Recent Improvements of MagicHaskeller

Katayama, Susumu

doi:10.1007/978-3-642-11931-6_9

Cited by 12 publications

(4 citation statements)

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“…We applied our algorithm to MagicHaskeller [13], our systematic exhaustive search library for Haskell. {f1, f2}, {f1, f2, g1}, {f1, f2, g1, h1, h2}, ...], where f1 and f2 are obtained from the first search, g1 is obtained from the first deepening, and h1 and h2 are obtained from the second deepening.…”

Section: Resultsmentioning

confidence: 99%

Efficient Exhaustive Generation of Functional Programs Using Monte-Carlo Search with Iterative Deepening

Katayama

2008

Lecture Notes in Computer Science

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Abstract. Genetic programming and inductive synthesis of functional programs are two major approaches to inductive functional programming. Recently, in addition to them, some researchers pursue efficient exhaustive program generation algorithms, partly for the purpose of providing a comparator and knowing how essential the ideas such as heuristics adopted by those major approaches are, partly expecting that approaches that exhaustively generate programs with the given type and pick up those which satisfy the given specification may do the task well. In exhaustive program generation, since the number of programs exponentially increases as the program size increases, the key to success is how to restrain the exponential bloat by suppressing semantically equivalent but syntactically different programs. In this paper we propose an algorithm applying random testing of program equivalences (or Monte-Carlo search for functional differences) to the search results of iterative deepening, by which we can totally remove redundancies caused by semantically equivalent programs. Our experimental results show that applying our algorithm to subexpressions during program generation remarkably reduces the computational costs when applied to rich primitive sets.

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Section: Resultsmentioning

confidence: 99%

Efficient Exhaustive Generation of Functional Programs Using Monte-Carlo Search with Iterative Deepening

Katayama

2008

Lecture Notes in Computer Science

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“…There are two main approaches (and combinations thereof): generate-and-test IP, which systematically produces many candidate function definitions and uses the I/O pairs to filter them, and analytical IP, which pursues synthesis directly using strategies modelled upon human programming, like detection of regularities in the I/O pairs. A recent representative of the generate-andtest approach is MagicHaskeller [Katayama 2007[Katayama , 2010, and of the analytical approach, is Igor-II [Kitzelmann and Schmid 2006;Kitzelmann 2007;Hofmann 2010]. Both systems generate functional (Haskell) programs.…”

Section: Inductive Program Synthesismentioning

confidence: 99%

Ideas for connecting inductive program synthesis and bidirectionalization

Voigtländer

2012

Proceedings of the ACM SIGPLAN 2012 Workshop on Partial Evaluation and Program Manipulation

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We share a vision of connecting the topics of bidirectional transformation and inductive program synthesis, by proposing to use the latter in approaching problematic aspects of the former. This research perspective does not present accomplished results, rather opening discussion and describing experiments designed to explore the potential of inductive program synthesis for bidirectionalization (the act of automatically producing a backwards from a forwards transformation), in particular to address the issue of integrating programmer intentions and expectations.

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“…As for non-GP systems, an approach using delayed-acceptance hillclimbing for inductive synthesis proved competitive with GP on PSB1, including producing the only known solutions to the Collatz Numbers problem [44]. A comparison was made between Flash Fill [11], MagicHaskeller [26], PushGP, and G3P, finding that the non-GP methods fared much worse but ran much faster than the GP methods [41]. Finally, Monte Carlo tree search was used to generate Java bytecode programs using a few of the problems in PSB1 [31].…”

Section: Past Research Using Psb1mentioning

confidence: 99%

Psb2

Helmuth

Kelly

2021

Proceedings of the Genetic and Evolutionary Computation Conference

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For the past six years, researchers in genetic programming and other program synthesis disciplines have used the General Program Synthesis Benchmark Suite to benchmark many aspects of automatic program synthesis systems. These problems have been used to make notable progress toward the goal of general program synthesis: automatically creating the types of software that human programmers code. Many of the systems that have attempted the problems in the original benchmark suite have used it to demonstrate performance improvements granted through new techniques. Over time, the suite has gradually become outdated, hindering the accurate measurement of further improvements. The field needs a new set of more difficult benchmark problems to move beyond what was previously possible.In this paper, we describe the 25 new general program synthesis benchmark problems that make up PSB2, a new benchmark suite. These problems are curated from a variety of sources, including programming katas and college courses. We selected these problems to be more difficult than those in the original suite, and give results using PushGP showing this increase in difficulty. These new problems give plenty of room for improvement, pointing the way for the next six or more years of general program synthesis research. CCS CONCEPTS• Software and its engineering → Automatic programming.

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Recent Improvements of MagicHaskeller

Cited by 12 publications

References 11 publications

Efficient Exhaustive Generation of Functional Programs Using Monte-Carlo Search with Iterative Deepening

Efficient Exhaustive Generation of Functional Programs Using Monte-Carlo Search with Iterative Deepening

Ideas for connecting inductive program synthesis and bidirectionalization

Psb2

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