BibSonomy publications for /user/emanuel/program_synthesisSun May 09 15:17:19 CEST 2010New York, NY, USAACM Transactions on Programming Languages and Systems190--121A Deductive Approach to Program Synthesis21980article ase deductive_program_synthesis program_synthesis Program synthesis is the systematic derivation of a program from a given specification. A deductive approach to program synthesis is presented for the construction of recursive programs. This approach regards program synthesis as a theorem-proving task and relies on a theorem-proving method that combines the features of transformation rules, unification, and mathematical induction within a single framework.Fri Apr 23 11:32:58 CEST 2010Logic Program Synthesis and Transformation, 7th International Workshop, LOPSTR'97, Proceedings128--147LNCSOn Correct Program Schemas14631998program_synthesis schemas Fri Apr 23 11:20:48 CEST 2010Logic Program Synthesis from Incomplete Information1995ilp inductive_programming program_synthesis Thu Apr 22 22:05:21 CEST 2010London, UKProceedings of the 6th European Conference on Machine Learning3--20LNCS{FOIL}: A Midterm Report1993applications enumerative_ip experiment foil ilp induction inductive_programming inproceedings machine_learning program_synthesis Thu Apr 22 22:04:43 CEST 2010Proceedings of the 13th International Joint Conference on Artificial Intelligence1050--1057Avoiding Pitfalls When Learning Recursive Theories1993enumerative_ip foil ilp induction inductive_programming inproceedings machine_learning program_synthesis Wed Apr 21 22:36:03 CEST 2010UnpublishedInductive Functional Program Synthesis -- A Term-Construction and Folding ApproachDiplomarbeit2003analytical_ip ifp igor1 induction inductive_programming mastersthesis program_synthesis recursive_program_schemes Wed Apr 21 22:14:40 CEST 2010Poster abstract at the German Conference on Artificial IntelligenceInduction of Functional Programs based on Relations between I/O Examples2006analytical_ip constructor_systems extended_abstract ifp igor2 induction inductive_programming program_synthesis Wed Apr 21 22:13:16 CEST 2010Cambridge, MA, USAJournal of Machine Learning ResearchRevised version of~\cite{KiSc05}429--454Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach72006analytical_ip article ebg ifp igor1 induction inductive_programming program_synthesis recursive_program_schemes We describe an approach to the inductive synthesis of recursive equations from input/output-examples which is based on the classical two-step approach to induction of functional Lisp programs of Summers (1977). In a first step, I/O-examples are rewritten to traces which explain the outputs given the respective inputs based on a datatype theory. These traces can be integrated into one conditional expression which represents a non-recursive program. In a second step, this initial program term is generalized into recursive equations by searching for syntactical regularities in the term. Our approach extends the classical work in several aspects. The most important extensions are that we are able to induce a set of recursive equations in one synthesizing step, the equations may contain more than one recursive call, and additionally needed parameters are automatically introduced.Wed Apr 21 22:07:00 CEST 2010Los Alamitos, CA, USAIEEE Transactions on Computers2122--136Speeding up the Synthesis of Programs from Traces241975analytical_ip enumerative_ip induction inductive_programming program_synthesis synthesis_from_traces An algorithm is given for synthesizing a computer program from a trace of its behavior. Since the algorithm involves a search, the length of time required to do the synthesis of nontrivial programs can be quite large. Techniques are given for preprocessing the trace information to reduce enumeration, for pruning the search using a failure memory technique, and for utilizing multiple traces to the best advantage. The results of numerous tests are given to demonstrate the value of the techniques.Wed Apr 21 22:06:18 CEST 2010Formal Methods at the Crossroads: From Panacea to Foundational Support267--286LNCSSoftware Development by Refinement27572003ase deductive_program_synthesis inproceedings kestrel overview program_synthesis software_engineering This paper presents an overview of the technical foundations and current directions of Kestrel’s approach to mechanizing software development. The approach emphasizes machine-supported refinement of property-oriented specifications to code, based on a category of higher-order specifications. A key idea is representing knowledge about programming concepts, such as algorithm design, and datatype refinement by means of taxonomies of design theories and refinements. Concrete refinements are generated by composing library refinements with a specification. The framework is partially implemented in the research systems Specware, Designware, Epoxi, and Planware. Specware provides basic support for composing specifications and refinements via colimit, and for generating code via logic morphisms. Specware is intended to be general-purpose and has found use in industrial settings. Designware extends Specware with taxonomies of software design theories and support for constructing refinements from them. Epoxi builds on Designware to support the specification and refinement of systems. Planware transforms behavioral models of tasks and resources into high-performance scheduling algorithms. A few applications of these systems are presented. ER -Wed Apr 21 22:05:42 CEST 2010Los Alamitos, CA, USAIEEE Transactions on Software Engineering3141--153Constructing Programs from Example Computations21976ase induction inductive_programming pre-summers program_synthesis synthesis_from_traces An autoprogrammer is an interactive computer programming system which automatically constructs computer programs from example computations executed by the user. The example calculations are done in a scratch pad fashion at a computer display using a light pen or other graphic input device, and the system stores a detailed history of all of the steps executed in the process. Then the system automatically synthesizes the shortest possible program which is capable of executing the observed examples. The paper describes the computational environment provided by the system, proves that the program synthesis technique is both "sound" and "complete," describes the design of the system, and gives some programs it was used to create.Wed Apr 21 22:04:40 CEST 2010AmsterdamInformation Processing 7741--45The Hierarchical Synthesis of {LISP} Scanning Programs1977analytical_ip ifp induction inductive_programming program_synthesis Wed Apr 21 22:03:53 CEST 2010IEEE Transactions on Systems, Man, and Cybernetics5260--276A Production Rule Mechanism for Generating {LISP} Code91979analytical_ip ifp induction inductive_programming program_synthesis Production rule schemas are given which hold the basic information necessary for coding recursive loops and branches in LISP. Information from the user concerning the desired program is used to instantiate the schemas to yield production rules, and then these rules generate executable code in a strictly syntactic fashion. Emphasis is placed on decomposing the synthesis problem into a hierarchy of tasks which can each be solved by application of a schema. The method is demonstrated by showing how programs can be synthesized from examples of their input-output behaviors.Wed Apr 21 21:56:05 CEST 2010Machine Learning195--117The Appropriateness of Predicate Invention as Bias Shift Operation in ILP201995bias_shift decidability ilp induction inductive_programming predicate_invention program_synthesis The task of predicate invention in Inductive Logic Programming is to extend the hypothesis language with new predicates if the vocabulary given initially is insufficient for the learning task. However, whether predicate invention really helps to make learning succeed in the extended language depends on the language bias currently employed.Wed Apr 21 21:55:06 CEST 2010International Joint Conference on Artificial Intelligence (IJCAI'75)268--273Automatic Program Synthesis from Example Problems1975ifp induction inductive_programming lisp pre-summers program_synthesis Wed Apr 21 21:54:38 CEST 2010International Joint Conference on Artificial Intelligence (IJCAI'75)260--267Inferring {LISP} Programs from Examples1975ifp induction inductive_programming lisp pre-summers program_synthesis Wed Apr 21 21:54:10 CEST 2010International Joint Conference on Artificial Intelligence (IJCAI'75)240--245Synthesis of {LISP} Functions from Examples1975ifp induction inductive_programming lisp pre-summers program_synthesis Tue Feb 23 13:10:12 CET 2010Journal of Machine Learning ResearchSpecial Topic on Inductive Programming493--518Learning Recursive Control Programs from Problem Solving72006inductive_programming learning-and-planning planning program_synthesis read Sun Jun 21 15:29:30 CEST 20097th International Workshop on Rule Based Programming {(RULE 2006)}Electronic Notes in Theoretical Computer Science149--63Inducing Constructor Systems from Example-Terms by Detecting Syntactical Regularities1742007analytical_ip article constructor_systems igor2 induction inductive_programming program_synthesis rule-based_programming We present a technique for inducing functional programs from few, well chosen input/output-examples (I/O-examples). Potential applications for automatic program or algorithm induction are to enable end users to create their own simple programs, to assist professional programmers, or to automatically invent completely new and efficient algorithms. In our approach, functional programs are represented as constructor term rewriting systems (CSs) containing recursive rules. I/O-examples for a target function to be implemented are a set of pairs of terms (F(i_i),o_i) meaning that F(i_i)---denoting application of function F to input i_i---is rewritten to o_i by a CS implementing the function F. Induction is based on detecting syntactic regularities between example terms. In this paper we present theoretical results and describe an algorithm for inducing CSs over arbitrary signatures/data types which consist of one function defined by an arbitrary number of rules with an arbitrary number of non-nested recursive calls in each rule. Moreover, we present empirical results based on a prototypical implementation.Sun Jun 21 15:27:54 CEST 2009{Beitr{\"a}ge zum Treffen der GI-Fachgruppe 1.1.3 Maschinelles Lernen (FGML 2002)}135--141Inductive Program Synthesis: From Theory to Application2002ifp induction inductive_programming inproceedings machine_learning program_synthesis recursive_program_schemes