microPLANNER(ID:516/mic020)

LISP implementation of PLANNER 

G.J. Sussman, Terry Winograd and Eugene Charniak, MIT 1970.

Subset of PLANNER, implemented in LISP. Superseded by Conniver. Important features: goal-oriented, pattern- directed procedure invocation, embedded knowledge base, automatic backtracking.
Related languages
PLANNER => microPLANNER   Subset
microPLANNER => CONNIVER   Evolution of
microPLANNER => PLANNER-73   Influence
microPLANNER => Prolog   Influence
microPLANNER => Scheme   Evolution of
References:
  • Sussman, Gerald Jay; and Winograd, Terry "microPLANNER Reference Manual", AI Memo 203, MIT AI Lab, July 1970. view details Abstract: Micro-Planner is an implementation of a subset of Cal Hewitt's language, PLANNER by Gerald Jay Sussman, Terry Winograd, and Eugene Charniak on the AI group computer in LISP. Micro-Planner is now a publically accessible systems program in the AI group systems ITS. The current version of Micro-Planner, embedded in an allocated LISP, may be obtained by incanting ':PLNR' or 'PLNR' to DDT. Micro-Planner is also available as EXPR code or LAP code. All questions, suggestions, or comments about Micro-Planner should be directed to Gerald Jay Sussman (login name GJS) who will maintain the program.
  • Sussman, Gerald Jay; Winograd, Terry and Charniak, Eugene "Micro-Planner Reference Manual (Update)" AI Memo 203A, MIT AI Lab, December 1971 view details Abstract: This is a manual for the use of the Micro Planner interpreter, which implements a subset of Carl Hewitt's language, PLANNER and is now available for use by the Artificial Intelligence Group.
  • Baumgart, B.G. "Micro-Planner Alternate Reference Manual" Stanford AI Lab Operating Note No. 67, April 1972. view details
  • Bobrow, D.G. and B. Raphael, "New programming languages for artificial intelligence" view details Extract: About Planner, MicroPlannerm, Conniver
    The PLANNER concept was developed by Hewitt at MIT starting in 1967 (Hewitt 1971, 197Z), and Sussman and Winograd built a first implementation, MICRO-PLANNER, which contained a subset of PLANNER features. These projects established the basis of the currently popular concept of procedural representation of knowledge. CONNIVER is a recent attempt by Sussman at MIT to remedy some observed shortcomings in the practical use of PLANNER, while preserving its good ideas.
          in [ACM] ACM Computing Surveys (CSUR) 6(3) September 1974 view details
  • Chuck Rieger, Hanan Samet. and Jonathan Rosenberg. "Artificial Intelligence Programming Languages for Computer Aided Manufacturing" Maryland Univ College Park Dept of Computer Science Sep 77 TR-595 AD-A047 179/7WC view details Abstract: Eight Artificial Intelligence programming languages (SAIL, LISP, MICROPLANNER, CONNIVER, MLISP, POP-2, AL and QLISP) are presented and surveyed, with examples of their use in an automated shop environment. Control structures are compared, and distinctive features of each language are highlighted. A simple programming task is used to illustrate programs in SAIL. LISP, MICROPLANNER and CONNIVER. The report assumes reader knowledge of programming concepts, but not necessarily of the languages surveyed.
          in [ACM] ACM Computing Surveys (CSUR) 6(3) September 1974 view details
  • Kornfeld, William A. "Pattern-directed invocation languages" pp34-48 view details
          in Byte, 4(8) (August 1979) view details
  • Faught, W. S., review of Kornfield view details Extract: Review
    Pattern-directed invocation languages (PDILs) are part of a technology developed by artificial intelligence researchers as one of several attempts to write systems without fixed control structures, i.e., systems whose control structures are not guided by a predetermined sequence of actions but by the data of the actual problem being solved. Kornfeld describes the basis of one such PDIL, Micro-Planner, in this article.

    The article could be more accurately titled "Pattern directed invocation in database manipulation." Kornfeld focuses on how PDILs can be used to collect facts into specialized databases. These databases use the various possible manipulations of those facts as triggers for computation.
    Kornfeld also demonstrates the utility of associative storage and retrieval of complex patterns and clearly describes basic matching algorithms based on the LISP language to implement the retrieval. The real contribution of PDILs is in the invocation of program-data elements based on a data pattern. In this area, Kornfeld describes the formalism by which simple deductions can be made, including variable binding and backward chaining.
    The only drawback to the article is the author's failure to reference the rich literature in PDILs, such as the recent voluminous collection of examples found in [1].

    W. S. Faught, Santa Monica, Calif.

    REFERENCE
    [1] WATERMAN, D. A.; AND HAYES-ROTH, F. (Eos.) Proc. workshop on pattern directed inference systems (Honolulu, 1977), Academic Press, New York, 1977.

          in ACM Computing Reviews 21(01) January 1980 view details