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Self-Learned Formula Synthesis in Set Theory [article]

Chad E. Brown, Thibault Gauthier
2019 arXiv   pre-print
A reinforcement learning algorithm accomplishes the task of synthesizing a set-theoretical formula that evaluates to given truth values for given assignments.  ...  Hereditarily finite sets In [1] Ackermann proved consistency of Zermelo's axioms of set theory without an axiom of infinity by interpeting natural numbers 0, 1, 2, . . . as sets.  ...  This resulted in a set F of 6750 formulas varying in size from 3 to 15 distributed as indicated in Table 1 .  ... 
arXiv:1912.01525v1 fatcat:g6cqft7vrbfc7kxdgzmzx2l7ne

Fast, Flexible, and Minimal CTL Synthesis via SMT [chapter]

Tobias Klenze, Sam Bayless, Alan J. Hu
2016 Lecture Notes in Computer Science  
We show how to formulate CTL model checking in terms of "monotonic theories", enabling us to use the SAT Modulo Monotonic Theories (SMMT) [5] framework to build an efficient SAT-modulo-CTL solver.  ...  CTL synthesis [8] is a long-standing problem with applications to synthesising synchronization protocols and concurrent programs.  ...  This work was supported in part by a grant from the Natural Sciences and Engineering Research Council of Canada. We also thank Javier Esparza for his encouragement and helpful advice.  ... 
doi:10.1007/978-3-319-41528-4_8 fatcat:hqjdzwzaprgwtmoojkmbszhxqa

Comparison of Syntactic and Semantic Representations of Programs in Neural Embeddings [article]

Austin P. Wright, Herbert Wiklicky
2020 arXiv   pre-print
This work aims to be the first empirical study comparing the effectiveness of natural language models and static analysis graph based models in representing programs in deep learning systems.  ...  Neural approaches to program synthesis and understanding have proliferated widely in the last few years; at the same time graph based neural networks have become a promising new tool.  ...  So we define theories for these domains in first order logic, and then use these theories in formulae to determine satisfiability, giving the Satisfiability Modulo Theories (SMT) problem.  ... 
arXiv:2001.09201v1 fatcat:afdy5whh7befpp5yfv3yuoons4

Page 1490 of Mathematical Reviews Vol. 41, Issue 5 [page]

1971 Mathematical Reviews  
T. 8169 Simultaneous detection and estimation of pattern charac- teristics in self-learning systems. Proc.  ...  In this paper, some results are presented concerning the inference of logical formulas, using the inverse method and the notion of favorable sets.  ... 

Page 1580 of Mathematical Reviews Vol. 55, Issue 5 [page]

1978 Mathematical Reviews  
*Pattern synthesis. Lectures in pattern theory. Vol. 1.  ...  Author’s summary: “Principles of design of a system capable of an active dialogue about facts are set forth. Information appears as formulae of a special language.  ... 

Manthan: A Data-Driven Approach for Boolean Function Synthesis [chapter]

Priyanka Golia, Subhajit Roy, Kuldeep S. Meel
2020 Lecture Notes in Computer Science  
Motivated by the progress in machine learning, we propose Manthan, a novel data-driven approach to Boolean functional synthesis.  ...  Boolean functional synthesis is a fundamental problem in computer science with wide-ranging applications and has witnessed a surge of interest resulting in progressively improved techniques over the past  ...  This work was supported in part by National Research Foundation Singapore under its NRF Fellowship Programme [NRF-NRFFAI1-2019-0004] and AI Singapore Programme [AISG-RP-2018-005], and NUS ODPRT Grant [  ... 
doi:10.1007/978-3-030-53291-8_31 fatcat:xvkvq2axajfmhbaofayqk6laqq

Manthan: A Data Driven Approach for Boolean Function Synthesis [article]

Priyanka Golia, Subhajit Roy, Kuldeep S. Meel
2020 arXiv   pre-print
Motivated by the progress in machine learning, we propose Manthan, a novel data-driven approach to Boolean functional synthesis.  ...  Boolean functional synthesis is a fundamental problem in computer science with wide-ranging applications and has witnessed a surge of interest resulting in progressively improved techniques over the past  ...  This work was supported in part by National Research Foundation Singapore under its NRF Fellowship Programme[NRF-NRFFAI1-2019-0004 ] and AI Singapore Programme [AISG-RP-2018-005], and NUS ODPRT Grant [  ... 
arXiv:2005.06922v1 fatcat:mcocntrshzbq3fhys2vfuj2hdu

Four b-learning models in children's speech-language therapy

Joanna Jatkowska, Kazimierz Wielki University in Bydgoszcz
2019 e-mentor  
Synthesis Using a set of criteria, established by the student or specified by the instructor, to arrive at a reasoned judgment.  ...  The information used may be rules, principles, formulas, theories, concepts, or procedures.  ... 
doi:10.15219/em82.1440 fatcat:uboedtb5e5d2tf7zhqor5ui7o4

A New Dynamic Assessment for Multi-parameters Information of Water Inrush in Coal Mine*

Ma Jun, Zhang Yingmei
2012 Energy Procedia  
driving and other factors, in combination with hydro-geological information and monitoring data, based on BP neural network and DS theory of levels of evidence coal face integration evaluation model of  ...  It has been carried on the example analysis in the ore 3# coal bed in Shanxi, the results of prediction in line with the actual results, and different conditions for many parameters of the mine statistics  ...  theory of evidence synthesis formula as follows: The size of k reflects the degree of evidence conflict.  ... 
doi:10.1016/j.egypro.2012.01.247 fatcat:zoe4ymmt6fgwtkuncm4hqqa2um

Learning Universally Quantified Invariants of Linear Data Structures [article]

Pranav Garg, Christof Loding, P. Madhusudan, Daniel Neider
2013 arXiv   pre-print
We then give an application of these theoretically sound and efficient active learning algorithms in a passive learning framework and show that we can efficiently learn quantified linear data structure  ...  In order to express invariants in decidable logics, we invent a decidable subclass of QDAs, called elastic QDAs, and prove that every QDA has a unique minimally-over-approximating elastic QDA.  ...  In this paper, we build active learning algorithms for quantified logical formulas describing sets of linear data-structures.  ... 
arXiv:1302.2273v1 fatcat:5wcmcxzeknff5ishmjqwvlqm2i

Page 20 of Technical Book Review Index Vol. 32, Issue 1 [page]

1966 Technical Book Review Index  
Those who wish to learn how to set up and solve problems on an analog computer would be well advised to use this text in connection with an analog computer and to solve problems of increasing complexity  ...  The text grew out of a set of lecture notes prepared for a term- inal course in network theory offered at the Georgia Institute of Technology, and brings up-to-date tech- niques to bear on the problems  ... 

Paradoxes of Human Cognition

Robert DJIDJIAN
2016 Imastut'yun  
Suggested solutions could be helpful in developing further the complete teaching of human cognition.  ...  This paper presents the main paradoxes of the theory of human cognition, namely the paradoxes of epistemology and methodology.  ...  denying the use of self-referential formula (Kleen, 1952) .  ... 
doi:10.24234/wisdom.v2i7.137 fatcat:gzt4qwdgdzhhvpsma5ctzpupxy

Page 937 of Mathematical Reviews Vol. 39, Issue 4 [page]

1970 Mathematical Reviews  
In this paper the author claims that all problems concern- ing adaption or self-learning really fall within the field of probability theory, particularly within statistical decision theory.  ...  L.} 5210 rte pe rr oe self-learning, and self-adaptive Avtomat. i Telemeh. 1968, no. 1, Sieese (Russian. English summary); translated as Automat. Remote Con- trol 1968, no. 1, 83—92.  ... 

Abstract Learning Frameworks for Synthesis [chapter]

Christof Löding, P. Madhusudan, Daniel Neider
2016 Lecture Notes in Computer Science  
While studying these embeddings, we also generalize some of the synthesis problems these instances are of, resulting in new ways of looking at synthesis problems using learning.  ...  Abstract Learning Frameworks for Synthesis 2015/7/11 Abstract Learning  ...  The goal of this paper is to develop a theory of iterative learning-based synthesis through a formalism we call abstract learning frameworks for synthesis.  ... 
doi:10.1007/978-3-662-49674-9_10 fatcat:qtqyvunkjzbhbgyroiszhenfzi

ICE: A Robust Framework for Learning Invariants [chapter]

Pranav Garg, Christof Löding, P. Madhusudan, Daniel Neider
2014 Lecture Notes in Computer Science  
mechanisms for invariant synthesis.  ...  We implement these ICE-learning algorithms in a verification tool and show they are robust, practical, and efficient.  ...  This work was partially funded by NSF CAREER award #0747041 and NSF Expeditions in Computing ExCAPE Award #1138994.  ... 
doi:10.1007/978-3-319-08867-9_5 fatcat:mx6bx2cywremddw2decrainf3e
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