In this paper, we extend Jones’ result—that cons-free programming with kth-order data and a callby-value strategy characterises EXPTIME—to a more general setting, including pattern-matching and non-deterministic choice. We show that the addition of non-determinism is unexpectedly powerful in the higher-order setting. Nevertheless, we can obtain a non-deterministic parallel to Jones’ hierarchy r...

This paper is about better engineering of cyber-physical systems (CPSs) through better models. Deterministic models have historically proven extremely useful and arguably form the kingpin of the industrial revolution and the digital and information technology revolutions. Key deterministic models that have proven successful include differential equations, synchronous digital logic and single-th...

We show that the traces of recently introduced dynamic programming algorithms for #SAT can be used to construct structured deterministic DNNF (decomposable negation normal form) representations of propositional formulas in CNF (conjunctive normal form). This allows us prove new upper bounds on the complexity of compiling CNF formulas into structured deterministic DNNFs in terms of parameters su...

Although Plotkin’s parallel-or is inherently deterministic, it has a non-deterministic interpretation in games based on (prime) event structures – in which an event has a unique causal history – because they do not directly support disjunctive causality. General event structures can express disjunctive causality and have a more permissive notion of determinism, but do not support hiding. We sho...

We consider the problem of planning in complex domains where actions are stochastic, non-instantaneous, may occur concurrently, and time is represented explicitly. Our approach is based on the situation calculus based language Golog. Instead of general search for a sequence of actions, as in classical planning, we consider the problem of computing a deterministic, sequential program (with stoch...

In this paper, based on uncertainty theory, a new class of two-stage uncertain programming model is presented firstly, and its deterministic equivalent programming problem is also characterized. Then some fundamental properties of the two-stage uncertain programming problem, including the convexity of feasible set as well as objective function, are investigated. Since the complexity of programm...

The traffic assignment problem is one of the most important problems for analyzing and optimizing the transportation network to find optimal flows. This study presented a new formulation based on a generalized Benders' decomposition approach to solve its important part, i.e. user equilibrium problems, in deterministic and stochastic cases. The new approach decomposed the problem into a master p...

Stochastic approximation methods for variational inference have recently gained popularity in the probabilistic programming community since these are amenable to automation and allow online, scalable, universal approximate Bayesian inference. Unfortunately, common Probabilistic Programming Languages (PPLs) with stochastic engines lack efficiency of message passing-based algorithms deterministic...

Capacity uncertainty is a common issue in the transportation planning field. However, few studies discuss the intermodal routing problem with service capacity uncertainty. Based on our previous study on the intermodal routing under deterministic capacity consideration, we systematically explore how service capacity uncertainty influences the intermodal routing decision. First of all, we adopt t...

In the oil supply chain, the refined petroleum products are transported by various transportation modes, such as rail, road, vessel and pipeline. The latter provides one of the safest and cheapest ways to connect production areas to local markets. This paper addresses the operational scheduling of a multi-product tree-like pipeline connecting several refineries to multiple distribution centers ...