More Like this

1. SwitchLog: A Logic Programming Language for Network Switches

   https://doi.org/10.1007/978-3-031-24841-2_12  

   Mehta, Vaibhav; Loehr, Devon; Sonchack, John; Walker, David (January 2023, Practical Aspects of Declarative Languages)

   Hanus, Michael; Inclezan, Daniela (Ed.)

   The development of programmable switches such as the Intel Tofino has allowed network designers to implement a wide range of new in-network applications and network control logic. However, current switch programming languages, like P4, operate at a very low level of abstraction. This paper introduces SwitchLog, a new experimental logic programming language designed to lift the level of abstraction at which network programmers operate, while remaining amenable to efficient implementation on programmable switches. SwitchLog is inspired by previous distributed logic programming languages such as NDLog, in which programmers declare a series of facts, each located at a particular switch in the network. Logic programming rules that operate on facts at different locations implicitly generate network communication, and are updated incrementally, as packets pass through a switch. In order to ensure these updates can be implemented efficiently on switch hardware, SwitchLog imposes several restrictions on the way programmers can craft their rules. We demonstrate that SwitchLog can be used to express a variety of networking applications in a mere handful of lines of code. 

   more » « less
2. Integrating Logic Rules with Everything Else, Seamlessly

   https://doi.org/10.1017/S1471068423000108  

   LIU, YANHONG A; STOLLER, SCOTT D; TONG, YI; LIN, BO (July 2023, Theory and Practice of Logic Programming)

   This paper presents a language, Alda, that supports all of logic rules, sets, functions, updates, and objects as seamlessly integrated built-ins. The key idea is to support predicates in rules as set-valued variables that can be used and updated in any scope, and support queries using rules as either explicit or implicit automatic calls to an inference function. We have defined a formal semantics of the language, implemented a prototype compiler that builds on an object-oriented language that supports concurrent and distributed programming and on an efficient logic rule system, and successfully used the language and implementation on benchmarks and problems from a wide variety of application domains. We describe the compilation method and results of experimental evaluation. 

   more » « less
3. Deep Learning Methods for Sign Language Translation

   https://doi.org/10.1145/3477498  

   Ananthanarayana, Tejaswini; Srivastava, Priyanshu; Chintha, Akash; Santha, Akhil; Landy, Brian; Panaro, Joseph; Webster, Andre; Kotecha, Nikunj; Sah, Shagan; Sarchet, Thomastine; et al (December 2021, ACM Transactions on Accessible Computing)

   Many sign languages are bona fide natural languages with grammatical rules and lexicons hence can benefit from machine translation methods. Similarly, since sign language is a visual-spatial language, it can also benefit from computer vision methods for encoding it. With the advent of deep learning methods in recent years, significant advances have been made in natural language processing (specifically neural machine translation) and in computer vision methods (specifically image and video captioning). Researchers have therefore begun expanding these learning methods to sign language understanding. Sign language interpretation is especially challenging, because it involves a continuous visual-spatial modality where meaning is often derived based on context. The focus of this article, therefore, is to examine various deep learning–based methods for encoding sign language as inputs, and to analyze the efficacy of several machine translation methods, over three different sign language datasets. The goal is to determine which combinations are sufficiently robust for sign language translation without any gloss-based information. To understand the role of the different input features, we perform ablation studies over the model architectures (input features + neural translation models) for improved continuous sign language translation. These input features include body and finger joints, facial points, as well as vector representations/embeddings from convolutional neural networks. The machine translation models explored include several baseline sequence-to-sequence approaches, more complex and challenging networks using attention, reinforcement learning, and the transformer model. We implement the translation methods over multiple sign languages—German (GSL), American (ASL), and Chinese sign languages (CSL). From our analysis, the transformer model combined with input embeddings from ResNet50 or pose-based landmark features outperformed all the other sequence-to-sequence models by achieving higher BLEU2-BLEU4 scores when applied to the controlled and constrained GSL benchmark dataset. These combinations also showed significant promise on the other less controlled ASL and CSL datasets. 

   more » « less
4. Translating LPOD and CR-Prolog ₂ into standard answer set programs

   https://doi.org/10.1017/S1471068418000315  

   LEE, JOOHYUNG; YANG, ZHUN (July 2018, Theory and Practice of Logic Programming)

   Abstract Logic Programs with Ordered Disjunction (LPOD) is an extension of standard answer set programs to handle preference using the construct of ordered disjunction, and CR-Prolog 2 is an extension of standard answer set programs with consistency restoring rules and LPOD-like ordered disjunction. We present reductions of each of these languages into the standard ASP language, which gives us an alternative way to understand the extensions in terms of the standard ASP language. 

   more » « less
5. Finite-Choice Logic Programming

   https://doi.org/10.1145/3704849  

   Martens, Chris; Simmons, Robert J; Arntzenius, Michael (January 2025, Proceedings of the ACM on Programming Languages)

   Hicks, Michael (Ed.)

   Logic programming, as exemplified by datalog, defines the meaning of a program as its unique smallest model: the deductive closure of its inference rules. However, many problems call for an enumeration of models that vary along some set of choices while maintaining structural and logical constraints—there is no single canonical model. The notion of stable models for logic programs with negation has successfully captured programmer intuition about the set of valid solutions for such problems, giving rise to a family of programming languages and associated solvers known as answer set programming. Unfortunately, the definition of a stable model is frustratingly indirect, especially in the presence of rules containing free variables. We propose a new formalism, finite-choice logic programming, that uses choice, not negation, to admit multiple solutions. Finite-choice logic programming contains all the expressive power of the stable model semantics, gives meaning to a new and useful class of programs, and enjoys a least-fixed-point interpretation over a novel domain. We present an algorithm for exploring the solution space and prove it correct with respect to our semantics. Our implementation, the Dusa logic programming language, has performance that compares favorably with state-of-the-art answer set solvers and exhibits more predictable scaling with problem size. 

   more » « less