Tabling with Sound Answer Subsumption

Tabling is a powerful resolution mechanism for logic programs that captures their least fixed point semantics more faithfully than plain Prolog. In many tabling applications, we are not interested in the set of all answers to a goal, but only require an aggregation of those answers. Several works have studied efficient techniques, such as lattice-based answer subsumption and mode-directed tabling, to do so for various forms of aggregation. While much attention has been paid to expressivity and efficient implementation of the different approaches, soundness has not been considered. This paper shows that the different implementations indeed fail to produce least fixed points for some programs. As a remedy, we provide a formal framework that generalises the existing approaches and we establish a soundness criterion that explains for which programs the approach is sound. This article is under consideration for acceptance in TPLP.Comment: Paper presented at the 32nd International Conference on Logic Programming (ICLP 2016), New York City, USA, 16-21 October 2016, 15 pages, LaTeX, 0 PDF figure

Algebras for weighted search

Weighted search is an essential component of many fundamental and useful algorithms. Despite this, it is relatively under explored as a computational effect, receiving not nearly as much attention as either depth- or breadth-first search. This paper explores the algebraic underpinning of weighted search, and demonstrates how to implement it as a monad transformer. The development first explores breadth-first search, which can be expressed as a polynomial over semirings. These polynomials are generalised to the free semi module monad to capture a wide range of applications, including probability monads, polynomial monads, and monads for weighted search. Finally, a monad trans-former based on the free semi module monad is introduced. Applying optimisations to this type yields an implementation of pairing heaps, which is then used to implement Dijkstra’s algorithm and efficient probabilistic sampling. The construction is formalised in Cubical Agda and implemented in Haskell

Mechanizing Abstract Interpretation

It is important when developing software to verify the absence of undesirable behavior such as crashes, bugs and security vulnerabilities. Some settings require high assurance in verification results, e.g., for embedded software in automobiles or airplanes. To achieve high assurance in these verification results, formal methods are used to automatically construct or check proofs of their correctness. However, achieving high assurance for program analysis results is challenging, and current methods are ill suited for both complex critical domains and mainstream use. To verify the correctness of software we consider program analyzers---automated tools which detect software defects---and to achieve high assurance in verification results we consider mechanized verification---a rigorous process for establishing the correctness of program analyzers via computer-checked proofs. The key challenges to designing verified program analyzers are: (1) achieving an analyzer design for a given programming language and correctness property; (2) achieving an implementation for the design; and (3) achieving a mechanized verification that the implementation is correct w.r.t. the design. The state of the art in (1) and (2) is to use abstract interpretation: a guiding mathematical framework for systematically constructing analyzers directly from programming language semantics. However, achieving (3) in the presence of abstract interpretation has remained an open problem since the late 1990's. Furthermore, even the state-of-the art which achieves (3) in the absence of abstract interpretation suffers from the inability to be reused in the presence of new analyzer designs or programming language features. First, we solve the open problem which has prevented the combination of abstract interpretation (and in particular, calculational abstract interpretation) with mechanized verification, which advances the state of the art in designing, implementing, and verifying analyzers for critical software. We do this through a new mathematical framework Constructive Galois Connections which supports synthesizing specifications for program analyzers, calculating implementations from these induced specifications, and is amenable to mechanized verification. Finally, we introduce reusable components for implementing analyzers for a wide range of designs and semantics. We do this though two new frameworks Galois Transformers and Definitional Abstract Interpreters. These frameworks tightly couple analyzer design decisions, implementation fragments, and verification properties into compositional components which are (target) programming-language independent and amenable to mechanized verification. Variations in the analysis design are then recovered by simply re-assembling the combination of components. Using this framework, sophisticated program analyzers can be assembled by non-experts, and the result are guaranteed to be verified by construction

Fixing non-determinism

Non-deterministic computations are conventionally modelled by lists of their outcomes. This approach provides a concise declarative description of certain problems, as well as a way of generically solving such problems. However, the traditional approach falls short when the non-deterministic problem is allowed to be recursive: the recursive problem may have infinitely many outcomes, giving rise to an infinite list. Yet there are usually only finitely many distinct relevant results. This paper shows that this set of interesting results corresponds to a least fixed point. We provide an implementation based on algebraic effect handlers to compute such least fixed points in a finite amount of time, thereby allowing non-determinism and recursion to meaningfully co-occur in a single program.status: Published onlin