ON BASIC LOGIC : INDUCTIVE AND DEDUCTIVE REASONING
FUNDAMENTAL LOGIC FOR ARTIFICIAL INTELLIGENCE
I had plenty of experience with logic in the work place through the
implementation and practice of use cases, in particular, writing database
triggers. But academically, they go back as far as my high-school coursework in
philosophy, with my study of inductive and deductive reasoning and syllogism,
which were based on a philosophical compendium of basic logic. During my law school years at Corporación
Universidad de la Costa, in Barranquilla, I had learned logic during a year
throughout my Theory of Knowledge and Logic coursework, and encounter various
differences in my approach to my systems engineering's Set Theory and Logic
class at Universidad del Norte, for which I had a strong academic background in
problem solving and through the diagraming of solutions utilizing Venn
diagrams, now widely used in AI problem solving. Then yet other computer
science classes such as Commutation (Switching Theory), Boolean algebra, Design
of Digital Systems, and Computer Architecture, among others brought a different
flavor of logic. In my AI classes, the topic of logic was essentially far below
the problem of searching, which is considered the most relevant to AI problem
solving. However, during my teaching I
have attempted to have students further understand logic, prior to doing any
searching problem solving, and further enhancing my work with various flavors
of logic including Predicate, Propositional, and Herbrand logic lectures. In each session I taught, I have encountered
a great positive from most of my students, who have excelled in this topic with
high grades at this level. However, they
have encountered difficulties differentiating problem solving and selectively
building tableaux using relational calculus to resolve propositional,
relational and Herbrand logic problems in comparison to straightforward Boolean
algebra drills. The following slides are the summary presented on my second AI
lecture.
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