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Related papers: Non-deterministic Characterisations

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We investigate the power of non-determinism in purely functional programming languages with higher-order types. Specifically, we consider cons-free programs of varying data orders, equipped with explicit non-deterministic choice.…

Computational Complexity · Computer Science 2017-01-31 Cynthia Kop , Jakob Grue Simonsen

We investigate the power of non-determinism in purely functional programming languages with higher-order types. Specifically, we set out to characterise the hierarchy NP $\subseteq$ NEXP $\subseteq$ NEXP$^{(2)}$ $\subseteq \cdots \subseteq$…

Logic in Computer Science · Computer Science 2017-11-16 Cynthia Kop

Constructor rewriting systems are said to be cons-free if, roughly, constructor terms in the right-hand sides of rules are subterms of constructor terms in the left-hand side; the computational intuition is that rules cannot build new data…

Computational Complexity · Computer Science 2016-05-02 Cynthia Kop , Jakob Grue Simonsen

Constructor rewriting systems are said to be cons-free if, roughly, constructor terms in the right-hand sides of rules are subterms of the left-hand sides; the computational intuition is that rules cannot build new data structures. In…

Computational Complexity · Computer Science 2023-06-22 Cynthia Kop , Jakob Grue Simonsen

This thesis investigates effectful declarative programming with an emphasis on non-determinism as an effect. On the one hand, we are interested in developing applications using non-determinism as underlying implementation idea. We discuss…

Programming Languages · Computer Science 2020-06-03 Sandra Dylus

Parallelism is often required for performance. In these situations an excess of non-determinism is harmful as it means the program can have several different behaviours or even different results. Even in domains such as high-performance…

Programming Languages · Computer Science 2022-10-28 Laure Gonnord , Ludovic Henrio , Lionel Morel , Gabriel Radanne

The exponential ordering is exploited in the context of non-auto\-no\-mous delay systems, inducing monotone skew-product semiflows under less restrictive conditions than usual. Some dynamical concepts linked to the order, such as…

Dynamical Systems · Mathematics 2022-12-09 Sylvia Novo , Rafael Obaya , Ana M. Sanz , Victor M. Villarragut

We reflect on programming with complicated effects, recalling an undeservingly forgotten alternative to monadic programming and checking to see how well it can actually work in modern functional languages. We adopt and argue the position of…

Programming Languages · Computer Science 2019-05-17 Oleg Kiselyov

We study pseudodeterministic constructions, i.e., randomized algorithms which output the same solution on most computation paths. We establish unconditionally that there is an infinite sequence $\{p_n\}_{n \in \mathbb{N}}$ of increasing…

Computational Complexity · Computer Science 2016-12-07 Igor C. Oliveira , Rahul Santhanam

In this paper we consider a nondeterministic computation by deterministic multi-head 2-way automata having a read-only access to an auxiliary memory. The memory contains additional data (a guess) and computation is successful iff it is…

Computational Complexity · Computer Science 2008-11-18 M. N. Vyalyi

The use of non-deterministic functions is a distinctive feature of modern functional logic languages. The semantics commonly adopted is call-time choice, a notion that at the operational level is related to the sharing mechanism of lazy…

Programming Languages · Computer Science 2009-03-13 Francisco Javier Lopez-Fraguas , Juan Rodriguez-Hortala , Jaime Sanchez-Hernandez

Motivated by applications where impatience is pervasive and evaluation times are uncertain, we study a selection model where options may expire at an unknown point in time and evaluation times are stochastic. Initially, the decision-maker…

Optimization and Control · Mathematics 2026-02-05 Yihua Xu , Rohan Ghuge , Sebastian Perez-Salazar

Probabilistic programming languages represent complex data with intermingled models in a few lines of code. Efficient inference algorithms in probabilistic programming languages make possible to build unified frameworks to compute…

Machine Learning · Statistics 2016-07-15 Anh Tong , Jaesik Choi

We deal here with the issue of determinism versus randomness in time series. One wishes to identify their relative weights in a given time series. Two different tools have been advanced in the literature to such effect, namely, i) the…

The central open question in Descriptive Complexity is whether there is a logic that characterizes deterministic polynomial time (PTIME) on relational structures. Towards this goal, we define a logic that is obtained from first-order logic…

Logic in Computer Science · Computer Science 2021-11-16 Eugenia Ternovska

In this paper, we prove that (first-order) cons-free term rewriting with a call-by-value reduction strategy exactly characterises the class of PTIME-computable functions. We use this to give an alternative proof of the result by Carvalho…

Logic in Computer Science · Computer Science 2017-11-15 Cynthia Kop

We give extensional and intensional characterizations of functional programs with nondeterminism: as structure preserving functions between biorders, and as nondeterministic sequential algorithms on ordered concrete data structures which…

Logic in Computer Science · Computer Science 2023-06-22 James Laird

Nondeterministic choice is a useful program construct that provides a way to describe the behaviour of a program without specifying the details of possible implementations. It supports the stepwise refinement of programs, a method that has…

Logic in Computer Science · Computer Science 2023-02-17 Yuan Feng , Yingte Xu

We show that descriptive complexity's result extends in High Order Logic to capture the expressivity of Turing Machine which have a finite number of alternation and whose time or space is bounded by a finite tower of exponential. Hence we…

Logic in Computer Science · Computer Science 2014-07-16 Arthur Milchior

Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making problems in such environments. In recent years, attempts were made…

Artificial Intelligence · Computer Science 2014-01-17 Mahdi Milani Fard , Joelle Pineau
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