Jonathan Waskan

The potential of artificial intelligence has always been tempered by the limits of computation, a notion that dates back at least to the 1930s work of Alan Turing and his famous thought experiment that gave us the abstract Turing machines.

Jonathan Waskan is a professor of philosophy - and the Beckman Institute's lone philosopher - but his perspective on the computational issues surrounding artificial intelligence (AI) is something computer scientists, cognitive scientists, and AI researchers may want to consider.

Waskan is an Assistant Professor of Philosophy at the University of Illinois and a member of the Cognitive Science group at the Beckman Institute. Some may not know, or think it unusual, that Beckman has a philosopher as one of its faculty members. But Waskan's Ph.D. from a unique program at Washington University in St. Louis that combines philosophy, neuroscience, and psychology attests to his interdisciplinary credentials. At Illinois his work focuses on the philosophy of cognitive science, including issues that apply to artificial intelligence.

In his writings, especially in his 2006 book Models and Cognition (MIT Press), Waskan delves into the intersection of philosophy, neuroscience, and artificial intelligence. He says that the logic metaphor in philosophy is a proposal stating that "the way we reason is very similar to what we do when we construct formal logic proofs" i.e., deducing conclusions from propositions, and that the metaphor also applies to the workings of computers.

"One of the nice things about computers is that they are able to implement that kind of process, to mechanize that sort of formal reasoning," Waskan said.

Creating a truly effective artificial intelligence system supported by logic-based computing, however, runs into problems that Waskan says are probably "insurmountable." He offers a different approach - one that looks to the kind of computer models used by hurricane predictors and systems designers.

Turing, working in 1936 before the advent of the modern computer, explored the limits of computation through abstract computational devices known as Turing machines. The Turing machines could be used to simulate the logic of computers; these mathematical abstractions later provided clues for computer scientists who, equipped with newer algorithms and everincreasing processing power, started to contemplate the possibility of creating artificial intelligence systems.

"In the early days of artificial intelligence, a lot of work was being done trying to figure out how we could use this fact about computers to model human cognition," Waskan said. "People were trying to get systems to engage in simple, practical reasoning in real-world environments. Like if a system wants a glass of water you want it to be able to figure out, for example, a situation where it can get a glass of water in its claw, or whatever. You have to give the system a bunch of rules and each of these rules has to have a huge number of qualifications."

As Waskan wrote in Models and Cognition, the huge number of qualifications needed in order to compute all the variables involved in a potential task make the qualification problem an overwhelming one for logic-based AI systems: "... in order to embody what we know about the consequences of alterations to the world, not only would an infinite number of rules be required, but each rule would also have to be qualified in a seemingly infinite number of ways."

The qualification problem is but one part of an overall difficulty in getting a logicbased system to respond to all the possible alterations to a situation.

"It's impossible to give a rule-based system, a logic-based system all of the knowledge that we have about alterations," Waskan said.

This is what is known as the "frame problem" which has been defined as "the challenge of getting a representational system to predict what will change and what will stay the same following alterations to the state of the world" (Bechtel, Abrahamsen & Graham 1998).