Making acquaintance with a theory of signs.
A definition of a model of cognitive activity.
An introduction of 'naive' logic.
An analysis of case studies in various knowledge domains.
A comparison of the properties of formal and human interpretation.
The promise of Artificial Intelligence research that the computer will become, in matters of information processing, an equal partner of man has not been realized so far. The bottleneck is the used knowledge representation that cannot deal with the adroitness of the human mind.
In this course we study a model for knowledge representation that is cognitively based, grounded in a theory of sign, and applicable to human and artificial information processing alike. By virtue of the fundamental character of cognition and interpretation, this model can be applied uniformly to knowledge modeling in different domains. Experimental evidence from neuro-physiological research shows that human information processing may work according to the same principle.
Modeling is a subject of study in many courses in Computer Science. This course generalizes modeling in a process of conceptualization. A characteristic property of this process is the use of a subset of predicate calculus, called `naive' logic, which can be shown to be present in natural language processing, reasoning, and inductive theorem proving (by humans). It can be essential for designing efficient human-computer interfaces as well.
Conceptualization as a process is assumed by traditional modeling as well, but a specification of the events of this process is usually omitted. This course provides a model of that process (and its events) and reveals the potential of this common element of human information processing, in different domains of knowledge. The process model developed suits a computational implementation also in combination with machine learning.