ECS 170: Introduction to Artificial Intelligence
Prof. Rao Vemuri

Historical Introduction

        Newell and Simon's Physical Symbol System Hypothesis

            "A physical symbol system has the necessary and sufficient means for intelligent action."

           A physical symbol system "consists of a set of entities, called symbols, which are physical patterns that can occur as components of another type of entity called an expression (or symbol structure). Thus, a symbol structure is composed of a number of instances (or tokens) of symbols related in some physical way (such as one token being next to another). For instance, "student" is a symbol indicating a concrete thing (an object, a person). "Thought" is a symbol indicating an abstract thing. "take" is a symbol denoting an activity.

At any instant of time a system will contain a collection of these symbol structures. Besides these structures, the system also contains a collection of processes that operate on expressions to produce other expressions: processes of creation, modification, reproduction and destruction. A physical symbol system is a machine that produces through time an evolving collection of symbol structures. Such a system exists in a world of objects wider than just these symbolic expressions themselves."

"Two notions are central to this structure of expressions, symbols, and objects: designation and interpretation."

"Designation. An expression designates an object if, given the expression, the system can either affect the object itself or behave in ways dependent on the object. ... In either case, access to the object via the expression has been obtained, which is the essence of designation."

"Interpretation. The system can interpret an expression if the expression designates a process and if, given the expression, the system can carry out the process. ...Interpretation implies a special form of dependent action: given an expression the system can perform the indicated process, which is to say, it can evoke and execute its own processes from expressions that designate them."

Strong AI and Weak AI

The current general position is that in order for a system to be intelligent it must be capable of  learning or "programming itself" and at the same time it probably must, in many cases, approach  its world  with just the right biases about how to learn or "program itself".

Weak Methods

AI attempted to respond to this problem of how to achieve intelligence without "programming" by "augmenting" the basic computational model defined by a Turing Machine. They did this by introducing the idea of a Weak Method. What is desired is to identify a perfectly general "program" or "method" that is as dumb as imaginable, but can provide the basis for "becoming more intelligent" through some learning mechanism.

The basic idea is referred to as generate and test. What this involves is to generate candidate solutions to some problem and then being able to test whether a candidate solution is in fact the solution to the problem. For example, say you are given the problem 2x + 2 = 10 and asked to provide the value for x. Now, assume you know absolutely nothing about algebra; that is, you have no special "program" or method for solving this type of problem. What could you do?

Well, let's assume that you do know how to evaluate an arithmetic expression. Consequently, you can compute the value of an expression like 2(100) + 2 = 202. Given this ability to evaluate arithmetical expressions, you can then use that ability to specify a test. That is, you can see if 202 equals 10. Since it does not, you can conclude that 100 is not the solution to this problem. Now, you also need the ability to generate these candidate solutions.

In this case this ability to generate candidate solutions can be achieved by simply being able to generate a number,...any number. So you need some sort of rule that generates a number, then you test that number to see if it is a solution. If the test succeeds, then stop and write out the solution. If not, simply generate another  number, and so on. This is what is known as blind search. It is blind in the sense that there is not only no knowledge that is being used to direct the search, but also in the sense that the search is unsystematic. It is about  the dumbest thing that we can imagine. The idea goes back to what is termed the British Museum Algorithm which gets its name from the notion that if you put a chimp or whatever in front of a typewriter, then eventually this chimp will generate all the books in the British Museum!