Dependent Object Types

A scalable programming language is one in which the same concepts can describe small as well as large parts. Towards this goal, Scala unifies concepts from object and module systems. In particular, objects can contain type members, which can be selected as types, called path-dependent types. Focusing on path-dependent types, we develop a type-theoretic foundation for Scala: the calculus of Dependent Object Types (DOT). We derive DOT from System F, we add a lower bound to each type variable, in addition to its usual upper bound, (2) in System D, we turn each type variable into a regular term variable containing a type, (3) for a full subtyping lattice, we add intersection and union types, (4) for objects, we consolidate all values into records, (5) for objects that close over a self, we introduce a recursive type, binding a self term variable, (6) for recursive types, we first extend the theory in typing and then also in subtyping. Through this bottom-up exploration, we discover a sound, uniform yet powerful design for DOT. We devise strategies and techniques for proving soundness that scale through this iterative step-by-step process: (1) "pushback" of subtyping transitivity or subsumption, to concisely capture inversion of subtyping or typing, (2) distinction between concrete vs. abstract context variables, to resolve tension between preservation of types vs. preservation of type abstractions, (3) and, specifically for big-step semantics, a type that closes over an environment, to relate context-dependent types across closures. While ultimately, we have developed sound models of DOT in both big-step and small-step operational semantics, historically, the shift to big-step semantics has been helpful in focusing the requirements. In particular, by developing a novel big-step soundness proof for System F<:, calculi like System D<: emerge as straightforward generalizations, almost like removing artificial restrictions. Interesting in their own right, our type soundness techniques for definitional interpreters extend to mutable references without use of co-induction. The DOT calculus finally grounds languages like Scala in firm theory. The DOT calculus helps in finding bugs in Scala, and in understanding feature interaction better as well as requirements. The DOT calculus serves as a good basis for future work which studies extensions or encodings on top of the core, bridging the gap from DOT to Dotty / Scala

A Programming System for End-user Functional Programming

This research involves the construction of a programming system, HASKEU, to support end-user programming in a purely functional programming language. An end-user programmer is someone who may program a computer to get their job done, but has no interest in becoming a computer programmer. A purely functional programming language is one that does not require the expression of statement sequencing or variable updating. The end-user is offered two views of their functional program. The primary view is a visual one, in which the program is presented as a collection of boxes (representing processes) and lines (representing data flow). The secondary view is a textual one, in which the program is presented as a collection of written function definitions. It is expected that the end-user programmer will begin with the visual view, perhaps later moving on to the textual view. The task of the programming system is to ensure that the visual and textual views are kept consistent as the program is constructed. The foundation of the programming system is a implementation of the Model-View-Controller (MVC) design pattern as a reactive program using the elegant Functional Reactive Programming (FRP) framework. Human-Computer Interaction (HCI) principles and methods are considered in all design decisions. A usabilty study was made to find out the effectiveness of the new system

Achieving Self-Sustainability in Interactive Graphical Programming Systems

Programming is fraught with accidental complexity. Software, including tools used for programming, is inflexible and hard to adapt to one's specific problem context. Programming tools do not support Notational Freedom, so programmers must waste cognitive effort expressing ideas in suboptimal notations. They must also work around problems caused by a reliance on plain text representations instead of Explicit Structure. The idea of a Self-Sustainable programming system, open to adaptation by its users, promises a way out of these accidental complexities. However, the principles underlying such a property are poorly documented, as are methods for practically achieving it in harmony with Notational Freedom and Explicit Structure. We trace the causes of this difficulty and use them to inform our construction of a prototype self-sustainable system. By carefully reflecting on the steps involved in our specific case, we provide insight into how self-sustainability can be achieved in general, and thus how a motivated programmer can escape the aforementioned sources of accidental complexity

Algorithmic debugging for complex lazy functional programs

An algorithmic debugger finds defects in programs by systematic search. It relies on the programmer to direct the search by answering a series of yes/no questions about the correctness of specific function applications and their results. Existing algorithmic debuggers for a lazy functional language work well for small simple programs but cannot be used to locate defects in complex programs for two reasons: Firstly, to collect the information required for algorithmic debugging existing debuggers use different but complex implementations. Therefore, these debuggers are hard to maintain and do not support all the latest language features. As a consequence, programs with unsupported language features cannot be debugged. Also inclusion of a library using unsupported languages features can make algorithmic debugging unusable even when the programmer is not interested in debugging the library. Secondly, algorithmic debugging breaks down when the size or number of questions is too great for the programmer to handle. This is a pity, because, even though algorithmic debugging is a promising method for locating defects, many real-world programs are too complex for the method to be usuable. I claim that the techniques in in this thesis make algorithmic debugging useable for a much more complex lazy functional programs. I present a novel method for collecting the information required for algorithmically debugging a lazy functional program. The method is non-invasive, uses program annotations in suspected modules only and has a simple implementation. My method supports all of Haskell, including laziness, higher-order functions and exceptions. Future language extensions can be supported without changes, or with minimal changes, to the implementation of the debugger. With my method the programmer can focus on untrusted code -- lots of trusted libraries are unaffected. This makes traces, and hence the amount of questions that needs to be answered, more manageable. I give a type-generic definition to support custom types defined by the programmer. Furthermore, I propose a method that re-uses properties to answer automatically some of the questions arising during algorithmic debugging, and to replace others by simpler questions. Properties may already be present in the code for testing; the programmer can also encode a specification or reference implementation as a property, or add a new property in response to a statement they are asked to judge

Theoretical and Practical Aspects of Typestate

The modelling and enforcement of typestate constraints in object oriented languages has the potential to eliminate a variety of common and difficult to diagnose errors. While the theoretical foundations of typestate are well established in the literature, less attention has been paid to the practical aspects: is the additional complexity justifiable? Can typestate be reasoned about effectively by "real" programmers? To what extent can typestate constraints be inferred, to reduce the burden of large type annotations? This thesis aims to answer these questions and provide a holistic treatment of the subject, with original contributions to both the theorical and practical aspects of typestate

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Concepts, Frames and Cascades in Semantics, Cognition and Ontology

This open access book presents novel theoretical, empirical and experimental work exploring the nature of mental representations that support natural language production and understanding, and other manifestations of cognition. One fundamental question raised in the text is whether requisite knowledge structures can be adequately modeled by means of a uniform representational format, and if so, what exactly is its nature. Frames are a key topic covered which have had a strong impact on the exploration of knowledge representations in artificial intelligence, psychology and linguistics; cascades are a novel development in frame theory. Other key subject areas explored are: concepts and categorization, the experimental investigation of mental representation, as well as cognitive analysis in semantics. This book is of interest to students, researchers, and professionals working on cognition in the fields of linguistics, philosophy, and psychology

Second Annual Workshop on Space Operations Automation and Robotics (SOAR 1988)

Papers presented at the Second Annual Workshop on Space Operation Automation and Robotics (SOAR '88), hosted by Wright State University at Dayton, Ohio, on July 20, 21, 22, and 23, 1988, are documented herein. During the 4 days, approximately 100 technical papers were presented by experts from NASA, the USAF, universities, and technical companies. Panel discussions on Human Factors, Artificial Intelligence, Robotics, and Space Systems were held but are not documented herein. Technical topics addressed included knowledge-based systems, human factors, and robotics