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ARTIFICIAL INTELLIGENCE
[INTELLIGENT AGENTS PARADIGM]
LAWS OF QUALITATIVE STRUCTURES,
SYMBOL SYSTEM HYPOTHESIS AND
KNOWLEDGE REPRESENTATION IN LOGIC
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: [email protected]
Laws of Qualitative Structures
• The study of logic and computers has
revealed that intelligence resides in physical
symbol systems. This is computer science’s
most basic law of qualitative structures.
• Symbol systems are collections of patterns
and processes, the latter being capable of
producing, destroying and modifying the
former.
Laws of Qualitative Structures
• The most important property of patterns is
that they can designate objects, processes
or other patterns, and that when they
designate processes they can be
interpreted.
• A second law of qualitative structure for
artificial intelligence is that symbol systems
solve problems by generating potential
solutions and testing them, that is, by
searching.
Laws of Qualitative Structures
• Solutions are usually sought by
creating symbolic expressions and
modifying them sequentially until they
satisfy the conditions for a solution.
Symbol System Hypothesis
•
Following Newell and Simon, intelligent
activities, in either human or machine, is
achieved through the use of:
1. Symbol patterns to represent
significant aspects of a problem domain.
2. Operations on these patterns to
generate potential solutions to
problems.
3. Search to select a solution from among
these possibilities.
Symbol System Hypothesis
• Thus, if intelligence derives only
from the structure of a symbol
system, then any medium that
successfully implements the correct
patterns and processes will achieve
intelligence.
Description of Knowledge-Based
System
• The knowledge level or epistemological level is
the most abstract. The system can be described by
saying what it knows. For example, one might be
said to know that the Golden Gate Bridge links
San Francisco and Marin County.
• The logical level is the level at which the
knowledge is encoded into sentences. For
example, the logical sentence may be
Links(Golden Gate Bridge, San Francisco, Marin
County)
Description of Knowledge-Based
System
• The implementation level is the level that runs on the
system arhictecture. It is the level at which there are
physical representations of the sentences at the logical
level. For example, a logical sentence could be represented
in the knowledge base by the string contained in a list of
strings; or by a “1” entry in a three dimensional table
indexed by road links and location pairs; or by a complex
set of pointers connecting machine addresses
corresponding to the individual symbols
• The choice of implementation is very important to the
efficient performance of the system, but it is irrelevant to
the logical level and the knowledge level
Knowledge Representation
• Provided the syntax and semantics are
defined precisely the language is called a
logic.
• From the syntax and semantics an inference
mechanism can be derived for an
intelligent system that uses the language.
Knowledge Representation
Follows
Facts
Semantics
Facts
Semantics
World
Representation Sentences
Sentences
Entails
Knowledge Representation
• Sentences are physical configurations,
thus reasoning must be a process of
constructing new physical configurations
from old ones.
• Proper reasoning should ensure that the new
configurations represent facts that actually
follow from the facts that the old
configurations represent.
Knowledge Representation
• The connection between sentences and facts
is provided by the semantics of the
language.
• The property of one fact following from
some other facts is mirrored by the property
of one sentence being entailed by some
other sentence.
• Logical inference generates new sentences
that are entailed by existing sentences.
Knowledge Representation
The semantics of the language determine the fact
to which a given sentence refers. Facts are part of
the world. It is important to distinguish between
facts and their representations. Representations
must be encoded in some way that can be
physically stored within an intelligent system
It is impossible to put the world inside a computer
(nor it is possible to put it inside a human), so all
reasoning mechanisms must operate on
representations of facts, rather than on facts
themselves.