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Deep neural network architectures often consist of repetitive structural elements. We introduce an approach that reveals these patterns and can be broadly applied to the study of deep learning. Similarly to how a power strip helps untangle…

Statistical Mechanics · Physics 2025-07-03 Donghee Lee , Hye-Sung Lee , Jaeok Yi

We present a novel decision tree-based synthesis algorithm of ranking functions for verifying program termination. Our algorithm is integrated into the workflow of CounterExample Guided Inductive Synthesis (CEGIS). CEGIS is an iterative…

Logic in Computer Science · Computer Science 2021-07-06 Satoshi Kura , Hiroshi Unno , Ichiro Hasuo

LLMs have recently been used to generate Python programs representing generalized plans in PDDL planning, i.e., plans that generalize across the tasks of a given PDDL domain. Previous work proposed a framework consisting of three steps: the…

Artificial Intelligence · Computer Science 2026-03-23 Katharina Stein , Nils Hodel , Daniel Fišer , Jörg Hoffmann , Michael Katz , Alexander Koller

The paper proposes a new static analysis designed to handle open programs, i.e., fragments of programs, with dynamic pointer-linked data structures - in particular, various kinds of lists - that employ advanced low-level pointer operations.…

Logic in Computer Science · Computer Science 2022-05-06 Lukáš Holík , Petr Peringer , Adam Rogalewicz , Veronika Šoková , Tomáš Vojnar , Florian Zuleger

Linear Temporal Logic (LTL) is a formal way of specifying complex objectives for planning problems modeled as Markov Decision Processes (MDPs). The planning problem aims to find the optimal policy that maximizes the satisfaction probability…

Robotics · Computer Science 2024-08-13 Zetong Xuan , Yu Wang

We study expression learning problems with syntactic restrictions and introduce the class of finite-aspect checkable languages to characterize symbolic languages that admit decidable learning. The semantics of such languages can be defined…

Logic in Computer Science · Computer Science 2023-03-22 Paul Krogmeier , P. Madhusudan

Recently many first and second order variants of SGD have been proposed to facilitate training of Deep Neural Networks (DNNs). A common limitation of these works stem from the fact that they use the same learning rate across all instances…

Machine Learning · Computer Science 2021-05-31 Shreyas Saxena , Nidhi Vyas , Dennis DeCoste

We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal, recursive, and large programs and supports predicate…

Artificial Intelligence · Computer Science 2021-12-08 Andrew Cropper

Logic programming is a flexible programming paradigm due to the use of predicates without a fixed data flow. To extend logic languages with the compact notation of functional programming, there are various proposals to map evaluable…

Programming Languages · Computer Science 2022-05-17 Michael Hanus

This paper presents Tree Notation, a new simple, universal syntax. Language designers can invent new programming languages, called Tree Languages, on top of Tree Notation. Tree Languages have a number of advantages over traditional…

Programming Languages · Computer Science 2017-10-25 Breck Yunits

The lack of mathematical tractability of Deep Neural Networks (DNNs) has hindered progress towards having a unified convergence analysis of training algorithms, in the general setting. We propose a unified optimization framework for…

Machine Learning · Computer Science 2018-05-24 Hadi Ghauch , Hossein Shokri-Ghadikolaei , Carlo Fischione , Mikael Skoglund

The integration of reasoning, learning, and decision-making is key to build more general artificial intelligence systems. As a step in this direction, we propose a novel neural-logic architecture, called differentiable logic machine (DLM),…

Artificial Intelligence · Computer Science 2023-07-07 Matthieu Zimmer , Xuening Feng , Claire Glanois , Zhaohui Jiang , Jianyi Zhang , Paul Weng , Dong Li , Jianye Hao , Wulong Liu

Slicing is a program analysis technique originally developed for imperative languages. It facilitates understanding of data flow and debugging. This paper discusses slicing of Constraint Logic Programs. Constraint Logic Programming (CLP) is…

Software Engineering · Computer Science 2007-05-23 Gyongyi Szilagyi , Tibor Gyimothy , Jan Maluszynski

Temporal logics like Computation Tree Logic (CTL) have been widely used as expressive formalisms to capture rich behavioral specifications. CTL can express properties such as reachability, termination, invariants and responsiveness, which…

Software Engineering · Computer Science 2025-02-24 Yu Liu , Yahui Song , Martin Mirchev , Abhik Roychoudhury

We present a new approach to the logical design of relational databases, based on strategic port graph rewriting. We show how to model relational schemata as attributed port graphs and provide port graph rewriting rules to perform…

Databases · Computer Science 2019-02-07 János Varga

Transition systems are often used to describe the behaviour of software systems. If viewed as a graph then, at their most basic level, vertices correspond to the states of a program and each edge represents a transition between states via…

Logic in Computer Science · Computer Science 2023-03-20 Diana Costa

The general setting of this work is the constraint-based synthesis of termination arguments. We consider a restricted class of programs called lasso programs. The termination argument for a lasso program is a pair of a ranking function and…

Logic in Computer Science · Computer Science 2014-01-22 Matthias Heizmann , Jochen Hoenicke , Jan Leike , Andreas Podelski

Program termination is a hot research topic in program analysis. The last few years have witnessed the development of termination analyzers for programming languages such as C and Java with remarkable precision and performance. These…

Programming Languages · Computer Science 2013-07-26 Roberto Bagnara , Fred Mesnard

Finite linear temporal logic ($\mathsf{LTL}_f$) is a powerful formal representation for modeling temporal sequences. We address the problem of learning a compact $\mathsf{LTL}_f$ formula from labeled traces of system behavior. We propose a…

Artificial Intelligence · Computer Science 2021-11-23 Homer Walke , Daniel Ritter , Carl Trimbach , Michael Littman

In this paper, we study classes of structures and individual structures for which programs implementing functions defined everywhere are equivalent to finite tree-programs. The programs under consideration may have cycles and at most…

Logic in Computer Science · Computer Science 2025-01-06 Mikhail Moshkov