Certifying Correctness for Combinatorial Algorithms : by Using Pseudo-Boolean Reasoning

Over the last decades, dramatic improvements in combinatorialoptimisation algorithms have significantly impacted artificialintelligence, operations research, and other areas. These advances,however, are achieved through highly sophisticated algorithms that aredifficult to verify and prone to implementation errors that can causeincorrect results. A promising approach to detect wrong results is touse certifying algorithms that produce not only the desired output butalso a certificate or proof of correctness of the output. An externaltool can then verify the proof to determine that the given answer isvalid. In the Boolean satisfiability (SAT) community, this concept iswell established in the form of proof logging, which has become thestandard solution for generating trustworthy outputs. The problem isthat there are still some SAT solving techniques for which prooflogging is challenging and not yet used in practice. Additionally,there are many formalisms more expressive than SAT, such as constraintprogramming, various graph problems and maximum satisfiability(MaxSAT), for which efficient proof logging is out of reach forstate-of-the-art techniques.This work develops a new proof system building on the cutting planesproof system and operating on pseudo-Boolean constraints (0-1 linearinequalities). We explain how such machine-verifiable proofs can becreated for various problems, including parity reasoning, symmetry anddominance breaking, constraint programming, subgraph isomorphism andmaximum common subgraph problems, and pseudo-Boolean problems. Weimplement and evaluate the resulting algorithms and a verifier for theproof format, demonstrating that the approach is practical for a widerange of problems. We are optimistic that the proposed proof system issuitable for designing certifying variants of algorithms inpseudo-Boolean optimisation, MaxSAT and beyond

Re-Imagining Text — Re-Imagining Hermeneutics

With the advent of the digital age and new mediums of communication, it is becoming increasingly important for those interested in the interpretation of religious text to look beyond traditional ideas of text and textuality to find the sacred in unlikely places. Paul Ricoeur’s phenomenological reorientation of classical hermeneutics from romanticized notions of authorial intent and psychological divinations to a serious engagement with the “science of the text” is a hermeneutical tool that opens up an important dialogue between the interpreter, the world of the text, and the contemporary world in front of the text. This article examines three significant insights that Paul Ricoeur contributes to our expanding understanding of text. First under scrutiny will be Ricoeur’s de-regionalization of classic hermeneutics culminating in his understanding of Dasein (Being) as “being-in-the-world,” allowing mean-ing to transcend the physical boundaries of the text. Next, Ricoeur’s three-fold under-standing of traditionality/Traditions/tradition as the “chain of interpretations” through which religious language transcends the tem-poral boundary of historicity will be explored. The final section will focus on Ricoeur’s understanding of the productive imagination and metaphoric truth as the under-appreciated yet key insight around which Ricoeur’s philosophical investigation into the metaphoric transfer from text to life revolves

Cardinal\u27s The Words to Say it: The Words to Reproduce Mother

The Words to Say it, an autobiographical novel by Algerian-born Frenchwoman Marie Cardinal, earned praise for the accuracy with which it documents a classic psychoanalysis. Quickly sketched, the plot seems to suggest that the separation from an overpowering mother is effected by paternal language and phallic law—the normal, normative psychic itinerary of the human subject. In its reconsideration of the Oedipal, this essay explores Irigaray\u27s idea of the ambiguities of separation from mother and the possibility that the story of (feminine) subjectivity begins with the mother, begins with affiliation and affirmation even as it speaks of separateness. From this perspective, the protagonist\u27s cure comes about when she associates with her mother\u27s belated madness and sees it as a revolt against the phallic laws of their bourgeois class and against the colonial laws of their Algerian homeland. In the last stages of analysis, the protagonist remembers the language her mother taught her to evoke all the particularities of Algeria; this maternal tongue connects the protagonist both to mother and Motherland. The image of a nurturing Algeria calls for a re-analysis of the cultural drama of the unnamed Algerian war. If the book models an investigation of other psychic versions that challenge what otherwise might be too readily assumed as psychoanalytic law, the book also suggests re-articulating what otherwise might be too readily assumed as cultural law and order

Brand Objects for Nominal Typing

Combinations of structural and nominal object typing in systems such as Scala, Whiteoak, and Unity have focused on extending existing nominal, class-based systems with structural subtyping. The typical rules of nominal typing do not lend themselves to such an extension, resulting in major modifications. Adding object branding to an existing structural system integrates nominal and structural typing without excessively complicating the type system. We have implemented brand objects to explicitly type objects, using existing features of the structurally typed language Grace, along with a static type checker which treats the brands as nominal types. We demonstrate that the brands are useful in an existing implementation of Grace, and provide a formal model of the extension to the language

Extending the Finite Domain Solver of GNU Prolog

International audienceThis paper describes three significant extensions for the Finite Domain solver of GNU Prolog. First, the solver now supports negative integers. Second, the solver detects and prevents integer overflows from occurring. Third, the internal representation of sparse domains has been redesigned to overcome its current limitations. The preliminary performance evaluation shows a limited slowdown factor with respect to the initial solver. This factor is widely counterbalanced by the new possibilities and the robustness of the solver. Furthermore these results are preliminary and we propose some directions to limit this overhead

Beyond the Frontiers of Timeline-based Planning

Any agent, either biological or artificial, understands how to behave in its environment according to its prior knowledge and to its prior experience. The process of deciding which actions to undertake and how to perform them so as to achieve some desired objective is called deliberation. In particular, planning is an abstract and explicit deliberation process that chooses and organizes actions, by anticipating their expected outcomes, with the aim to achieve, as best as possible, some pre-stated objectives called goals. Among the most widespread approaches to automated planning, the classical approach broadly pursues to the following definition of planning: starting from a description of the initial state of the world, a description of the desired goals, and a description of a set of possible actions, the planning problem consists in synthesizing a plan, i.e., a sequence of actions, that is guaranteed, when applied to the initial state, to generate a state, called a goal state, which contains the desired goals. In order to cope with computational complexity, however, the classical approach to planning introduces some restrictive assumptions. Among them, for example, there is no explicit model of time and concurrency is treated only roughly. Additionally, goals are specified as a set of goal states, therefore, objectives such as states to be avoided and constraints on state trajectories or utility functions are not handled. In order to relax these restrictions, some alternative approaches have been proposed over the years. The timeline-based approach to planning, in particular, represents an effective alternative to classical planning for complex domains requiring the use of both temporal reasoning and scheduling features. This thesis focuses on timeline-based planning, aiming at solving some efficiency issues which inevitably raise as a consequence of the drop out of these restrictions. Regardless of the followed approach, indeed, it turns out that automated planning is a rather complex task from a computational point of view. Furthermore, not all of the approaches proposed in literature can rely on effective heuristics for efficiently tackling the search. This is particularly true in the case of the more recent and hence less investigated timeline-based formulation. Most of the timeline-based planners, in particular, have usually neglected the advantages triggered in classical planning from the use of Graphplan and/or modern heuristic search, namely the capability of reasoning on the whole domain model. This thesis aims at reducing the performance gap between the classical approach at planning and the timeline-based one. Specifically, the overall goal is to improve the efficiency of timeline-based reasoners taking inspiration from techniques applied in more classical approaches to planning. The main contributions of this thesis, therefore, are a) a new formalism for timeline-based planning which overcomes some limitations of the existing ones; b) a set of heuristics, inspired by the classical approach, that improve the performance of the timeline-based approach to planning; c) the introduction of sophisticated techniques like the non-chronological backtracking and the no-good learning, commonly used in other fields such as Constraint Processing, into the search process;d) the reorganization of the existing solver architectures, of a new solver called ORATIO, that allows to push the reasoning process beyond the sole automated planning, winking at emerging fields like, for example, Explainable AI and e) the introduction of a new language for expressing timeline-based planning problems called RIDDLE

An investigation into figurative language in the ‘LOLITA' NLP system

The classical and folk theory view on metaphor and figurative language assumes that metaphor is a rare occurrence, restricted to the realms of poetry and rhetoric. Recent results have, however, unarguably shown that figurative language of various complexity exhibits great systematicity and is pervasive in everyday language and texts. If the ubiquity of figurative language cannot be disputed, however, any natural language processing (NLP) system aiming at processing text beyond a restricted scope has to be able to deal with figurative language. This is particularly true if the processing is to be based on deep techniques, where a deep analysis of the input is performed. The LOLITA NLP system employs deep techniques and, therefore, must be capable of dealing with figurative input. The task of natural language (NL) generation is affected by the naturalness of figurative language, too. For if metaphors are frequent and natural, NL generation not capable of handling figurative language will seem restricted and its output unnatural. This thesis describes the work undertaken to examine the options for extending the LOLITA system in the direction of figurative language processing and the results of this project. The work critically examines previous approaches and their contribution to the field, before outlining a solution which follows the principles of natural language engineering