Artificial Intelligence: Predict Future Outcomes With Logic

Translate FACTS, DATA and BELIEFS into computer-REASONABLE CONTENT, predict outcomes from LOGICALLY LINKED assertions!
What you'll learn
Learn how to let the computer reason just like a human brain would do!
Predict future outcomes based on present assertions!
Understand Logic and Reasoning
Learn how to translate everyday sentences to a computer-compatible language
Learn the programming language Z3 and write SAT Solvers
Requirements
No programming, math and logic knowledge required
Description
Use Description Logic to translate FACTS, DATA, and BELIEFS into computer-REASONABLE CONTENT, predict events and outcomes, use programs to automatically derive conclusions from LOGICALLY LINKED ASSERTIONS, check whether a phrase is SATISFIABLE, provide a truth-table to identify whose propositions holds and build a KNOWLEDGE BASE.Artificial intelligence requires reasoning in order for the computer to think rationally and perform as well as a human brain.In this course, every lecture will analyze a specific assignment to reinforce and apply your learned pieces of information!-------------------------------------------------------------------SAT problems are present everywhere in practical applications of computer science and artificial intelligence: they range from the problem of ensuring correct behavior of circuits, programs, and protocols, to problems of data consistency, scheduling, optimization, etc. Therefore it is essential to address SAT problems with the best tools available to current technologies. Very often heuristics are used, targeted to the specificities of the problems that are faced!--------------------------------------------------------------------This tutorial is intended for two audiences. The primary audience is individuals somewhat new to SMT solvers, or at least to the particular input and output format that is SMT-LIB v.2. This tutorial will provide these readers: • a very brief introduction to some of the key concepts of logic and automated theorem proving that are needed to use SMT solvers• examples and description of how SMT-LIB is used to interact with SMT solvers• and descriptions of some tools and test suites that may be useful to you
Overview
Section 1: Introduction
Lecture 1 Greetings
Section 2: Introduction to Reasosing
Lecture 2 Reasoning
Lecture 3 Deductive Reasoning
Section 3: Description Logics
Lecture 4 Sentences
Lecture 5 Connectives
Lecture 6 Truth Functions
Lecture 7 Truth Table
Section 4: Transformation Algorithms
Lecture 8 Negation Normal Form
Section 5: Sat Solvers
Lecture 9 SAT Solvers
Lecture 10 Z3 Online Compiler
Lecture 11 Write with Z3 Programming Language
Lecture 12 Use Z3 to Solve Math Problems
Lecture 13 Define Complex and Self-Operating Varibales
Lecture 14 Assertion Excercise
Lecture 15 Distinct Variables Problem
Lecture 16 Syllogism problem
Section 6: Excercises
Anyone interested in Artificial Intelligence,Anyone interested in seeing how artificial intelligence is applied in Description Logic
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