Static Computation and Reflection

Thesis (PhD) - Indiana University, Computer Sciences, 2008Most programming languages do not allow programs to inspect their static type information or perform computations on it. C++, however, lets programmers write template metaprograms, which enable programs to encode static information, perform compile-time computations, and make static decisions about run-time behavior. Many C++ libraries and applications use template metaprogramming to build specialized abstraction mechanisms, implement domain-specific safety checks, and improve run-time performance. Template metaprogramming is an emergent capability of the C++ type system, and the C++ language specification is informal and imprecise. As a result, template metaprogramming often involves heroic programming feats and often leads to code that is difficult to read and maintain. Furthermore, many template-based code generation and optimization techniques rely on particular compiler implementations, rather than language semantics, for performance gains. Motivated by the capabilities and techniques of C++ template metaprogramming, this thesis documents some common programming patterns, including static computation, type analysis, generative programming, and the encoding of domain-specific static checks. It also documents notable shortcomings to current practice, including limited support for reflection, semantic ambiguity, and other issues that arise from the pioneering nature of template metaprogramming. Finally, this thesis presents the design of a foundational programming language, motivated by the analysis of template metaprogramming, that allows programs to statically inspect type information, perform computations, and generate code. The language is specified as a core calculus and its capabilities are presented in an idealized setting

Multimethods and separate static typechecking in a language with C++-like object model

The goal of this paper is the description and analysis of multimethod implementation in a new object-oriented, class-based programming language called OOLANG. The implementation of the multimethod typecheck and selection, deeply analyzed in the paper, is performed in two phases in order to allow static typechecking and separate compilation of modules. The first phase is performed at compile time, while the second is executed at link time and does not require the modules' source code. OOLANG has syntax similar to C++; the main differences are the absence of pointers and the realization of polymorphism through subsumption. It adopts the C++ object model and supports multiple inheritance as well as virtual base classes. For this reason, it has been necessary to define techniques for realigning argument and return value addresses when performing multimethod invocations.Comment: 15 pages, 18 figure

Using Natural Language as Knowledge Representation in an Intelligent Tutoring System

Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they don’t learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge

Animating complex concepts

Techniques in computer-aided learning offer significant benefits for explaining difficult concepts in a way that is both stimulating and efficient. In the context of the STORM system, we have employed computer-based animation as a means of elucidating complex concepts in the educational domain of Internet and communications technology. Our experience reveals two important lessons for the application of computer animated instruction. Firstly, there is an essential requirement in the design process to ensure that the ontology and manner of presentation accurately conveys the intended message, whilst avoiding ambiguity and false or 'hidden' information. This focuses upon concise and disambiguated animations. Secondly, this requirement is best achieved through an iterative group-based development cycle of specification, testing and implementation

Acquiring Word-Meaning Mappings for Natural Language Interfaces

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance