Computer Science as Empirical Inquiry  : Symbols and Search Allen Newell and Herbert A.Simon1976

Computer Science as Empirical Inquiry : Symbols and Search Allen Newell and Herbert A.Simon1976 PowerPoint PPT Presentation


  • 96 Views
  • Uploaded on
  • Presentation posted in: General

1. Introduction. Computer Science is an empirical discipline.Each new machine and new program that are built are experiments.It poses a question to nature, and its behavior offers clues to an answer.As basic scientists we build machines and programs as a way of discovering new phenomena and anal

Download Presentation

Computer Science as Empirical Inquiry : Symbols and Search Allen Newell and Herbert A.Simon1976

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


1. Computer Science as Empirical Inquiry : Symbols and Search Allen Newell and Herbert A.Simon(1976) Interdisciplinary Program in Cognitive Science Lee Jung-Woo March, 22, 1999

2. 1. Introduction Computer Science is an empirical discipline. Each new machine and new program that are built are experiments. It poses a question to nature, and its behavior offers clues to an answer. As basic scientists we build machines and programs as a way of discovering new phenomena and analyzing phenomena we already know about.

3. 2. Symbols and Physical Symbol System 2.1 Laws of Qualitative Structure All science characterize the essential nature of the systems they study. These characterizations are invariably qualitative in nature, for they set the terms within which more detailed knowledge can be developed. The Cell Doctrine in Biology / Plate Tectonics in Geology The Germ Theory of Disease / The Doctrine of Atomism 2.2 Physical Symbol Systems 2.3 Development of the Symbol System Hypothesis 2.4 The Evidence

4. 2.2 Physical Symbol Systems(1) Requirement for Intelligent Action The ability to store and manipulate symbols Physical Symbol System “Physical” : (1) obey the laws of physics(realizable by engineering) (2) not restricted to human symbol systems Symbol(physical pattern), Expression(symbol structure), Process(creation,modification,reproduction,destruction) Designation : An expression designate an object or an process Interpretation : The system can interpret an expression Additional requirements

5. 2.2 Physical Symbol Systems(2) Physical Symbol System Hypothesis(PSSH) A physical symbol system has the necessary and sufficient means for general intelligent action This is an empirical hypothesis. Scientifically, one can attack or defend it only by bringing forth empirical evidence about the natural world. We need to trace the development of this hypothesis and look at the evidence for it.

6. 2.3 Development of the PSSH(1) Formal Logic Program of Frege, Whitehead and Russell for formalizing logic Mathematical logic(propositional, first-order, and higher-order logics) “Symbol game” : Logic was a game played with meaningless tokens according to certain purely syntactic rules. All meaning had been purged. One had a mechanical system about which various things could be proved.

7. 2.3 Development of the PSSH(2) Turing Machines and Digital Computer The Stored Program Concept List Processing Lisp

8. 2.4 The Evidence for PSSH(1) The evidence for the hypothesis that physical symbol systems are capable of intelligent action, and that general intelligent action calls for a physical symbol system. The evidence for the sufficiency of physical symbol systems for producing intelligence(Attempt to construct and test specific systems that have such a capability) -- Artificial Intelligence The evidence for the necessity of having a physical symbol systems wherever intelligence is exhibited.(Attempt to discover whether Man’s cognitive activity can be explained as the working of a physical symbol system) -- Cognitive Psychology.

9. 2.4 The Evidence for PSSH(2) Constructing Intelligent Systems(A.I.) Identify a task domain calling for intelligence, then construct a program for a digital computer that can handle tasks in that domain Puzzles and games such as chess programs System that handle and understand natural language, systems for interpreting visual scenes, systems for hand-eye coordination, systems that design, systems that writhe computer programs, systems for speech understanding General Problem Solver(GPS), PLANNER, CONNIVER An initial burst of activity aimed at building intelligent programs for a wide variety of almost randomly selected tasks is giving way to more sharply targeted research aimed at understanding the common mechanisms of such systems.

10. 2.4 The Evidence for PSSH(3) The Modeling of Human Symbolic Behavior(Cognitive Psychology) The search for explanations of man’s intelligent behavior in terms of symbol systems has had a large measure of success to the point where information processing theory is the leading contemporary point of view in cognitive psychology. In the areas of problem solving, concept attainment, and long-term memory, symbol manipulation models now dominate the scene. Other Evidence Negative evidence : the absence of specific competing hypotheses as to how intelligent activity might be accomplished ex. Behaviorism and Gestalt theory

11. 3. Heuristic Search Question : “OK, so far. But how physical symbol systems accomplish such intelligent actions?” Answer : Symbol systems solve problems by using the processes of heuristic search Heuristic Search Hypothesis The solution to problems are represented as symbol structures. A physical symbol system exercises it intelligence in problem solving by search-that is, by generating and progressively modifying symbol structures until it produces a solution structure.

12. 3.1 Problem Solving(1) Ability to solve problem is generally taken as a prime indicator that a system has intelligence To state a problem is to designate (1) a test for a class of symbol structures(solutions of the problem) and (2) a generator of symbol structures(potential solutions). To solve a problem is to generate a structure, using (2), that satisfies the test of (1)

13. 3.1 Problem Solving(2) The physical symbol systems can represent problem spaces and possess move generators. Problem space : a space of symbol structures in which problem situations, including the initial and goal situations, can be represented. Move generator : the processes for modifying one situation in the problem space into another. The physical symbol systems’ task, when it is presented with a problem and a problem space, is to use its limited processing resources to generate possible solution, one after another, until it finds one that satisfies the problem-defining test.

14. 3.2 Search in Problem Solving(1) The study of problem solving was almost synonymous with the study of search processes Extracting Information from the Problem Space A condition for the appearance of intelligence is that the space of symbol structures exhibit at least some degree of order and pattern. Pattern in the space of symbol structures be more or less detectable The generator of potential solutions be able to behave differentially, depending on what pattern it detected. Ex) AX+B = CX+D --> X = E

15. 3.2 Search in Problem Solving(2) Search Trees Programs that play chess VS. Strongest human players Search is a fundamental aspect of a symbol system’s exercise of intelligence in problem solving but that amount of search is not a measure of the amount of intelligence being exhibited. When the symbolic systems that is endeavoring to solve a problem knows enough what to do, it simply proceeds directly towards its goal.

16. 3.2 Search in Problem Solving(3) The Forms of Intelligence An intelligent system generally needs to supplement the selectivity of its solution generator with other information-using techniques to guide search, that is, to generate only structures that show promise of being solutions or of being along the path toward solutions. In serial heuristic search, the basic question always is : “What shall be done next?” That question has two components : (1) from what node in the tree shall we search next, and (2) what direction shall we take from that node?

17. 3.2 Search in Problem Solving(4) A Summary of the Experience First conclusion : from what has been learned about human expert performance in tasks like chess, it is likely that any system capable of matching that performance will have to have access, in its memories, to very large stores of semantic information. Second conclusion : some part of the human superiority in tasks with a large perceptual component can be attributed to the special-purpose built-in parallel processing structure of the human eye and ear.

18. 3.3 Intelligence Without Much Search(1) Our analysis of intelligence equated it with ability to extract and use information about the structure of the problem space, so as to enable a problem solution to be generated as quickly and directly as possible Nonlocal Use of Information Information gathered in the course of tree search was usually only used locally, to help make decisions at the specific node. In recent years, a few exploratory efforts have been made to transport information from its context of origin to other appropriate contexts. Berliner(1975) : use causal analysis to determine the range over which a particular piece of information is valid.

19. 3.3 Intelligence Without Much Search(2) Semantic Recognition Systems A second active possibility for raising intelligence is to supply the symbol system with a rich body of semantic information about the task domain it is dealing with. What is new is the realization of the number of patterns and associated information that may have to be stored for master-level play. A particular, and especially a rare, pattern can contain an enormous amount of information, provided that it is closely linked to the structure of the problem space.

20. 3.3 Intelligence Without Much Search(3) Selecting Appropriate Representations A third line of inquiry is concerned with the possibility that search can be reduced or avoided by selecting an appropriate problem space.

21. 4. Conclusion(1) Physical Symbol Systems Intelligence resides in physical symbol systems. This is computer science’s most basic law of qualitative structure. Symbol systems are collections of patterns and processes, the latter being capable of producing, destroying and modifying the former. The most important properties of patterns is that they can designate objects, processes, or other patterns, and that, when they designate processes, they can be interpreted. Interpretation means carrying out the designated process. Symbolic system : Formal logic, The Turing machine, The stored-program concept, List processing

22. 4. Conclusion(2) Heuristic Search A second law of qualitative structure for AI is that symbol systems solve problems by generating potential solutions and testing them, that is, by searching. Since they have finite resources, the search cannot be carried out all at once, but must be sequential. They exercise intelligence by extracting information from a problem domain and using that information to guide their search, avoiding wrong turns and circuitous bypaths.

23. Postscript There remain intellectual positions that stand outside the entire computational view and regard the hypothesis as undoubtedly false(Dreyfus 1979, Searle 1980) Philosopher : The central problem of semantics or intentionality-how symbols signify their external referents-is not addressed by physical symbol systems. Connectionists : There are forms of processing organization that will accomplish all that symbol systems do, but in which symbols will not be identifiable entities.

  • Login