%TI An ALPS ASCII interchange format for describing hypertext trails %OR UNISA %AV fax +61 8 3023381 %LT CIS-92-002 %AU Thomas, B.H. %YR 1992 %AB A subset of ALPS (A Language for Process Specification) has been used to describe hypertext trails. One major problem in describing these hypertext trails is having a proper interchange language external to the information spaces. This paper describes an ASCII file format for the subset of ALPS used for hypertext trail definition. This paper gives a BNF grammar, lexicon, and a detailed example of an ALPS program.
%TI The Use of Explicit Goals for Knowledge to Guide Inference and Learning %AU Ashwin Ram %AU Lawrence Hunter %PU Journal of Applied Intelligence, 2(1):47-73, 1992 %AV ftp://ftp.cc.gatech.edu/pub/ai/ram/git-cc-92-04.ps.Z %OR GTECH %LT GIT-CC-92/04 %YR 1992 %AB Combinatorial explosion of inferences has always been a central problem in artificial intelligence. Although the inferences that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially infinite), the inferential resources available to any reasoning system are limited. With limited inferential capacity and very many potential inferences, reasoners must somehow control the process of inference. Not all inferences are equally useful to a given reasoning system. Any reasoning system that has goals (or any form of a utility function) and acts based on its beliefs indirectly assigns utility to its beliefs. Given limits on the process of inference, and variation in the utility of inferences, it is clear that a reasoner ought to draw the inferences that will be most valuable to it. This paper presents an approach to this problem that makes the utility of a (potential) belief an explicit part of the inference process. The method is to generate explicit desires for knowledge. The question of focus of attention is thereby transformed into two related problems: How can explicit desires for knowledge be used to control inference and facilitate resource-constrained goal pursuit in general? and, Where do these desires for knowledge come from? We present a theory of knowledge goals, or desires for knowledge, and their use in the processes of understanding and learning. The theory is illustrated using two case studies, a natural language understanding program that learns by reading novel or unusual newspaper stories, and a differential diagnosis program that improves its accuracy with experience.
%TI Learning a Class of Large Finite State Machines with a Recurrent Neural Network %AU C. L. Giles %AU B. G. Horne %AU T. Lin %MN August %YR 1994 %LT CS-TR-3328 %AB One of the issues in any learning model is how it scales with problem size. Neural networks have not been immune to scaling issues. We show that a dynamically-driven discrete-time recurrent network (DRNN) can learn rather large grammatical inference problems when the strings of a finite memory machine (FMM) are encoded as temporal sequences. FMMs are a subclass of finite state machines which have a finite memory or a finite order of inputs and outputs. The DRNN that learns the FMM is a neural network that maps directly from the sequential machine implementation of the FMM. It has feedback only from the output and not from any hidden units; an example is the recurrent network of Narendra and Parthasarathy. (FMMs that have zero order in the feedback of outputs are called definite memory machines and are analogous to Time-delay or Finite Impulse Response neural networks.) Due to their topology these DRNNs are as least as powerful as any sequential machine implementation of a FMM and should be capable of representing any FMM. We choose to learn ``particular FMMs.\' Specifically, these FMMs have a large number of states (simulations are for $256$ and $512$ state FMMs) but have minimal order, relatively small depth and little logic when the FMM is implemented as a sequential machine. Simulations for the number of training examples versus generalization performance and FMM extraction size show that the number of training samples necessary for perfect generalization is less than that necessary to completely characterize the FMM to be learned. This is in a sense a best case learning problem since any arbitrarily chosen FMM with a minimal number of states would have much more order and string depth and most likely require more logic in its sequential machine implementation. %OR UMD %AV url ftp://ftp.cs.umd.edu/pub/papers/TRs/3328.ps.Z
%AU Margaret Recker %PU Educational Multimedia and Hypermedia Annual 1994 %AV ftp://ftp.cc.gatech.edu/pub/ai/mimi/er-mr-94-01.ps.gz %OR GTECH %LT ER-MR-94/01 %YR 1994 %AB We present a theoretical approach and a methodology for analyzing data from students interacting with and learning from hypermedia systems. In our approach, interactions are viewed to be mutually influenced by individual students' goals and strategies and the actions supported by the interface of the learning environment. The approach is illustrated by modelling data from an empirical study in which students browsed through a hypertext instructional environment to learn about programming concepts. By using the explanatory power of the computational model, interactions can be analyzed to determine patterns of use. Results obtained from this method of analysis yield specific feedback on system design and prescriptions for improving the design. More theoretically, they provide valuable insights on the nature of human cognition and learning in the context of interactive educational technologies.
Petre & Price (1992), "Why Computer Interfaces Are Not Like Paintings: the user as a deliberate reader".
Abstract
Designers seeking to improve human-computer interfaces, particularly those concerned with programming environments, often assume that "graphics" will always result in an improvement over "text". Such claims are especially difficult to assess, given that people have used the terms "text" and "graphics" in different and conflicting ways throughout the literature. This paper suggests a preliminary, consistent terminology for discussing "graphical interfaces" (including so-called "visual programming systems") to highlight some of the issues involved in using "graphics" in notations and interfaces. It discusses evidence from empirical studies showing that the use of "graphics" doesn't necessarily lead to improvement and may introduce its own problems. The paper concludes with a discussion of the succesful integration of "graphics" and "text".