Bonus
Hopcroft’s algorithm can be used to minimize one type of these constructs. For 10 points each:
[10h] Name these models of computation which are equivalent to regular languages by Kleene’s theorem. The subset construction algorithm converts the “nondeterministic” type of these constructs into a “deterministic” type.
ANSWER: finite automata [or finite automaton or FAs; or finite state machines or FSMs; or finite state automata or FSAs; accept nondeterministic finite automata or NFAs; accept deterministic finite automata or DFAs; accept nondeterministic finite state machines or NFSMs; accept deterministic finite state machines or DFSMs; accept nondeterministic finite state automata or NFSAs; accept deterministic finite state automata or DFSAs; prompt on automata or automaton or state machines or state automata]
[10m] Push-down automata consist of a finite automaton and one of these data structures, allowing them to accept context-free languages. Operations on these last-in, first-out data structures include push and pop.
ANSWER: stacks
[10e] Recursively-enumerable languages can be accepted by machines named after this computer scientist, who theorized a test in which a machine imitates human behavior.
ANSWER: Alan Turing [or Alan Mathison Turing; accept Turing machines; accept Turing test]
<Editors, Other Science>
| Editions | Heard | PPB | Easy % | Medium % | Hard % |
|---|---|---|---|---|---|
| 2 | 9 | 16.67 | 100% | 56% | 11% |
Conversion
| Team | Opponent | Part 1 | Part 2 | Part 3 | Total | Parts |
|---|---|---|---|---|---|---|
| Boston College A | Vermont B | 0 | 0 | 10 | 10 | E |
| Brown A | Dartmouth B | 0 | 10 | 10 | 20 | ME |
| Brown B | Amherst A | 0 | 0 | 10 | 10 | E |
| Maryland A | George Mason A | 0 | 10 | 10 | 20 | ME |
| McMaster A | Toronto B | 0 | 0 | 10 | 10 | E |
| Rutgers A | Maryland C | 0 | 10 | 10 | 20 | ME |
| UC Santa Barbara | Claremont A | 10 | 10 | 10 | 30 | HME |
| Vanderbilt A | Chipola College | 0 | 10 | 10 | 20 | ME |
| Williams B | Dartmouth A | 0 | 0 | 10 | 10 | E |
Summary
| Tournament | Edition | Exact Match? | Heard | PPB | Easy % | Medium % | Hard % |
|---|---|---|---|---|---|---|---|
| Southern California | US | Y | 1 | 30.00 | 100% | 100% | 100% |
| Eastern Canada (2) | US | Y | 1 | 10.00 | 100% | 0% | 0% |
| Upper Mid-Atlantic | US | Y | 2 | 20.00 | 100% | 100% | 0% |
| Northeast | US | Y | 4 | 12.50 | 100% | 25% | 0% |
| Southeast | US | Y | 1 | 20.00 | 100% | 100% | 0% |