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[Note: this is another in my series on computational complexity for dummies.]

The field of Artificial Intelligence (AI) got its start in 1950 with the famous Turing test. It was thought at that time that, by the year 2000, computers would possessĀ  human-level intelligence. Well, this didn’t come to pass and human-level intelligence still seems as far off now as it did then.

One metric back then was the game of chess. It was thought that for a machine to beat the best human at chess would require human-type thinking. This metric was met in 1997 when Deep Blue beat Gary Kasparov. However, as the designers of Deep Blue freely admit, Deep Blue won mostly by brute force enumerating all the solutions. it was clear, and had been for a while, that the chess problem was going to be solved by exploiting computers vastly faster speed compared to humans rather than by any form of human-type thinking.

Chess is a particular type of game in which there are two players, with one player moving first, the second making a counter-move, and the players alternating until one player wins or a draw is achieved. The goal is to find a winning strategy. We can formulate this as follows (for player 2 in this case):

for all first moves player 1 makes,
there exists a first move that player 2 makes such that,
for all second moves player 1 makes,
there exists a second move that player 2 makes such that,
player 2 wins.

The computationally complexity of problems with this structure (alternating “for all” and “there exists” quantifiers) is in the class PSPACE-complete. Recall that the NP-hard class is the set of problems that tend to be hard. The NP-complete class is a subset of the NP-hard class and these tend to be the easiest problems to solve in the NP-hard class. PSPACE-complete problems are the hardest subset of the NP-hard class. In general, there is no easier way to solve them than by enumerating all the solutions until a satisfying one is found. This is why research into chess programs focusses on making search speed as fast as possible and why it takes specialized hardware to beat a human.

But, what has this got to do with verification? At the most abstract level, a verification system consists of two parts: the device-under-test (DUT) and the environment it sits in. The DUT doesn’t do anything unless the environment initiates something by injecting a request. The DUT responds to this in some way. This is analogous to a two player game in which the DUT is one player and the environment is the other. Requests and responses correspond to moves and counter-moves. The question then is: what is analogous to a game in verification?

There are two fundamental categories of properties that all verfication checks fall into: safety properties and liveness properties. A safety property is one that says “nothing bad will happen” and a liveness property says “something good will always happen.” An example of a safety property is that a traffic light should never be green in both directions simultaneously. An example of a liveness property is that, for a given direction, the light should always eventually turn green.

The vast majority of properties that we verify on a day-to-day basis are safety properties. For example, the property, 2+2=4, in an adder is a safety property. It is not possible to prove any type of property using simulation alone. It is possible to demonstrate the existence of safety property violations, but not liveness property violations using simulation alone. For example, suppose we simulated the traffic light controller for a million time steps and the light was always red in one direction. Does this constitute a violation of the liveness property? It is not conclusively a violation because it is possible the light could turn green if we simulated one more time step. At the same time, even if we saw the light turning green, this does not mean that it won’t get stuck red at some point.

It is possible to prove liveness properties using formal verification. How would we formulate the liveness property of the traffic light? One way is to consider it as a game between the cars arriving at the light (the environment) and the traffic light state. Then we could formulate the problem:

for all possible combinations of cars arriving at step 1,
there exists a state of the traffic light at step 1 such that
for all possible combinations of cars arriving at step 2,
there exists a state of the traffic light at step 2 such that
if the traffic light state is red, 
                     then in the next state it will be green.

This has the same structure as a game and so is PSPACE-complete. Since PSPACE-complete problems are solveable to a certain degree, we can prove liveness properties, but generally only on very small designs. The most effective use of this is to verify highly abstracted designs, typically when doing protocol verification.

This deep connection between the formal verification field and AI field goes further than having similar complexity. The core tool used in formal verification today, SAT solving, emerged from research into AI problems such as planning. The cross fertilization between these two fields has yielded dramatic improvements in efficiency in SAT solving over the last 15 years.