Recent Developments in SAIL. An ALGOL-Based Language for Artificial Intelligence

Feldman, Jerome A.; Low, James Richard; Swinehart, Daniel C.; Taylor, R. H.

doi:10.21236/ad0754109

Cited by 17 publications

(7 citation statements)

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“…A prototype system for semi-automatic selection of representations was constructed for a subset of the SAIL programming language (5,12). SAIL is an ALGOL-60 based language with extensions including varying length strings, processes, event mechanisms, and an associative data language called LEAP (4).…”

Section: Section II Prototype Systemmentioning

confidence: 99%

Automatic data structure selection

Low

1978

Commun. ACM

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The use of several levels of abstraction has proved to be very helpful in constructing and maintaining programs. Abstract data types such as sets and lists reduce programmer time by relieving ~irn from the petty details of low-level implementations.In the past, programming systems have provided only a single qeneral purpose implementation for an abstract type. Thus the programs produced using abstract types were often inefficient in space or time. In this paper a system for automatically choosing efficient implementations for abstract types from a library of implementations is discussed. ~~ example program is processed in detail.

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Section: Section II Prototype Systemmentioning

confidence: 99%

Automatic data structure selection

Low

1978

Commun. ACM

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“…A branch-and-bound algorithm, such as A*, can then schedule the processes (subgoals), always executing the most promising subgoal. Such dynamic allocation of effort to problem-solving processes motivated the design of the SAIL multiple process structure (Feldman, 1972).…”

Section: Coping With Uncertaintymentioning

confidence: 99%

Decision Theory and Artificial Intelligence II: The Hungry Monkey*

Feldman

Sproull

1977

Cognitive Science

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This paper describes a problem-solving framework In which aspects of mathematical declsion theory are incorporated into symbolic problem-solving techmques currently predominant in artificial intelligence. The utility function of declslon theory IS used to reveal . tradeoffs among competing strategies for achieving various goals, taking into account such factors as reliability, the complexity of steps in the strategy, and the value of the goal. The utility funchon on strategies can therefore be used as a guide when searching for good strategies. It is also used to formulate solutions to the problems of how to acquire a world model, how much planning effort is worthwhile, and whether verification tests should be performed. These techniques are illustrated by application to the classic monkey and bananas problem.

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“…These languages -such as QA4 (Rulifson et al, 1972), PLANNER (Hewitt, 1972), CONNIVER (Sussman and McDermott, 1972), and SAIL (Feldman et al, 1972) incorporate features that make it natural for the programmer to use techniques analogous to some described earlier. Indeed, these languages are among the most important results of artificial intelligence research, in that they embody and codify those techniques that have proven most successful.…”

Section: Problem-solving Languagesmentioning

confidence: 99%