” A: false
Working Memory
The engine turns over
Knowledge
Base
Rule 1
Rule 2
Rule 3
Rule 4
Rule 2 fails; consideration moves to Rule 3,
where the first premise fails.
Knowledge Processing
in 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
Working Memory
There is gas in the carburator
There is gas in the fuel tank
The engine turns over
Knowledge
Base
Rule 1
Rule 2
Rule 3
Rule 4
Knowledge Processing
in Rule-Based Expert Systems
DATA-DRIVEN REASONING (continued)
Rule 4 fires:
Working Memory
The engine is getting gas
There is gas in the carburator
There is gas in the fuel tank
The engine turns over
Knowledge
Base
Rule 1
Rule 2
Rule 3
Rule 4
Knowledge Processing
in Rule-Based Expert Systems
DATA-DRIVEN REASONING (continued)
Rule 1 fires:
Working Memory
The problem is spark plugs
The engine is getting gas
There is gas in the carburator
There is gas in the fuel tank
The engine turns over
Knowledge
Base
Rule 1
Rule 2
Rule 3
Rule 4
Rules 2 and 3 fails. The process terminates
with no further rules matching.
Knowledge Processing
in 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.
Knowledge Processing
in 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.
Knowledge Processing
in Rule-Based Expert Systems
GOAL-DRIVEN REASONING
The top-level goal is placed in working
memory.
Working Memory
The problem is X
Knowledge
Base
Rule 1
Rule 2
Rule 3
Rule 4
Three rules match with the working memory.
So, the conflict set contains Rules 1, 2, and 3.
Knowledge Processing
in 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.
Working Memory
The engine is getting gas
The engine will turn over
The problem is spark plugs
Knowledge
Base
Rule 1
Rule 2
Rule 3
Rule 4
Knowledge Processing
in 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
Working Memory
Gas is in the fuel tank
Gas is in the carburator
The engine is getting gas
The engine will turn over
The problem is spark plugs
Knowledge
Base
Rule 1
Rule 2
Rule 3
Rule 4
Knowledge Processing
in 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.
Knowledge Processing
in 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.
Knowledge Processing
in 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.