Evolving Solutions that are Competitive with Humans

David B. Fogel

Natural Selection, Inc.

3333 N. Torrey Pines Ct., Suite 200

La Jolla, CA 92037

dfogel@natural-selection.com

Extended Abstract

Ever since the advent of the modern digital computer, we've tried to generate machines that are

as intelligent as humans. Indeed, this was the primary goal of early artificial intelligence (AI)

researchers. The "general problem solver" served as one example of efforts along this line, and

it's lack of success was disappointing to many in the AI community. One of the primary

deficiencies of early, and even modern, efforts in AI has been a lack of a definition of the

intelligence that is sought. There is no accepted definition of intelligence, let alone "artificial

intelligence." In place of careful definitions, the Turing Test evolved as a surrogate criterion for

judging intelligence.

The famous Turing Test involves an interrogator asking questions of a woman and man via a

teletype machine, with the goal of correctly identifying the woman. The woman presumably

gives truthful answers to questions, while the man may lie. Turing proposed replacing the man

with a computer and suggested that if a computer can fool the interrogator into believing that it is

the woman as often as a man can fool the interrogator then the machine will be said to have

passed the test. Interestingly, Turing never claimed that passing the test meant that such a

machine would be "intelligent," an issue that he described as being "too meaningless to deserve

discussion."

In retrospect, the Turing Test is no more a test for intelligence than it is a test for femininity. If a

man can fool an interrogator into believing that he is the woman, that does not make the man a

woman. Similarly, just because a computer might fool an interrogator into believing that it is

intelligent does not make the computer intelligent. Nevertheless, the Turing Test has had a

profound impact on efforts to simulate behaviors that we associate with intelligence, mainly as

we observe them in ourselves. Unfortunately, over time, the impact was mainly to narrow the

focus of AI to simply generate programs that could compete with humans in specialized areas,

such as chess. The mechanism for generating the required behavior became irrelevant, as all that

was of importance was the end result. The culmination of this process is Deep Blue, a very fast

machine that can beat Garry Kasparov in chess, but is no more "intelligent" than a calculator, or

a hammer.
Rather than begin from the perspective of the Turing Test, an alternative perspective begins with

the concept of decision making. For an organism to be intelligent, it must make decisions. It is

pointless to speak of the intelligence of something that does not make decisions. A decision can

be defined to arise when available resources are allocated. Note that a range of possible

decisions, possible allocations, must be available otherwise there really is no decision at all.

Logically, decision making requires a goal, for decision making in the absence of a goal is

pointless. Thus we must inquire as to where goals come from.

In natural systems, the primary goal instilled in all living organisms is survival. Those organisms

that do not possess this goal may be "successful," but are uninteresting from an evolutionary

perspective. Thus behaviors can be judged in how well they support this ultimate goal, and

subgoals that underlie it. More generally then, intelligence can be defined as "the ability for a

system to adapt its behavior to meet its goals in a range of environments." This capability of

intelligent decision making can be observed strikingly in the evolving phyletic lines of organisms

(the reader is free to choose which ones), such as frogs and insects in which cryptic coloration,

poison, reliable signaling, and mimicking have all been invented to meet the primary goal of

"avoiding being someone else's lunch." No single individual invented any of these "tricks," rather

the intelligent organism in these cases is the evolving line of individuals.

Taking this cue from nature, it is reasonable to assess the intelligence capability of a machine that

evolves solutions to problems in a manner similar to that of evolving phyletic lines in the natural

environment. From the perspective of performance comparison, many efforts in evolutionary

computation have been measured in light of human capabilities. For example, L. Fogel (Artificial

Intelligence Through Simulated Evolution, Wiley, 1966) compared the ability of graduate

students and an evolutionary program operating on finite state machines to predict sequences of

symbols. The results showed that the evolutionary program was competitive or slightly more

capable than its human competitors. In Germany, in the mid-1960s, H.-P. Schwefel used an

evolutionary algorithm to create a new design for a flashing nozzle, which exceeded the

capabilities of the previous human design. There are many other relevant results in the literature.

More recently, the author and a colleague (Kumar Chellapilla) investigated the ability for an

evolutionary algorithm to learn to play checkers at a level that is commensurate with human

experts, without relying on human expertise about checkers. Instead, neural networks were

used to evaluate candidate board positions based only on the inputs found in the number,

location, and types of pieces on the board. Furthermore, the neural networks were not told

which games were won, lost, or drawn. Only an overall point value was assessed to each neural

network, which signified the total value earned over a series of games.

Starting from randomly weighted connections, a population of neural networks evolved, using

random variation of the weights of each neural network and a selective mechanism to eliminate

poor-scoring networks, over 100 generations to be competitive with "Class B" human players

on the Internet. With some modifications of the input design to the neural networks, which
allowed a recognition that the game is played on a two-dimensional board, and 840 generations,

the best-evolved neural network (called Blondie24) was able to compete with human experts

and finished in the top 500 of over 120,000 people on the Internet site, www.zone.com. More

details on this effort can be found in D. Fogel's book, Blondie24: Playing at the Edge of AI,

Morgan Kaufmann, 2002.

The results indicate that evolution is a suitable mechanism for creating intelligent behavior in

machines, and that it can learn to generate behavior that is competitive with human experts even

without relying on human expertise. As computer hardware increases in speed according to

Moore's Law, it is important to recall that this acceleration in speed is not sufficient to generate

intelligent machines. The software that this hardware will execute is critically important. The

results described here and presented in the plenary lecture indicate one step toward creating

intelligent machines that may someday possess an ability to adapt their behavior, to meet their

goals, in a range of environments that is commensurate with our own abilities.