The Inference of Regular LISP Programs from Examples

By Alan W. Biermann. 1978

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Citations in the corpus

Continual and Multi-Task Architecture Search (2019, methodology): “NAS shares some similarity to program synthesis and inductive programming (Summers, 1986; Biermann, 1978), and it has been successfully applied to some simple Q&A tasks (Liang et al., 2010; Neelakantan et al., 2015; Andreas et al., 2016; Lake et al., 2015).”
Neuro-Symbolic Program Synthesis (2016, background): “Program Synthesis, the problem of automatically constructing programs that are consistent with a given speciﬁcation, has long been a subject of research in Computer Science (Biermann, 1978; Summers, 1977).”
Inductive Logic Programming: Theory and Methods (1994, neutral): “Logic programs are arguably much easier to manipulate for a machine learning algorithm than other universal representations which have been investigated, such as Universal Turing Machines programs [14] and LISP programs [133, 12].”
Neural Program Synthesis with Priority Queue Training (2018, methodology): “oundat https://github.com/tensorﬂow/models/tree/master/research/brain_coder. 2 RELATED WORK Ourmethodsharesthesamegoalwithtraditionaltechniquesinprogramsynthesisandinductiveprogramming(Summers, 1977; Biermann, 1978; Muggleton& de Raedt, 1994; Angulin, 1987). These techniqueshave foundmanyimporta”
Bayesian Sketch Learning for Program Synthesis (2017, background): “The automatic synthesis of programs [11, 18, 29, 47] is a long-standing goal in computer science.”
Inductive Programming: A Survey of Program Synthesis Techniques (2009, methodology): “On the contrary, Biermann’s approach [3] is to employ traces (fragments) to speed up an exhaustive enumeration of a well-defined program class, the so-called regular LISP programs.”
Neural Architecture Search with Reinforcement Learning (2016, methodology): “Neural Architecture Search has some parallels to program synthesis and inductive programming, the idea of searching a program from examples (Summers, 1977; Biermann, 1978).”
An Empirical Study of the Non-Determinism of ChatGPT in Code Generation (2023, unknown): “When setting temperature=0, the code candidates generated from the Concise prompt are more deterministic than our Base prompt, while those code candidates generated from the CoT prompt suffer from higher randomness than our Base prompt.”
Acquiring Recursive and Iterative Concepts with Explanation-Based Learning (1990, methodology): “Some research in automatic programming shares many characteristics with this approach, namely that involving program synthesis from examples (e.g., Bauer, 1979; Biermann, 1978; Kodratoff, 1979; Summers, 1977).”
Program Synthesis (2017, methodology): “Summers [137] and Biermann [13] developed techniques to learn a rich class of LISP programs from multiple input-output examples.”
Neural Guided Constraint Logic Programming for Program Synthesis (2018, background): “Programming by example (PBE) problems have a long history dating to the 1970’s [14, 15].”