Physical symbol system

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See also: Philosophy of artificial intelligence

A physical symbol system (also called a formal system) takes physical patterns (symbols), combining them into structures (expressions) and manipulating them (using processes) to produce new expressions. Examples of physical symbol systems include:

  • Formal logic: the symbols are words like "and", "or", "not", "for all x" and so on. The expressions are statements in formal logic which can be true or false. The processes are the rules of logical deduction.
  • Algebra: the symbols are "+", "×", "x", "y", "1", "2", "3", etc. The expressions are equations. The processes are the rules of algebra, that allow you to manipulate a mathematical expression and retain its truth.
  • A digital computer: the symbols are zeros and ones of computer memory, the processes are the operations of the CPU that change memory.
  • Chess: the symbols are the pieces, the processes are the legal chess moves, the expressions are the positions of all the pieces on the board.

The physical symbol system hypothesis is a position in the philosophy of artificial intelligence formulated by Allen Newell and Herbert Simon. They wrote:

"A physical symbol system has the necessary and sufficient means for general intelligent action."[1]

– Alan Newell and Herbert Simon

This claim is very strong: it implies both that human thinking is a kind of symbol manipulation (because a symbol system is necessary for intelligence) and that machines can be intelligent (because a symbol system is sufficient for intelligence). It claims that both of these are also examples of physical symbol systems:

  • Intelligent human thought: the symbols are encoded in our brains. The expressions are thoughts. The processes are the mental operations of thinking.
  • A running artificial intelligence program: The symbols are data. The expressions are more data. The processes are programs that manipulate the data.

The idea has philosophical roots in Hobbes (who claimed reasoning was "nothing more than reckoning"), Leibniz (who attempted to create a logical calculus of all human ideas), Hume (who thought perception could be reduced to "atomic impressions") and even Kant (who analyzed all experience as controlled by formal rules).[2] The latest version is called the computational theory of mind, and is associated with philosophers Hilary Putnam and Jerry Fodor.[3]

The hypothesis has been criticized strongly by various parties, but is a core part of AI research. A common critical view is that the hypothesis seems appropriate for higher-level intelligence such as playing chess, but less appropriate for commonplace intelligence such as vision. A distinction is usually made between the kind of high level symbols that directly correspond with objects in the world, such as <dog> and <tail> and the more complex "symbols" that are present in a machine like a neural network.

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[edit] Arguments in favor of the physical symbol system hypothesis

Two lines of evidence suggested to Alan Newell and Herbert Simon that "symbol manipulation" was the essence of both human and machine intelligence: the development of artificial intelligence programs and psychological experiments on human beings.

In the early decades of AI research there were a number of very successful programs that used high level symbol processing, such as Newell and Herbert Simon's General Problem Solver or Terry Winograd's SHRDLU.[4] John Haugeland named this kind of AI research "Good Old Fashioned AI" or GOFAI.[5] Expert systems and logic programming are descendants of this tradition.

Psychological experiments carried out at the same time found that, for difficult problems in logic, planning or any kind of "puzzle solving", people used this kind of symbol processing as well. AI researchers were able to simulate the step by step problem solving skills of people with computer programs. This collaboration and the issues it raised eventually would lead to the creation of the field of cognitive science.[6]

[edit] Criticism

[edit] Dreyfus and the primacy of unconscious skills

Main article: Dreyfus' critique of artificial intelligence

Another version of this position was described by philosopher Hubert Dreyfus, who called it "the psychological assumption":

  • The mind can be viewed as a device operating on bits of information according to formal rules.[7]

Dreyfus refuted this by showing that human intelligence and expertise depended primarily on unconscious instincts rather than conscious symbolic manipulation. Experts solve problems quickly by using their intuitions, rather than step-by-step trial and error searches. Dreyfus argued that these unconscious skills would never be captured in formal rules.[8]

[edit] Searle and his Chinese Room

Main article: chinese room

John Searle's Chinese Room argument, presented in 1980, attempted to show that a program (or any physical symbol system) could not be said to "understand" the symbols that it uses; that the symbols have no meaning for the machine, and so the machine can never be truly intelligent.[9]

[edit] Brooks and the roboticists

Main articles: nouvelle AI, situated, and Moravec's paradox

In the sixties and seventies, several laboratories attempted to build robots that used symbols to represent the world and plan actions (such as the Stanford Cart). These projects had limited success. In the middle eighties, Rodney Brooks of MIT was able to build robots that had superior ability to move and survive without the use of symbolic reasoning at all. Brooks (and others, such as Hans Moravec) discovered that our most basic skills of motion, survival, perception, balance and so on did not seem to require high level symbols at all, that in fact, the use of high level symbols was more complicated and less successful.

In a 1990 paper Elephants Don't Play Chess, robotics researcher Rodney Brooks took direct aim at the physical symbol system hypothesis, arguing that symbols are not always necessary since "the world is its own best model. It is always exactly up to date. It always has every detail there is to be known. The trick is to sense it appropriately and often enough."[10]

[edit] Connectionism

Main article: connectionism
 Please help improve this article or section by expanding it.
Further information might be found on the talk page or at requests for expansion. (November 2007)

[edit] Embodied philosophy

Main article: embodied philosophy
 Please help improve this article or section by expanding it.
Further information might be found on the talk page or at requests for expansion. (November 2007)

George Lakoff, Mark Turner and others have argued that our abstract skills in areas such as mathematics, ethics and philosophy depend on unconscious skills that derive from the body.

[edit] Notes

  1. ^ Newell & Simon 1976, p. 116 and Russell & Norvig 2003, p. 18
  2. ^ Dreyfus 1979, p. 156, Haugeland, pp. 15-44
  3. ^ Horst 2005
  4. ^ Dreyfus 1979, pp. 130-148
  5. ^ Haugeland 1985, p. 112
  6. ^ Dreyfus 1979, pp. 91-129, 170-174 This type of research was called "cognitive simulation."
  7. ^ Dreyfus 1979, p. 156
  8. ^ Dreyfus 1972, Dreyfus 1979, Dreyfus & Dreyfus 1986. See also Russell & Norvig 2003, pp. 950-952, Crevier & 1993 120-132 and Hearn 2007, pp. 50-51
  9. ^ Searle 1980, Crevier 1993, pp. 269-271
  10. ^ Brooks 1990, p. 3

[edit] References

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