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Summary
The purpose of this paper is to examine the development of basic understanding of computer science by empirical inquiry.All sciences characterize the nature of their systems through qualitative statements. While such statements or laws are generic in nature, they are of great importance and often set the terms on which a whole science operates.
One of the fundamental contributions of computer science has been to explain what symbols are. Symbols lie at the root of intelligent action, and are a primary topic for computer science.
A physical symbol system consists of a set of symbols, which may be related to each other to form expressions or symbol structures. In addition, a physical symbol system also contains a collection of processes that operate on these expressions to produce other expressions. The notions of designation and interpretation are central to physical symbol systems, along with the requirements of completeness and closure.
The Physical Symbol System Hypothesis: A physical symbol system has the necessary and sufficient means for general intelligent action.
A physical symbol system is an instance of a universal machine. Thus the hypothesis implies that intelligence will be realized by a universal computer. It also asserts that the intelligent machine is a symbol system. The development of symbol systems can be traced through time by examination of formal logic (formal symbol manipulation), Turing machines (automatic formal symbol manipulation), stored programs (interpretations), list processing (designation and dynamic symbol structures).
The authors provide the evidence for the symbol system hypothesis by modeling human symbolic behavior. The symbol system implies that the intelligent behavior of man arises because he has characteristics of a physical symbol system. The search for explanations of man's intelligent behavior in terms of symbols systems has had success over the years. In areas like problem solving, concept attainment and long-term memory, symbol manipulation models are common. While this supports the hypothesis, another evidence is the absence of competing hypotheses in the field of psychology.
The question of how symbol systems provide intelligence behavior leads to the concept of heuristic search.
The Heuristic Search Hypothesis: The solutions to problems are represented as symbol structures. A physical symbol system exercises its intelligence in problem solving by search - that is, by generating and progressively modifying symbol structures until it produces a solution structure.
Since the ability to solve problems is generally taken as an indicator of intelligence, it has been a primary problem for artificial intelligence. During early AI research, study of problem solving was almost synonymous with study of search. When a symbol system that is trying to solve a problem knows enough about what to do, it simply proceeds directly towards its goal; but whenever its knowledge is inadequate, it is faced with large amounts of search to find its way again. The task of intelligence is to avert the threat of exponential explosion of search by extracting and using information about the structure of the problem space.
There are other ways of achieving more information about the problem space: 1) Non local use of information, 2) Semantic Recognition systems and 3) selecting appropriate representation.
Definitions
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.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.
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