Model theory and computer science: an appetizer
Handbook of logic in computer science (vol. 1)
Notes on set theory
Reasoning about knowledge
Temporal verification of reactive systems: safety
Temporal verification of reactive systems: safety
On full abstraction for PCF: I, II, and III
Information and Computation
Sequentiality vs. concurrency in games and logic
Mathematical Structures in Computer Science
Locus Solum: From the rules of logic to the logic of rules
Mathematical Structures in Computer Science
Elements Of Finite Model Theory (Texts in Theoretical Computer Science. An Eatcs Series)
Elements Of Finite Model Theory (Texts in Theoretical Computer Science. An Eatcs Series)
Finite Model Theory and Its Applications (Texts in Theoretical Computer Science. An EATCS Series)
Finite Model Theory and Its Applications (Texts in Theoretical Computer Science. An EATCS Series)
Homomorphism preservation theorems
Journal of the ACM (JACM)
Pillars of computer science
From a zoo to a zoology: descriptive complexity for graph polynomials
CiE'06 Proceedings of the Second conference on Computability in Europe: logical Approaches to Computational Barriers
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In this paper I discuss what, according to my long experience, every computer scientists should know from logic. We concentrate on issues of modeling, interpretability and levels of abstraction. We discuss what the minimal toolbox of logic tools should look like for a computer scientist who is involved in designing and analyzing reliable systems. We shall conclude that many classical topics dear to logicians are less important than usually presented, and that less known ideas from logic may be more useful for the working computer scientist.