197,629 research outputs found

    Reasoning About Multi-stage Programs

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    Multi-stage programming (MSP) is a style of writing program generators---programs which generate programs---supported by special annotations that direct construction, combination, and execution of object programs. Various researchers have shown MSP to be effective in writing efficient programs without sacrificing genericity. However, correctness proofs of such programs have so far received limited attention, and approaches and challenges for that task have been largely unexplored. In this thesis, I establish formal equational properties of the multi-stage lambda calculus and related proof techniques, as well as results that delineate the intricacies of multi-stage languages that one must be aware of. In particular, I settle three basic questions that naturally arise when verifying multi-stage functional programs. Firstly, can adding staging MSP to a language compromise the interchangeability of terms that held in the original language? Unfortunately it can, and more care is needed to reason about terms with free variables. Secondly, staging annotations, as the term ``annotations'' suggests, are often thought to be orthogonal to the behavior of a program, but when is this formally guaranteed to be the case? I give termination conditions that characterize when this guarantee holds. Finally, do multi-stage languages satisfy extensional facts, for example that functions agreeing on all arguments are equivalent? I develop a sound and complete notion of applicative bisimulation, which can establish not only extensionality but, in principle, any other valid program equivalence as well. These results improve our general understanding of staging and enable us to prove the correctness of complicated multi-stage programs

    Does modularity undermine the pro‐emotion consensus?

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    There is a growing consensus that emotions contribute positively to human practical rationality. While arguments that defend this position often appeal to the modularity of emotion-generation mechanisms, these arguments are also susceptible to the criticism, e.g. by Jones (2006), that emotional modularity supports pessimism about the prospects of emotions contributing positively to practical rationality here and now. This paper aims to respond to this criticism by demonstrating how models of emotion processing can accommodate the sorts of cognitive influence required to make the pro-emotion position plausible whilst exhibiting key elements of modularity

    Learning programs by learning from failures

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    We describe an inductive logic programming (ILP) approach called learning from failures. In this approach, an ILP system (the learner) decomposes the learning problem into three separate stages: generate, test, and constrain. In the generate stage, the learner generates a hypothesis (a logic program) that satisfies a set of hypothesis constraints (constraints on the syntactic form of hypotheses). In the test stage, the learner tests the hypothesis against training examples. A hypothesis fails when it does not entail all the positive examples or entails a negative example. If a hypothesis fails, then, in the constrain stage, the learner learns constraints from the failed hypothesis to prune the hypothesis space, i.e. to constrain subsequent hypothesis generation. For instance, if a hypothesis is too general (entails a negative example), the constraints prune generalisations of the hypothesis. If a hypothesis is too specific (does not entail all the positive examples), the constraints prune specialisations of the hypothesis. This loop repeats until either (i) the learner finds a hypothesis that entails all the positive and none of the negative examples, or (ii) there are no more hypotheses to test. We introduce Popper, an ILP system that implements this approach by combining answer set programming and Prolog. Popper supports infinite problem domains, reasoning about lists and numbers, learning textually minimal programs, and learning recursive programs. Our experimental results on three domains (toy game problems, robot strategies, and list transformations) show that (i) constraints drastically improve learning performance, and (ii) Popper can outperform existing ILP systems, both in terms of predictive accuracies and learning times.Comment: Accepted for the machine learning journa

    Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

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    Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

    The Design of the Fifth Answer Set Programming Competition

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    Answer Set Programming (ASP) is a well-established paradigm of declarative programming that has been developed in the field of logic programming and nonmonotonic reasoning. Advances in ASP solving technology are customarily assessed in competition events, as it happens for other closely-related problem-solving technologies like SAT/SMT, QBF, Planning and Scheduling. ASP Competitions are (usually) biennial events; however, the Fifth ASP Competition departs from tradition, in order to join the FLoC Olympic Games at the Vienna Summer of Logic 2014, which is expected to be the largest event in the history of logic. This edition of the ASP Competition series is jointly organized by the University of Calabria (Italy), the Aalto University (Finland), and the University of Genova (Italy), and is affiliated with the 30th International Conference on Logic Programming (ICLP 2014). It features a completely re-designed setup, with novelties involving the design of tracks, the scoring schema, and the adherence to a fixed modeling language in order to push the adoption of the ASP-Core-2 standard. Benchmark domains are taken from past editions, and best system packages submitted in 2013 are compared with new versions and solvers. To appear in Theory and Practice of Logic Programming (TPLP).Comment: 10 page
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