All of these, in different ways, involve hierarchical representation
of data.
Lists - linked lists are used to represent hierarchical knowledge
Trees - graphs which represent hierarchical knowledge. LISP,
the main programming language of AI, was developed to process lists and
trees.
Semantic networks - nodes and links - stored as propositions.
Examples in Stillings et. al. pp. 146-147
Schemas - used to represent commonsense or stereotyped
knowledge.
Frames (Minsky) - Describe objects. Consist of a
cluster of nodes and links manipulated as a whole. Knowledge
is organised in slots. Frames are hierarchically organised.
Example on p. 151 of Stillings
Scripts (Schank and Abelson) - Describe event rather
than objects. Consist of stereotypically ordered causal or
temporal chain of events. Example on p. 156 of Stillings
Rule-based representations (Newell and Simon) - used in specific
problem-solving contexts. Involve production rules containing if-then or situation-action pairs. Specific example: problem
space representations. Contain:
Initial state
Goal state
Legal operators, i.e. things you are allowed to do
Operator restrictions, i.e. factors which constrain the
application of operators
(More on Problem-space representations and strategies in Semester 2 -
Problem solving - expert-novice studies)
Logic-based representations - may use deductive or inductive
reasoning. Contain:
Facts and premises
Rules of propositional logic (Boolean - dealing with
complete statements
Rules of predicate calculus (allows use of additional
information about objects in the proposition, use of variables and
functions of variables
Measures of certainty - may involve Certainty Factors
(eg. If symptom then (CF) diagnosis) which could be derived
from expert estimation or from statistical data; Bayesian
probability; or Fuzzy logic (in which the concepts or
information itself has some associated certainty value).
