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Introduction: logic motivation

Module by: Ian Barland, John Greiner, Phokion Kolaitis, Moshe Vardi, Matthias Felleisen. E-mail the authors

Summary: Logic provides a way to talk about truth and correctness in a rigourous way, so that we can prove things, rather than make intelligent guesses and just hope they are correct.

The ancient Greeks loved to hang around on the stoa, sip some wine, and debate. But at the end of the day, they wanted to sit back and decide who had won the argument. When Socrates claims that one statement follows from another, is it actually so? Shouldn't there be some set of rules to officially determine when an argument is correct? Thus began the formal study of logic.

Aside:

The three fundamental studies were the Trivium — grammar (words), logic (reasoning), and rhetoric (effective communication). These allowed study of the Quadrivium — arithmetic (patterns in number), geometry (patterns in space), music (patterns in tone), and astronomy (patterns in time). All together, these subjects comprise the seven liberal arts.

These issues are of course still with us today. And while it might be difficult to codify real-world arguments about (say) gun-control laws, programs can be fully formalized, and correctness can be specified. We'll look at three examples where formal proofs are applicable:

  • playing a simple game, WaterWorld;
  • checking a program for type errors;
  • circuit verification.
Many other areas of computer science routinely involve proofs, although we won't explore them here. Manufacturing robots first prove that they can twist and move to where they need to go before doing so, in order to avoid crashing into what they're building. When programming a collection of client and server computers, we usually want to prove that the manner in which they communicate guarantees that no clients are always ignored. Optimizing compilers prove that, within your program, some faster piece of code behaves the same as and can replace what you wrote. With software systems controlling more and more life-critical applications, it's important to be able to prove that a program always does what it claims.

WaterWorld

Consider a game called WaterWorld, where each location is either empty sea or contains a pirate. When you enter a location, you must correctly anticipate whether or not it contains pirates.

  • If you correctly anticipate open sea, you are able to enter and determine how many of the (up to 3) adjacent locations contain a pirate.
  • If you correctly anticipate a pirate, the location is tagged as dangerous, and you gather no further information.
Furthermore, there are really two types of moves: guesses, and assertions. If you make an assertion, then even if you happen to be correct but it is possible you could have been wrong, then it is an error. Also, it is an error if you make a guess about a location if it is actually possible to assert a location's contents. The interesting fact about these types of games is that while sometimes guesses are necessary (when?), surprisingly often an assertion can be made.

(You can freely download WaterWorld.)

Figure 1: Glimpses of two different WaterWorld boards
(a) (b)
Figure 1(a) (ww-board-snippet1-partI.png)Figure 1(b) (ww-board-snippet2-partI.png)

For instance, in the first board, what assertions can we be sure of? What, exactly, is your reasoning? How about in the second board? You can certainly envision wanting a computer player that can deduce certain moves, an make those for you automatically.

Type Checking

When writing a program, we'd like to simply look at the program and determine whether it has any bugs, without having to run it. We'll see in the future, however, that such a general problem cannot be solved. Instead, we focus on finding more limited kinds of errors. Type checking determines whether all functions are called with the correct type of inputs. E.g., the function + should be called with numbers, not Booleans, and a function which a programmer has declared to return an integer really should always return an integer. Consider the following program:


// average: 
// Simply divide sum by N, but guard against dividing by 0.
//
real-or-false average( real sum, natNum N ) {
  if (N != 0)
    return sum / N;
  else
    return false;
}

One reason programmers are required to declare the intended type of each variable is so that the computer (the compiler) can prove that certain errors won't occur. How can you or the compiler prove, in the above, that average returns a real number or false, but never returns (say) a string, and doesn't raise an exception? Deductions are made based on premises about the types that are passed in, along with axioms about the input and return types of the built-in functions if, !=, and /, as well as which exceptions those built-ins might raise.

Consider this variant:


// augment-average:
// Given an old sum and N, compute the average if one more
// datum were included.
// 
real augment_average( real old_sum, natNum old_N, real new_datum ) {
  return average( old_sum + new_datum, old_N + 1 );
}

Most compilers will reject augment-average, claiming that it may actually return false. However, we're able prove that it really will only return a real, by using some knowledge about natural numbers and adding 1, plus some knowledge of what average returns. (Note that our reasoning uses aspects of average's interface which aren't explicitly stated; most type systems aren't expressive enough to allow more detailed type contracts, for reasons we'll allude to later.) So we see that many compilers have overly conservative type-checkers, rejecting code which is perfectly safe, because they are reasoning with only a narrow set of type-rules.

This example alludes to another use of logic: Not only is it the foundation of writing proofs (ones that can be created or checked by computers), but logic can also be used as an unambiguous specification language. Observe that while a function's implementation is always specified formally and unambiguously — in a programming language — the interface is specified entirely English, aside from a few type declarations. Many bugs stem from ambiguities in the English, that different humans interpret differently (or, don't think about). Being able to use logic to specify an interface (and cannot be modified even if the somebody later tunes the implementation) is an important skill for programmers, even when those logic formulas aren't used in proofs.

Circuit Verification

Given a circuit's blueprints, will it work as advertised? In 1994, Intel had to recall five million of its Pentium processors, due to a bug in the arithmetic circuitry: This cost Intel nearly half a billion dollars, lots of bad publicity, and it happened after intensive testing. Might it have been possible to have a program try to prove the chip's correctness or uncover an error, before casting it in silicon?

Software and hardware companies are increasingly turning to the use of automated proofs, rather than semi-haphazard testing, to verify (parts of) large products correct. However, it is a formidable task, and how to do this is also an active area of research.

There are of course many more examples; one topical popular concern is verifying certain security properties of electronic voting machines (often provided by vendors who keep their source software a proprietary secret).

Having proofs of correctness is not just comforting; it allows us to save effort (less time testing, and also able to make better optimizations), and prevent recall of faulty products. But: who decides a proof is correct — the employee with best SAT scores?!? Is there some trusted way to verify proofs, besides careful inspection by a skilled, yet still error-prone, professional?

Many highly intelligent people are poor thinkers. Many people of average intelligence are skilled thinkers. The power of the car is separate from the way the car is driven. Edward De Bono, consultant, writer, and speaker (1933- )

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