A critical analysis of string APIs: The case of Pharo

International audienceMost programming languages, besides C, provide a native abstraction for character strings, but string APIs vary widely in size, expressiveness, and subjective convenience across languages. In Pharo, while at first glance the API of the String class seems rich, it often feels cumbersome in practice; to improve its usability, we faced the challenge of assessing its design. However, we found hardly any guideline about design forces and how they structure the design space, and no comprehensive analysis of the expected string operations and their different variations. In this article, we first analyse the Pharo 4 String library, then contrast it with its Haskell, Java, Python, Ruby, and Rust counterparts. We harvest criteria to describe a string API, and reflect on features and design tensions. This analysis should help language designers in understanding the design space of strings, and will serve as a basis for a future redesign of the string library in Pharo

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Students´ language in computer-assisted tutoring of mathematical proofs

Truth and proof are central to mathematics. Proving (or disproving) seemingly simple statements often turns out to be one of the hardest mathematical tasks. Yet, doing proofs is rarely taught in the classroom. Studies on cognitive difficulties in learning to do proofs have shown that pupils and students not only often do not understand or cannot apply basic formal reasoning techniques and do not know how to use formal mathematical language, but, at a far more fundamental level, they also do not understand what it means to prove a statement or even do not see the purpose of proof at all. Since insight into the importance of proof and doing proofs as such cannot be learnt other than by practice, learning support through individualised tutoring is in demand. This volume presents a part of an interdisciplinary project, set at the intersection of pedagogical science, artificial intelligence, and (computational) linguistics, which investigated issues involved in provisioning computer-based tutoring of mathematical proofs through dialogue in natural language. The ultimate goal in this context, addressing the above-mentioned need for learning support, is to build intelligent automated tutoring systems for mathematical proofs. The research presented here has been focused on the language that students use while interacting with such a system: its linguistic propeties and computational modelling. Contribution is made at three levels: first, an analysis of language phenomena found in students´ input to a (simulated) proof tutoring system is conducted and the variety of students´ verbalisations is quantitatively assessed, second, a general computational processing strategy for informal mathematical language and methods of modelling prominent language phenomena are proposed, and third, the prospects for natural language as an input modality for proof tutoring systems is evaluated based on collected corpora

Artist-Programmers and Programming Languages for the Arts

We consider the artist-programmer, who creates work through its description as source code. The artist-programmer grandstands computer language, giving unique vantage over human-computer interaction in a creative context. We focus on the human in this relationship, noting that humans use an amalgam of language and gesture to express themselves. Accordingly we expose the deep relationship between computer languages and continuous expression, examining how these realms may support one another, and how the artist-programmer may fully engage with both. Our argument takes us up through layers of representation, starting with symbols, then words, language and notation, to consider the role that these representations may play in human creativity. We form a cross-disciplinary perspective from psychology, computer science, linguistics, human-computer interaction, computational creativity, music technology and the arts. We develop and demonstrate the potential of this view to inform arts practice, through the practical introduction of software prototypes, artworks, programming languages and improvised performances. In particular, we introduce works which demonstrate the role of perception in symbolic semantics, embed the representation of time in programming language, include visuospatial arrangement in syntax, and embed the activity of programming in the improvisation and experience of art

Aspects of Qualitative Consciousness: A Computer Science Perspective

The domain of artificial intelligence (AI) has been characterised by John Searle [Sear84] by distinguishing between iveak AI, according to which computers are useful tools for studying mind, and strong AI, according to which an equivalence is made between mind and programs such that computers executing programs actually possess minds. This dissertation explores a third alternative, namely: the prospects and promise of m ild AI, according to which a suitable computer is capable of possessing species of mentality that may differ from or be weaker than ordinary human mentality, but qualify as “mentality” nonetheless. The purpose of this dissertation is to explore the prospects and promise of mild AI. The approach adopted explores whether mind can be replicated, as opposed to merely simulated, in digital machines. This requires a definition of mind in order to judge success. James Fetzer [Fetz90] has suggested minds can be defined as sign using systems in the sense of Charles Peirce’s semiotic (theory of signs) and, on this basis, argues convincingly against strong AI. Determining if his negative conclusion applies to mild AI requires rejoining Fetzer’s analysis of the analogical argument for strong AI and redressing his laws of human beings and digital machines. This is tackled by focusing on the nature and form of the operational relationship between the physical machine and mind, and suggesting some operational requirements for a minimal semiotic system independently of any underlying physical implementation. This involves four steps. Firstly, as a formal foundation, a characterisation of systems is developed in terms of the causal structure and ontological levels in the system, where an ontological level is individuated by the laws that are in effect. This is in contrast to levels of organisation, such as levels of software abstraction. This exploration suggests the necessity — as a matter of natural law — for a mediating level between the physical machine and mind that is or, at least, appears to be necessary for producing forms of mentality. The lawful structure that appears to be required within this level and between levels is examined with respect to the prospects for implementing a semiotic system. Secondly, how a system can operate in terms of semiotic processes based on a network of instantiated dispositions is explored. These are modelled as the temporal counterparts of state-transitions and stationary-representations, which are termed causal-flows and temporal-representations, respectively. They highlight the varying interactive structure of temporal patterns of causal activity in time. For the purposes of replicating mind, preserving the causal-flow structure of mental processes arises as an important requirement. Thirdly, the system structure sufficient for generating consciousness is explored — a necessary condition for a cognitive semiotic system. This suggests a requirement relating to the causal accessibility of the contents of consciousness. This structuring is driven by the system’s need to signify reality by categorising these aspects as operational entities upon which decisions can be made. Consciousness arises through the manner in which the signified reality is generated. This makes mind and consciousness the result of a co-ordinated occurrent system wide activity. Fourthly, in a mathematical sense, brains and computers can be classified as types of numeric and symbolic systems, respectively. These systems are compared and conditions formulated under which they may give rise to equivalent ontological levels. Peirce’s triadic sign relation is analysed in terms of ontological levels and the results used to clarify the nature of the ground relation in machine forms of mentality. According to the theorems developed, the introduction of a dispositional mediating level might effectively enable a suitable computer to replicate species of mentality. An important factor in determining whether a computer is suitable for this purpose is its performance capacity and thus some estimates are calculated in this respect. It is shown how these requirements, along with a number of others, can help in the development of semiotic systems and variants, such as the iconic state machine of Igor Aleksander [Alek96]

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 23rd International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 31 regular papers presented in this volume were carefully reviewed and selected from 98 submissions. The papers cover topics such as categorical models and logics; language theory, automata, and games; modal, spatial, and temporal logics; type theory and proof theory; concurrency theory and process calculi; rewriting theory; semantics of programming languages; program analysis, correctness, transformation, and verification; logics of programming; software specification and refinement; models of concurrent, reactive, stochastic, distributed, hybrid, and mobile systems; emerging models of computation; logical aspects of computational complexity; models of software security; and logical foundations of data bases.

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 23rd International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 31 regular papers presented in this volume were carefully reviewed and selected from 98 submissions. The papers cover topics such as categorical models and logics; language theory, automata, and games; modal, spatial, and temporal logics; type theory and proof theory; concurrency theory and process calculi; rewriting theory; semantics of programming languages; program analysis, correctness, transformation, and verification; logics of programming; software specification and refinement; models of concurrent, reactive, stochastic, distributed, hybrid, and mobile systems; emerging models of computation; logical aspects of computational complexity; models of software security; and logical foundations of data bases.