Logic Programming as Constructivism

The features of logic programming that seem unconventional from the viewpoint of classical logic can be explained in terms of constructivistic logic. We motivate and propose a constructivistic proof theory of non-Horn logic programming. Then, we apply this formalization for establishing results of practical interest. First, we show that 'stratification can be motivated in a simple and intuitive way. Relying on similar motivations, we introduce the larger classes of 'loosely stratified' and 'constructively consistent' programs. Second, we give a formal basis for introducing quantifiers into queries and logic programs by defining 'constructively domain independent* formulas. Third, we extend the Generalized Magic Sets procedure to loosely stratified and constructively consistent programs, by relying on a 'conditional fixpoini procedure

Reasoning paradigms for OWL ontologies

Representing knowledge in OWL provides two important limitations; on one hand efficient reasoning on real-world ontologies containing a large set of individuals is still a challenging task. On the other hand though OWL offers a reasonable trade-off between expressibility and decidability, it can not be used efficiently to model certain application domains. In this paper we give an overview of some of the most relevant approaches in this domain and present OWL2Jess, which is a comprehensive converter tool enabling Jess reasoning over OWL ontologies

Algebraic Stream Processing

We identify and analyse the typically higher-order approaches to stream processing in the literature. From this analysis we motivate an alternative approach to the specification of SPSs as STs based on an essentially first-order equational representation. This technique is called Cartesian form specification. More specifically, while STs are properly second-order objects we show that using Cartesian forms, the second-order models needed to formalise STs are so weak that we may use and develop well-understood first-order methods from computability theory and mathematical logic to reason about their properties. Indeed, we show that by specifying STs equationally in Cartesian form as primitive recursive functions we have the basis of a new, general purpose and mathematically sound theory of stream processing that emphasises the formal specification and formal verification of STs. The main topics that we address in the development of this theory are as follows. We present a theoretically well-founded general purpose stream processing language ASTRAL (Algebraic Stream TRAnsformer Language) that supports the use of modular specification techniques for full second-order STs. We show how ASTRAL specifications can be given a Cartesian form semantics using the language PREQ that is an equational characterisation of the primitive recursive functions. In more detail, we show that by compiling ASTRAL specifications into an equivalent Cartesian form in PREQ we can use first-order equational logic with induction as a logical calculus to reason about STs. In particular, using this calculus we identify a syntactic class of correctness statements for which the verification of ASTRAL programmes is decidable relative to this calculus. We define an effective algorithm based on term re-writing techniques to implement this calculus and hence to automatically verify a very broad class of STs including conventional hardware devices. Finally, we analyse the properties of this abstract algorithm as a proof assistant and discuss various techniques that have been adopted to develop software tools based on this algorithm

Derivation of logic programs

Imperial Users onl

Workshop on Database Programming Languages

These are the revised proceedings of the Workshop on Database Programming Languages held at Roscoff, Finistère, France in September of 1987. The last few years have seen an enormous activity in the development of new programming languages and new programming environments for databases. The purpose of the workshop was to bring together researchers from both databases and programming languages to discuss recent developments in the two areas in the hope of overcoming some of the obstacles that appear to prevent the construction of a uniform database programming environment. The workshop, which follows a previous workshop held in Appin, Scotland in 1985, was extremely successful. The organizers were delighted with both the quality and volume of the submissions for this meeting, and it was regrettable that more papers could not be accepted. Both the stimulating discussions and the excellent food and scenery of the Brittany coast made the meeting thoroughly enjoyable. There were three main foci for this workshop: the type systems suitable for databases (especially object-oriented and complex-object databases,) the representation and manipulation of persistent structures, and extensions to deductive databases that allow for more general and flexible programming. Many of the papers describe recent results, or work in progress, and are indicative of the latest research trends in database programming languages. The organizers are extremely grateful for the financial support given by CRAI (Italy), Altaïr (France) and AT&T (USA). We would also like to acknowledge the organizational help provided by Florence Deshors, Hélène Gans and Pauline Turcaud of Altaïr, and by Karen Carter of the University of Pennsylvania

On Differentiable Interpreters

Neural networks have transformed the fields of Machine Learning and Artificial Intelligence with the ability to model complex features and behaviours from raw data. They quickly became instrumental models, achieving numerous state-of-the-art performances across many tasks and domains. Yet the successes of these models often rely on large amounts of data. When data is scarce, resourceful ways of using background knowledge often help. However, though different types of background knowledge can be used to bias the model, it is not clear how one can use algorithmic knowledge to that extent. In this thesis, we present differentiable interpreters as an effective framework for utilising algorithmic background knowledge as architectural inductive biases of neural networks. By continuously approximating discrete elements of traditional program interpreters, we create differentiable interpreters that, due to the continuous nature of their execution, are amenable to optimisation with gradient descent methods. This enables us to write code mixed with parametric functions, where the code strongly biases the behaviour of the model while enabling the training of parameters and/or input representations from data. We investigate two such differentiable interpreters and their use cases in this thesis. First, we present a detailed construction of ∂4, a differentiable interpreter for the programming language FORTH. We demonstrate the ability of ∂4 to strongly bias neural models with incomplete programs of variable complexity while learning missing pieces of the program with parametrised neural networks. Such models can learn to solve tasks and strongly generalise to out-of-distribution data from small datasets. Second, we present greedy Neural Theorem Provers (gNTPs), a significant improvement of a differentiable Datalog interpreter NTP. gNTPs ameliorate the large computational cost of recursive differentiable interpretation, achieving drastic time and memory speedups while introducing soft reasoning over logic knowledge and natural language