1. 1 Automata Theory, Languages and Computation Fall 2014 Instructor: John Miller, West 134E jhmiller@tricity.wsu.edu Class web page can be found at http://www/tricity.wsu.edu/~jhmiller Welcome to CptS 317

2. 2 Background uIn 1930s A.Turing studied abstract machines (Turing machine) with properties like modern computers. uHis objective was to discover what computers could and could not do. uThis subject now called “deciability” uIf problem can be solved by computer, it is “decidable”

3. 3 More Background uIn 40s and 50s, simple machines called “finite automata” were studied as models of brain function. uAlthough not good brain models, they turned out to be useful for other reasons. uIn 1950s, N. Chomsky introduced the concept of “grammars” that is closely related to finite automata.

4. 4 More Background uIn 1969 S. Cook extended Turing work. uHe devised ways to separate computer problems into those that could be solved efficiently (tractable) from those that took so much time that computers are useless (intractable or NP-hard). uCptS 317 is about these classical issues in the theory of computing.

5. 5 Why Study Automata? uFinite automata are models for protocols, electronic circuits, etc. uRegular expressions are essential for all types of computing uContext-free grammars are used to describe the syntax of almost every programming language.

6. 6 More reasons uReal problems sometimes confront the limitations of what software can do. wUndecidable things no program can do wIntractable things programs can do but no fast programs exist.

7. 7 Practical Application u“Intractable” problems should not be addressed “head on” (i.e. write code based on rigorous step-by-step method) uLook for an approximate method uTry “heuristic” approach (likely to give the correct answer by no guarantee)