Abstract We study the fully automated amortised analysis of purely functional data structures like skew heaps , as well as weight - and rank-biased leftist heaps. For that we generalise earlier works on automated amortised resource analysis by developing a type inference based approach with a generic type system. This allows for modular reasoning and the inference of precise and optimal cost bounds. More specifically, we extend the work on the ATLAS system by Leutgeb et al. which was developed to cover the analysis of splay trees and some closely related data structures. To enable the analysis of skew heaps, however, and the even more challenging (amortised) analysis of leftist heaps, we have developed a range of new techniques for type-based automated analysis. By introducing a generic type system we allow for arbitrary (classes of) potential functions, compared to the use of hard-coded potential functions in ATLAS, which we have implemented in Haskell in an entirely modular way. We have also greatly enhanced the existing type inference algorithm by extensions in multiple directions, including path-sensitive reasoning, data structure invariants, and template parameters for piecewise defined potential functions. We show how our newly developed system supports the use of all known potential functions for analysing skew heaps and leftist heaps, confirming the known bounds.

Abstract In this paper, we provide a novel view upon reinforcement learning (RL for short). In particular, we are interested in applications of RL in use cases, where average rewards may be nonzero. While RL methodologies have been extensively researched upon, this particular application area has only received scarce attention in the Literature. In part, our motivation stems from applications in Operation Research (OR for short), where it is typically the case that rewards are profit derived. Similar use cases can be found in more general applications in economics. Based on a principled study of the mathematical background of discounted reinforcement learning we establish a novel adaptation of standard RL, dubbed Average Reward Adjusted Discounted Reinforcement Learning (ARAL for short). Our approach stems from revisiting the Laurent Series expansion of the discounted state value and a subsequent reformulation of the target function guiding the learning process. While the theoretical advance is arguably incremental, we provide ample experimental evidence that the thus obtained novel RL methodology compares favorable to well-established techniques like Q-learning or R-learning.

Abstract In this paper, we study quantitative properties of quantum programs. Properties of interest include (positive) almost-sure termination, expected runtime or expected cost, that is, for example, the expected number of applications of a given quantum gate, etc. After studying the completeness of these problems in the arithmetical hierarchy over the Clifford+T fragment of quantum mechanics, we express these problems using a variation of a quantum pre-expectation transformer, a weakest pre-condition based technique that allows to symbolically compute these quantitative properties. Under a smooth restriction—a restriction to polynomials of bounded degree over a real closed field—we show that the quantitative problem, which consists in finding an upper-bound to the pre-expectation, can be decided in time double-exponential in the size of a program, thus providing, despite its great complexity, one of the first decidable results on the analysis and verification of quantum programs. Finally, we sketch how the latter can be transformed into an efficient synthesis method.

Abstract In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with well-established methods in inductive logic programming (ILP) and rule induction to provide efficient and scalable algorithms for the classification of vast data sets. By construction, these classifications are based on the synthesis of simple rules, thus providing direct explanations of the obtained classifications. Apart from evaluating our approach on the common large scale data sets MNIST, Fashion-MNIST and IMDB, we present novel results on explainable classifications of dental bills. The latter case study stems from an industrial collaboration with Allianz Private Krankenversicherung which is an insurance company offering diverse services in Germany.

We propose, implement, and evaluate a hopping proof approach for proving expectation-based properties of probabilistic programs. Our approach combines EHL, a syntax-directed proof system for reducing proof goals of a program to proof goals of simpler programs, with a "hopping" proof rule for reducing proof goals of an original program to proof goal of a different program which is suitably related (by means of pRHL, a relational program logic for probabilistic program) to the original program. We prove that EHL is sound for a core language with procedure calls and adversarial computations, and complete for the adversary-free fragment of the language. We also provide an implementation of EHL into EasyCrypt, a proof assistant tailored for reasoning about relational properties of probabilistic programs. We provide a tight integration of EHL with other program logics supported by EasyCrypt, and in particular probabilistic Relational Hoare Logic (pRHL). Using this tight integration, we give mechanized proofs of expected complexity of in-place implementations of randomized quickselect and skip lists. We also sketch applications of our approach to cryptographic proofs and discuss the broader impact of EHL in the EasyCrypt proof assistant.

When substitutions and bindings interact, there is a risk of undesired side effects if the substitution is applied naïvely. The λ-calculus captures this phenomenon concretely, as β-reduction may require the renaming of bound variables to avoid variable capture. In this paper we introduce α-paths as an estimation for α-avoidance, roughly expressing that α-conversions are not required to prevent variable capture. These paths provide a novel method to analyse and predict the potential need for α in different calculi. In particular, we show how α-path characterises α-avoidance for several sub-calculi of the λ-calculus like (i) developments, (ii) affine/linear λ-calculi, (iii) the weak λ-calculus, (iv) μ-unfolding and (iv) finally the safe λ-calculus. Furthermore, we study the unavoidability of α-conversions in untyped and simply-typed λ-calculi and prove undecidability of the need of α-conversions for (leftmost-outermost reductions) in the untyped λ-calculus. To ease the work with α-paths, we have implemented the method and the tool is publicly available.

In this work, we study the fully automated inference of expected result values of probabilistic programs in the presence of natural programming constructs such as procedures, local variables and recursion. While crucial, capturing these constructs becomes highly non-trivial. The key contribution is the definition of a term representation, denoted as infer[.], translating a pre-expectation semantics into first-order constraints, susceptible to automation via standard methods. A crucial step is the use of logical variables, inspired by previous work on Hoare logics for recursive programs. Noteworthy, our methodology is not restricted to tail-recursion, which could unarguably be replaced by iteration and wouldn't need additional insights. We have implemented this analysis in our prototype ev-imp. We provide ample experimental evidence of the prototype's algorithmic expressibility.

Abstract In this paper, we present the first fully-automated expected amortised cost analysis of self-adjusting data structures, that is, of randomised splay trees, randomised splay heaps and randomised meldable heaps, which so far have only (semi-)manually been analysed in the literature. Our analysis is stated as a type-and-effect system for a first-order functional programming language with support for sampling over discrete distributions, non-deterministic choice and a ticking operator. The latter allows for the specification of fine-grained cost models. We state two soundness theorems based on two different—but strongly related—typing rules of ticking, which account differently for the cost of non-terminating computations. Finally we provide a prototype implementation able to fully automatically analyse the aforementioned case studies."Image missing"

We introduce a new kind of expectation transformer for a mixed classical-quantum programming language. Our semantic approach relies on a new notion of a cost structure, which we introduce and which can be seen as a specialisation of the Kegelspitzen of Keimel and Plotkin. We show that our weakest precondition analysis is both sound and adequate with respect to the operational semantics of the language. Using the induced expectation transformer, we provide formal analysis methods for the expected cost analysis and expected value analysis of classical-quantum programs. We illustrate the usefulness of our techniques by computing the expected cost of several well-known quantum algorithms and protocols, such as coin tossing, repeat until success, entangled state preparation, and quantum walks.

Abstract We introduce a novel amortised resource analysis couched in a type-and-effect system. Our analysis is formulated in terms of the physicist’s method of amortised analysis and is potentialbased. The type system makes use of logarithmic potential functions and is the first such system to exhibit logarithmic amortised complexity . With our approach, we target the automated analysis of self-adjusting data structures, like splay trees, which so far have only manually been analysed in the literature. In particular, we have implemented a semi-automated prototype, which successfully analyses the zig-zig case of splaying , once the type annotations are fixed.

Abstract Being able to argue about the performance of self-adjusting data structures such as splay trees has been a main objective, when Sleator and Tarjan introduced the notion ofamortisedcomplexity. Analysing these data structures requires sophisticated potential functions, which typically contain logarithmic expressions. Possibly for these reasons, and despite the recent progress in automated resource analysis, they have so far eluded automation. In this paper, we report on the first fully-automated amortised complexity analysis of self-adjusting data structures. Following earlier work, our analysis is based on potential function templates with unknown coefficients. We make the following contributions: 1) We encode the search for concrete potential function coefficients as an optimisation problem over a suitable constraint system. Our target function steers the search towards coefficients that minimise the inferred amortised complexity. 2) Automation is achieved by using a linear constraint system in conjunction with suitable lemmata schemes that encapsulate the required non-linear facts about the logarithm. We discuss our choices that achieve a scalable analysis. 3) We present our tool $$\mathsf {ATLAS}$$ ATLAS and report on experimental results forsplay trees,splay heapsandpairing heaps. We completely automatically infer complexity estimates that match previous results (obtained by sophisticated pen-and-paper proofs), and in some cases even infer better complexity estimates than previously published.

Delivering high quality code is a critical success factor for any software project. Thus the teaching of proper software quality assurance skills presents an important objective for educational institutions. We conducted a single-case study in a student project environment to evaluate the improvement of the quality assurance process by measures of continuous feedback and elements of gamification and also have students gain experience with these measures in an industrial-like setup. Based on our data analysis, results suggest that the software quality and also learning experience can both be improved by our proposed measures. Moreover, key findings include that gamification can serve as a strong motivational driver to developers to deal with software quality issues and also facilitate knowledge transfer, but also that sufficient effort needs to be put into balancing the reward system to achieve a long-lasting effect.

We present a novel methodology for the automated resource analysis of non-deterministic, probabilistic imperative programs, which gives rise to a modular approach . Program fragments are analysed in full independence. Moreover, the established results allow us to incorporate sampling from dynamic distributions , making our analysis applicable to a wider class of examples, for example the Coupon Collector’s problem . We have implemented our contributions in the tool , exploiting a constraint-solver over iterative refineable cost functions facilitated by off-the-shelf SMT solvers. We provide ample experimental evidence of the prototype’s algorithmic power. Our experiments show that our tool runs typically at least one order of magnitude faster than comparable tools. On more involved examples, it may even be the case that execution times of seconds become milliseconds. At the same time we retain the precision of existing tools. The extensions in applicability and the greater efficiency of our prototype, yield scalability of sorts. This effects into a wider class of examples, whose expected cost analysis can be thus be performed fully automatically.

In this tool paper we describe a variation of Nintendo’s Super Mario World dubbed Super Formula World that creates its game maps based on an input quantified Boolean formula. Thus in Super Formula World, Mario, the plumber not only saves his girlfriend princess Peach, but also acts as a QBF solver as a side. The game is implemented in Java and platform independent. Our implementation rests on abstract frameworks by Aloupis et al. that allow the analysis of the computational complexity of a variety of famous video games. In particular it is a straightforward consequence of these results to provide a reduction from QSAT to Super Mario World. By specifying this reduction in a precise way we obtain the core engine of Super Formula World. Similarly Super Formula World implements a reduction from SAT to Super Mario Bros., yielding significantly simpler game worlds.

Journal Article KBOs, ordinals, subrecursive hierarchies and all that Get access Georg Moser Georg Moser Search for other works by this author on: Oxford Academic Google Scholar Journal of Logic and Computation, Volume 27, Issue 2, March 2017, Pages 469–495, https://doi.org/10.1093/logcom/exu072 Published: 03 December 2014 Article history Received: 03 June 2013 Published: 03 December 2014

In this paper, we analyze the complexity of functional programs written in the interaction-net computation model, an asynchronous, parallel and confluent model that generalizes linear-logic proof nets. Employing user-defined sized and scheduled types, we certify concrete time, space and space-time complexity bounds for both sequential and parallel reductions of interaction-net programs by suitably assigning complexity potentials to typed nodes. The relevance of this approach is illustrated on archetypal programming examples. The provided analysis is precise, compositional and is, in theory, not restricted to particular complexity classes.

We show how the complexity of higher-order functional programs can be analysed automatically by applying program transformations to a defunctionalised versions of them, and feeding the result to existing tools for the complexity analysis of first-order term rewrite systems. This is done while carefully analysing complexity preservation and reflection of the employed transformations such that the complexity of the obtained term rewrite system reflects on the complexity of the initial program. Further, we describe suitable strategies for the application of the studied transformations and provide ample experimental data for assessing the viability of our method.

We present an elementary proof of the classical result that the leftmost outermost strategy is normalizing for left-normal orthogonal rewrite systems. Our proof is local and extends to hyper-normalization and weakly orthogonal systems. Based on the new proof, we study basic normalization, i.e., we study normalization if the set of considered starting terms is restricted to basic terms. This allows us to weaken the left-normality restriction. We show that the leftmost outermost strategy is hyper-normalizing for basically left-normal orthogonal rewrite systems. This shift of focus greatly extends the applicability of the classical result, as evidenced by the experimental data provided.

We propose a new order-theoretic characterisation of the class of polytime computable functions. To this avail we define the small polynomial path order (sPOP⁎ for short). This termination order entails a new syntactic method to analyse the innermost runtime complexity of term rewrite systems fully automatically: for any rewrite system compatible with sPOP⁎ that employs recursion up to depth d, the (innermost) runtime complexity is polynomially bounded of degree d. This bound is tight. Thus we obtain a direct correspondence between a syntactic (and easily verifiable) condition of a program and the asymptotic worst-case complexity of the program.

Interaction nets form a local and strongly confluent model of computation that is per se parallel. We introduce a Curry–Howard correspondence between well-formed interaction nets and a deep-inference deduction system based on linear logic. In particular, linear logic itself is easily expressed in the system and its computational aspects materialise though the correspondence. The system of interaction nets obtained is a typed variant of already well-known sharing graphs. Due to a strong confluence property, strong normalisation for this system follows from weak normalisation. The latter is obtained via an adaptation of Girard's reducibility method. The approach is modular, readily gives rise to generalisations (e.g. second order, known as polymorphism to the programmer) and could therefore be extended to various systems of interaction nets.

In this paper we present a combination framework for the automated polynomial complexity analysis of term rewrite systems. The framework covers both derivational and runtime complexity analysis, and is employed as theoretical foundation in the automated complexity tool TCT. We present generalisations of powerful complexity techniques, notably a generalisation of complexity pairs and (weak) dependency pairs. Finally, we also present a novel technique, called dependency graph decomposition, that in the dependency pair setting greatly increases modularity.

The Tyrolean Complexity Tool, TCT for short, is an open source complexity analyser for term rewrite systems. Our tool TCT features a majority of the known techniques for the automated characterisation of polynomial complexity of rewrite systems and can investigate derivational and runtime complexity, for full and innermost rewriting. This system description outlines features and provides a short introduction to the usage of TCT.

Commercial distributed systems such as Clouds are managed by selfish providers that strategically try to increase their revenues regardless of the utility of other providers and users. These selfish behaviors affect the efficiency of using such environments. In this paper, based on a general game theoretic truthful reverse auction mechanism, we investigate the scheduling problem of dependent tasks on distributed Cloud resources owned by selfish providers. The social cost of the game is to minimize the make span and monetary cost simultaneously. Extensive simulation experiments show that the schedules obtained are approximately Pareto optimal.

In this paper we present a new path order for rewrite systems, the exponential path order EPO*. Suppose a term rewrite system is compatible with EPO*, then the runtime complexity of this rewrite system is bounded from above by an exponential function. Furthermore, the class of function computed by a rewrite system compatible with EPO* equals the class of functions computable in exponential time on a Turing machine.

We study the complexity of rewrite systems shown terminating via the dependency pair framework using processors for reduction pairs, dependency graphs, or the subterm criterion. The complexity of such systems is bounded by a multiple recursive function, provided the complexity induced by the employed base techniques is at most multiple recursive. Moreover this upper bound is tight.

In earlier work, we have shown that for confluent TRSs, innermost polynomial runtime complexity induces polytime computability of the functions defined. In this paper, we generalise this result to full rewriting, for that we exploit graph rewriting. We give a new proof of the adequacy of graph rewriting for full rewriting that allows for a precise control of the resources copied. In sum we completely describe an implementation of rewriting on a Turing machine (TM for short). We show that the runtime complexity of the TRS and the runtime complexity of the TM is polynomially related. Our result strengthens the evidence that the complexity of a rewrite system is truthfully represented through the length of derivations. Moreover our result allows the classification of nondeterministic polytime-computation based on runtime complexity analysis of rewrite systems.

For a given (terminating) term rewriting system one can often estimate its \emph{derivational complexity} indirectly by looking at the proof method that established termination. In this spirit we investigate two instances of the interpretation method: \emph{matrix interpretations} and \emph{context dependent interpretations}. We introduce a subclass of matrix interpretations, denoted as \emph{triangular matrix interpretations}, which induce polynomial derivational complexity and establish tight correspondence results between a subclass of context dependent interpretations and restricted triangular matrix interpretations. The thus obtained new results are easy to implement and considerably extend the analytic power of existing results. We provide ample numerical data for assessing the viability of the method.