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!
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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!
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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
Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications!
4.3
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