BibSonomy publications for /user/ip/importedThu Dec 06 13:02:35 CET 2007ILPLecture Notes in Computer ScienceInductive Logic Programming, 16th International Conference, ILP 2006, Santiago de Compostela, Spain, August 24-27, 2006, Revised Selected Papers44552007imported Thu Dec 06 13:02:35 CET 2007ILPDBLP:conf/ilp/200679-93Lecture Notes in Computer ScienceLearning Recursive Patterns for Biomedical Information Extraction44552006imported Thu Dec 06 13:02:35 CET 2007ILPLecture Notes in Computer ScienceInductive Logic Programming, 14th International Conference, ILP 2004, Porto, Portugal, September 6-8, 2004, Proceedings31942004imported Thu Dec 06 13:02:35 CET 2007ILPDBLP:conf/ilp/200444-62Lecture Notes in Computer ScienceOn the Effect of Caching in Recursive Theory Learning31942004imported Thu Dec 06 13:02:35 CET 2007Fundam. Inform.139-77Learning Recursive Theories in the Normal ILP Setting572003imported Thu Dec 06 13:02:35 CET 2007ILPLecture Notes in Computer ScienceInductive Logic Programming, 10th International Conference, ILP 2000, London, UK, July 24-27, 2000, Proceedings18662000imported Thu Dec 06 13:02:35 CET 2007ILPDBLP:conf/ilp/200093-111Lecture Notes in Computer ScienceInduction of Recursive Theories in the Normal ILP Setting: Issues and Solutions18662000imported Wed Nov 21 12:01:39 CET 2007New York, NY, USAProceedings of the eighteenth annual computer personnel research conference302--322A study of programming problem representation in novice and expert programmers1981imported Based on a study by Chi and Glasser on physics problem-solving, [1] we hypothesized that novices represent computer programming problems initially on the basis of surface features, such as application areas, while experts represent programming problems on the basis of deep features, such as solution method and data structures. Using a problem sorting task, we studied differences in representation among beginning programmers, experienced programmers, and programming managers. Each participant in the study was given specifications for 27 different programming problems, and was asked to group together problems whose method of solution would be similar. The 27 problems were designed to have three different treatments on each of three dimensions of variation: data-structure, algorithm, and application area. Beginning programmers tended to group problems by application area, experienced programmers grouped by algorithm, and programming managers used a mix of the two methods. The experts were more consistent in their groupings and took more time to complete the grouping task, both of which are consistent with Chi and Glasser and reflect the expected novice/expert representation difference.Wed Nov 21 12:00:38 CET 2007IJCAI260-267Inferring LISP Programs From Examples1975imported Wed Nov 21 11:58:03 CET 2007New York, NY, USAProceedings of the ACM SIGPLAN symposium on Very high level languages1--12An overview of nonprocedural languages1974imported This paper attempts to describe some of the basic characteristics and issues involving the class of programming languages commonly referred to as “nonprocedural” or “very high level”. The paper discusses major issues such as terminology, relativeness, and arbitrary sequencing. Five features of nonprocedural languages are described, and a number of specific languages are discussed briefly. A short history of the subject is included.Wed Nov 21 11:57:08 CET 2007New YorkEncyclopedia of Artificial Intelligence. 2nd edition18 - 35Automatic Programming1992imported Wed Nov 21 11:56:10 CET 2007Journal of Educational Computing ResearchChildren's Mental Models of Recursive Logo Programs.1:21985imported Wed Nov 21 11:50:54 CET 2007Department of Computer Sciences, The University of Texas at Austin, Austin, Texas 78712, USA; Department of Information Science, Uppsala University, Box 311, S-751 05 Uppsala, SwedenInternational Journal of Intelligent Systems10995-1019Completing open logic programs by constructive induction141999imported Wed Nov 21 11:44:28 CET 2007Los Altos, CAMachine Learning -- An Artificial Intelligence Approach393-422 Programming by analogy21986imported Wed Nov 21 11:44:28 CET 2007{Proc. International Workshop on Analogical and Inductive Inference (AII '89)}45--59LNCSWhy and how program synthesis?1989imported Wed Nov 21 11:44:28 CET 2007Proc. {AISE Meeting Hambourg}169-175A sane algorithm for the synthesis of {LISP} functions from example problems: {The Boyer and Moore} algorithm1978imported Wed Nov 21 11:44:28 CET 2007Proc. {International Joint Conference on Artificial Intelligence (IJCAI-79)}440--447Characterization of a class of functions synthesized from examples by a Summers like method using a `{B}.{M}.{W}.' matching technique1979imported Wed Nov 21 11:44:28 CET 2007Hillsdale, NJStudying the Novice Programmer1989imported Wed Nov 21 11:44:28 CET 2007Berlin{Proceedings of the 7th International Workshop on Inductive Logic Programming}September17--20~296--308LNAIWhich Hypotheses Can Be Found with inverse Entailment?12971997imported Wed Nov 21 11:44:28 CET 2007AmsterdamArtificial Intelligence: Methodology, Systems, Applications3-11Program synthesis by hierarchical planning1987imported