Physical symbol system
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.
The physical symbol system hypothesis (PSSH) is a position in the philosophy of artificial intelligence formulated by Allen Newell and Herbert A. Simon. They wrote:
"A physical symbol system has the
necessary and sufficient means for general intelligent action."[1]— Allen Newell and Herbert A. Simon
This claim 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
The idea has philosophical roots in
Examples
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 one to manipulate a mathematical expression and retain its truth.
- 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.
- A computer running a program: the symbols and expressions are data structures, the process is the program that changes the data structures.
The physical symbol system hypothesis 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.
- English language: the symbols are words. The expressions are sentences. The processes are the mental operations that enable speaking, writing or reading.
Evidence for the hypothesis
Two lines of evidence suggested to Allen Newell and Herbert A. Simon that "symbol manipulation" was the essence of both human and machine intelligence: psychological experiments on human beings and the development of artificial intelligence programs.
Psychological experiments and computer models
Newell and Simon carried out psychological experiments that showed that, for difficult problems in logic, planning or any kind of "puzzle solving", people carefully proceeded step-by-step, considering several different possible ways forward, selected the most promising one, backing up when the possibility hit a dead end. Each possible solution was visualized with symbols, such as words, numbers or diagrams. This was "symbol manipulation" -- the people were iteratively exploring a formal system looking for a matching pattern that solved the puzzle.[5][6][7] Newell and Simon were able to simulate the step by step problem solving skills of people with computer programs; they created programs that used the same algorithms as people and were able to solve the same problems.
This type of research, using both experimental psychology and computer models, was called "cognitive simulation" by Hubert Dreyfus.[8] Their work was profoundly influential: it contributed to the cognitive revolution of the 1960s, the founding of the field of cognitive science and cognitivism in psychology.
This line of research suggested that human problem solving consisted primarily of the manipulation of high-level symbols.
Artificial intelligence programs in the 1950s and 60s
In the early decades of AI research there were many very successful programs that used high-level symbol processing. These programs were very successful, demonstrating skills that many people at the time had assumed were impossible for machines, such as solving
The success of these programs suggested that symbol processing systems could simulate any intelligent action.
Clarifications
The physical symbol systems hypothesis becomes trivial, incoherent or irrelevant unless we recognize a distinction between "digitized signals" and "symbols", between
Semantic symbols vs. dynamic signals
The physical symbol system hypothesis is only interesting if we restrict the "symbols" to things that have a recognizable
The same issue applies to the unidentified numbers that appear in the matrixes of a
General intelligence vs. "narrow" intelligence
The PSSH refers to "general intelligent action" -- that is, to every activity that we would consider "intelligent". Thus it is the claim that
Artificial intelligence research has succeeded in developing many programs that are capable of intelligently solving particular problems. However, AI research has so far not been able to produce a system with artificial general intelligence -- the ability to solve a variety of novel problems, as human do. Thus, the criticism of the PSSH refers to the limits of AI in the future, and does not apply to any current research or programs.
Consciousness vs. intelligent action
The PSSH refers to "intelligent action" -- that is, the behavior of the machine -- it does not refer to the "mental states", "mind", "consciousness", or the "experiences" of the machine. "Consciousness", as far as neurology can determine, is not something that can deduced from the behavior of an agent: it is always possible that the machine is simulating the experience of consciousness, without actually experiencing it, similar to the way a perfectly written fictional character might simulate a person with consciousness.
Thus, the PSSH is not relevant to positions which refer to "mind" or "consciousness", such as
The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.[14][15]
Evidence against the hypothesis
- The "erroneous claim that the [physical symbol system hypothesis] lacks symbol grounding" which is presumed to be a requirement for general intelligent action.
- The common belief that AI requires non-symbolic processing (that which can be supplied by a connectionist architecture for instance).
- The common statement that the brain is simply not a computer and that "computation as it is currently understood, does not provide an appropriate model for intelligence".
- And last of all that it is also believed in by some that the brain is essentially mindless, most of what takes place are chemical reactions and that human intelligent behaviour is analogous to the intelligent behaviour displayed for example by ant colonies.
Evidence the brain does not always use symbols
If the human brain does not use symbolic reasoning to create intelligent behavior, then the necessary side of the hypothesis is false, and human intelligence is the counter-example.
Dreyfus
Hubert Dreyfus attacked the necessary condition of the physical symbol system hypothesis, calling it "the psychological assumption" and defining it thus:
- The mind can be viewed as a device operating on bits of information according to formal rules.[17]
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.[18]
Tversky and Kahnemann
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Embodied cognition
Evidence that symbolic AI can't efficiently generate intelligence for all problems
It is impossible to prove that symbolic AI will never produce general intelligence, but if we can not find an efficient way to solve particular problems with symbolic AI, this is evidence that the sufficient side of the PSSH is unlikely to be true.
Intractability
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Common sense knowledge, frame, qualification and ramification problems
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Moravec's paradox
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Evidence that sub-symbolic or neurosymbolic AI programs can generate intelligence
If sub-symbolic AI programs, such as deep learning, can intelligently solve problems, then this is evidence that the necessary side of the PSSH is false.
If hybrid approaches that combine symbolic AI with other approaches can efficiently solve a wider range of problems than either technique alone, this is evidence that the necessary side is true and the sufficiency side is false.
Brooks
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."[19]
Connectionism and deep learning
In 2012 AlexNet, a deep learning network, outperformed all other programs in classifying images on ImageNet by a substantial margin. In the years since, deep learning has proved to be much more successful in many domains than symbolic AI.[citation needed]
Hybrid AI
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Symbol grounding
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See also
- Artificial intelligence, situated approach
- Artificial philosophy
Notes
- ^ Newell & Simon 1976, p. 116 and Russell & Norvig 2003, p. 18
- ^ Nilsson 2007, p. 1.
- ^ Dreyfus 1979, p. 156, Haugeland, pp. 15–44
- ^ Horst 2005
- ^ Newell, Shaw & Simon 1958.
- ^ McCorduck 2004, pp. 450–451.
- ^ Crevier 1993, pp. 258–263.
- ^ Dreyfus 1979, pp. 130–148.
- ^ McCorduck 2004, pp. 243–252.
- ^ Crevier 1993, pp. 52–107.
- ^ Russell & Norvig 2021, pp. 19–21.
- ^ a b Reconstructing Physical Symbol Systems David S. Touretzky and Dean A. Pomerleau Computer Science Department Carnegie Mellon University Cognitive Science 18(2):345–353, 1994. https://www.cs.cmu.edu/~dst/pubs/simon-reply-www.ps.gz
- ^ Nilsson 2007, p. 10.
- ^ Searle 1999, p. [page needed].
- ^ Dennett 1991, p. 435.
- ^ Nilsson, p. 1.
- ^ Dreyfus 1979, p. 156
- ^ Dreyfus 1972, Dreyfus 1979, Dreyfus & Dreyfus 1986. See also Crevier 1993, pp. 120–132 and Hearn 2007, pp. 50–51
- ^ Brooks 1990, p. 3
References
- , retrieved 2007-08-30.
- Cole, David (Fall 2004), "The Chinese Room Argument", in Zalta, Edward N. (ed.), The Stanford Encyclopedia of Philosophy.
- ISBN 0-465-02997-3.
- ISBN 978-0-7139-9037-9
- ISBN 978-0-06-011082-6
- Dreyfus, Hubert (1979), What Computers Still Can't Do, New York: MIT Press.
- Dreyfus, Hubert; Dreyfus, Stuart (1986), Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer, Oxford, U.K.: Blackwell
- ISBN 978-0-316-17232-5.
- Haugeland, John (1985), Artificial Intelligence: The Very Idea, Cambridge, Mass.: MIT Press.
- Hobbes (1651), Leviathan.
- Horst, Steven (Fall 2005), "The Computational Theory of Mind", in Zalta, Edward N. (ed.), The Stanford Encyclopedia of Philosophy.
- ISBN 978-0-670-03384-3.
- Rochester, Nathan; Shannon, Claude (1955), A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, archived from the originalon 2008-09-30.
- Shaw, J. C.; Simon, H. A. (1958), "Elements of a theory of human problem solving", Psychological Review, 65 (3): 151–166
- Newell, Allen; Simon, H. A. (1963), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E.A.; Feldman, J. (eds.), Computers and Thought, New York: McGraw-Hill
- Nilsson, Nils (2007), Lungarella, M. (ed.), "The Physical Symbol System Hypothesis: Status and Prospects"(PDF), 50 Years of AI, Festschrift, LNAI 4850, Springer, pp. 9–17
- OCLC 231867665
- doi:10.1093/mind/LIX.236.433, archived from the originalon 2008-07-02