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A theory and methodology of inductive learning
Compacts As described in the preceding material, the 'compacts' concept is a collective term for a number of short c o m m u n i c a t i o n s included in both the paper and online versions of the journal. In principle, a compact is not to exceed t w o pages of text on a normal video display terminal or to exceed 500 words. Compacts will present information in the f o l l o w i n g areas: 1. The online journal, explanations - Command overview -
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Editorials News Abstracts Journals
Books 5. A n n o u n c e m e n t s 6. Conferences 7. Organizational profiles Obviously, only a limited number of compacts per issue can be selected for publication in the paper version of COMPUTER C O M P A C T S . Selection will be based on topicality, but COMPUTER C O M P A C T S will remain sensitive to the interests of its readership. Only compacts from categories 3 - 6 have been presented in this issue. Statements of preference are invited and should be submitted to the Journal Manager. -
Abstracts Publications A Theory and Methodology of Inductive Learning
Forthcoming Prototypical Knowledge for Expert Systems by Janice S. Aikins
by Ryszard S. Michalski A theory of inductive learning is presented that characterizes it as a heuristic search through a space of symbolic descriptions, generated by an application of certain inference rules to the initial observational statements (the teacher-provided examples of some concepts, or facts about a class of objects or a phenomenon). The inference rules include generalization rules, which perform generalizing transformations on descriptions, and conventional truthpreserving deductive rules (specialization and reformulation rules). The application of the inference rules to descriptions is constrained by problem background knowledge, and guided by criteria evaluating the 'quality' of generated inductive assertions. Based on this theory, a general methodology for learning structural descriptions from examples, called STAR is described and illustrated by a problem from the area of conceptual data analysis (Artificial Intelligence 20, 2 (1983)).
Knowledge of situations typically encountered in performing a task is an important and useful source of information for solving that task. This paper presents a system that uses a representation of prototypical knowledge to guide computer consultations, and to focus the application of production rules used to represent inferential knowledge in the domain. The explicit representation of control knowledge for each prototypical situation is also emphasized (Artificial Intelligence 20, 2 (1983)).
On the Efficient Synthesis of Efficient Programs by Elaine Kant Efficiency is a problem in automatic programming - both in the programs produced and in the synthesis process itself. The efficiency problem arises because many target-
language programs (which vary in their time and space performance) typically satisfy one abstract specification. This paper presents a framework for using analysis and searching knowledge to guide program synthesis in a stepwise refinement paradigm. A particular implementation of the framework, called LIBRA, is described. Given a program specification that includes size and frequency notes, the performance measure to be minimized, and some limits on synthesis resources, LIBRA selects algorithms and data representations and decides whether to use 'optimizing' transformations. By applying incremental, algebraic program analysis, explicit rules about plausible implementations, and resource allocation on the basis of decision importance, LIBRA has guided the automatic implementation of a number of programs in the domain of symbolic processing (Artificial Intelligence 21, 1 (1983)).
On the Nature of Pathology in Game Searching by Judea Pearl Game-playing programs usually process the estimates attached to game positions through repeated minimax operations, as if these were true terminal payoffs. This process introduces a spurious noise which degrades the quality of the decisions and, in the extreme case, may cause a pathological 49
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Navigating - Searching Displaying Printing Setting terminal characteristics -
2. 3. 4. -
Editorials News Abstracts Journals
Books 5. A n n o u n c e m e n t s 6. Conferences 7. Organizational profiles Obviously, only a limited number of compacts per issue can be selected for publication in the paper version of COMPUTER C O M P A C T S . Selection will be based on topicality, but COMPUTER C O M P A C T S will remain sensitive to the interests of its readership. Only compacts from categories 3 - 6 have been presented in this issue. Statements of preference are invited and should be submitted to the Journal Manager. -
Abstracts Publications A Theory and Methodology of Inductive Learning
Forthcoming Prototypical Knowledge for Expert Systems by Janice S. Aikins
by Ryszard S. Michalski A theory of inductive learning is presented that characterizes it as a heuristic search through a space of symbolic descriptions, generated by an application of certain inference rules to the initial observational statements (the teacher-provided examples of some concepts, or facts about a class of objects or a phenomenon). The inference rules include generalization rules, which perform generalizing transformations on descriptions, and conventional truthpreserving deductive rules (specialization and reformulation rules). The application of the inference rules to descriptions is constrained by problem background knowledge, and guided by criteria evaluating the 'quality' of generated inductive assertions. Based on this theory, a general methodology for learning structural descriptions from examples, called STAR is described and illustrated by a problem from the area of conceptual data analysis (Artificial Intelligence 20, 2 (1983)).
Knowledge of situations typically encountered in performing a task is an important and useful source of information for solving that task. This paper presents a system that uses a representation of prototypical knowledge to guide computer consultations, and to focus the application of production rules used to represent inferential knowledge in the domain. The explicit representation of control knowledge for each prototypical situation is also emphasized (Artificial Intelligence 20, 2 (1983)).
On the Efficient Synthesis of Efficient Programs by Elaine Kant Efficiency is a problem in automatic programming - both in the programs produced and in the synthesis process itself. The efficiency problem arises because many target-
language programs (which vary in their time and space performance) typically satisfy one abstract specification. This paper presents a framework for using analysis and searching knowledge to guide program synthesis in a stepwise refinement paradigm. A particular implementation of the framework, called LIBRA, is described. Given a program specification that includes size and frequency notes, the performance measure to be minimized, and some limits on synthesis resources, LIBRA selects algorithms and data representations and decides whether to use 'optimizing' transformations. By applying incremental, algebraic program analysis, explicit rules about plausible implementations, and resource allocation on the basis of decision importance, LIBRA has guided the automatic implementation of a number of programs in the domain of symbolic processing (Artificial Intelligence 21, 1 (1983)).
On the Nature of Pathology in Game Searching by Judea Pearl Game-playing programs usually process the estimates attached to game positions through repeated minimax operations, as if these were true terminal payoffs. This process introduces a spurious noise which degrades the quality of the decisions and, in the extreme case, may cause a pathological 49