Abstract not found

Software development remains mentally challenging despite the continual advancement of training, techniques, and tools. Because completely automating software development is currently impossible, it makes sense to seriously consider how tools can improve the mental activities of developers apart from automating them away. Such mental assistance can be called “cognitive support”. Understanding and developing cognitive support in software engineering tools is an important research issue but, unfortunately, at the moment our theoretical foundations for it are inadequately developed. Furthermore, much of the relevant research has occurred outside of the software engineering community, and is therefore not easily available to the researchers who typically develop software engineering tools. Tool evaluation, comparison, and development are consequently impaired. The present work introduces a theoretical framework intended to seed further systematic study of cognitive support in the field of software engineering tools. This theoretical framework, called RODS, imports ideas and methods from a field of cognitive science called “distributed cognition”. The crucial concept in RODS is that cognitive support can be understood and explained in terms of the computational advantages that are conferred when cognition is redistributed between software developer and their tools and environment. The name RODS, in fact, comes from the

An account of parameter setting during grammatical acquisition is presented in terms of Generalized Categorial Grammar embedded in a multiple default inheritance hierarchy, providing a natural partial ordering on the setting of parameters (Briscoe, 1997a). Experiments reported show that several experimentally effective learners can be defined in this framework capable of reliably acquiring a grammar from a sequence of triggers drawn from one of 70 full languages (or the 200+ more restricted subset languages of these full languages). Evolutionary computational simulations of evolving populations of such language learners/users suggest that: 1) languages evolve towards greater learnability, interpretability and/or expressivity; 2) learning procedures evolve towards more efficient variants depending on the linguistic environment of adaptation. The reciprocal evolution of language learning procedures and of language creates a genuinely coevolutionary dynamic, despite the relative speed of ...

The concept of emergent “levels ” (i.e. levels that arise from interactions of objects at lower levels) is fundamental to scientific theory. In this paper, we argue for an expanded role for this concept of “levels ” in the study of science. We show that confusion of levels (and “slippage ” between levels) is the source of many deep misunderstandings about patterns and phenomena in the world. These misunderstandings are evidenced not only in students ’ difficulties in the formal study of science but also in their misconceptions about experiences in their everyday lives. The StarLogo modeling language is designed as a medium for students to build models of multi-leveled phenomena and through these constructions explore the concept of levels. We describe several case studies of students working in StarLogo. The cases illustrate students ’ difficulties with the concept of levels, and how they can begin to develop richer understandings.

The main purpose of this article is to argue the merits of ‘population thinking’ in gaining insight into linguistic and, in particular, syntactic change. Population-level thinking and modelling can shed new light on many issues in the study of language acquisition and language change, and leads directly to a precise and useful characterisation of E-language. Something which is lacking in current generative linguistics. Moreover, this way of thinking is fully compatible with the major insights of the latter, and integrates them into a framework in which language variation and change are inherent and inevitable, rather than peripheral and/or accidental, properties of language. I will argue that (E-)languages are best modelled as particular kinds of dynamical systems; namely, complex adaptive systems (where these terms are used in technical senses made precise below). The article both introduces some relevant ideas and techniques from modern evolutionary theory, and from the mathematical and computational study of dynamical systems, and also offers a critique and review of some recent work on syntactic change in this emerging framework, arguing that a useful population model needs to support overlapping generations of language users and learners and to allow quite detailed modelling of differing demographic scenarios. I utilise simple linguistic scenarios based on constituent order changes to illustrate the ideas and techniques clearly. I abstract away from the sociolinguistic detail of the actuation

The field of artificial life is enriching both the content and method of philosophy. One example of the impact of artificial life on the content of philosophy is the light it sheds on the perennial philosophical question of the nature of emergent pheonomena in general. Another second example is the way it highlights and promises to explain the suppleness of mental processes. Artificial life's computational thought experiments also provide philosophy with a methodological innovation. The limitations of the central arguments in Stephen Jay Gould's Wonderful Life and Daniel Dennett's Darwin's Dangerous Idea illustrate the value of this new method.

To surmount the notorious difficulties of defining life, we should evaluate theories of life not by whether they provide necessary and sufficient conditions for our current preconceptions about life but by how well they explain living phenomena and how satisfactorily they resolve puzzles about life. On these grounds, the theory of life as supple adaptation (Bedau 1996) gets support from its natural and compelling resolutions of the following four puzzles: (1) How are different forms of life at different levels of the vital hierarchy related? (2) Is there a continuum between life and non-life? (3) Does life essentially concern a living entity's material composition or its form? (4) Are life and mind intrinsically connected?

ABSTRACT. This paper suggests that from a cognitive-evolutionary perspective, computational media are qualitatively different from many of the technologies that have promised educational change in the past and failed to deliver. Recent theories of human cognitive evolution suggest that human cognition has evolved through four distinct stages: episodic, mimetic, mythic, and theoretical. This progression was driven by three cognitive advances: the ability to “represent ” events, the development of symbolic reference, and the creation of external symbolic representations. In this paper, we suggest that we are developing a new cognitive culture: a “virtual ” culture dependent on the externalization of symbolic processing. We suggest here that the ability to externalize the manipulation of formal systems changes the very nature of cognitive activity. These changes will have important consequences for mathematics education in coming decades. In particular, we argue that mathematics education in a virtual culture should strive to give students generative fluency to learn varieties of representational systems, provide opportunities to create and modify representational forms, develop skill in making and exploring virtual environments, and emphasize mathematics as a fundamental way of making sense of the world, reserving most exact computation and formal proof for those who will need those specialized skills.

This thesis demonstrates how the power of symbolic processing can be exploited in the learning of low level control functions. It proposes a novel hybrid architecture with a tight coupling between a variant of symbolic planning and reinforcement learning. This architecture combines the strengths of the function approximation of subsymbolic learning with the more abstract compositional nature of symbolic learning. The former is able to represent mappings of world states to actions in an accurate way. The latter allows a more rapid solution to problems by exploiting structure within the domain. A control function is learnt over time through interaction with the world. Symbols are attached to features in the functions. The symbolic attachments act as anchor points used to transform the function of a previously learnt task to that of a new task. The solution of more complex tasks is achieved through composing simpler functions, using the symbolic attachments to determine the composition. T...

Stochastic Diffusion Search (sds) was introduced by Bishop (1989a) as an algorithm to solve pattern matching problems. It relies on many concurrent partial evaluations of candidate solutions by a population of agents and communication between those agents to locate the optimal match to a target pattern in a search space. In subsequent research, several variations on the original algorithmic formulation were proposed. It also became evident that its main principles – partial evaluation and communication between agents – can be employed to problems outside the pattern matching domain. The primary aim of this dissertation is to develop these expansive views further: sds is proposed as a metaheuristic, a generic heuristic procedure for solving problems through search. Furthermore, it is proposed as a challenge to the dominant metaphor in computer science: sequential computation. The thesis proceeds in a structured way by first considering all questions that can be asked about a heuristic procedure like sds: questions of a foundational nature, questions pertaining to mathematical analysis, questions about application domains and questions about physical implementation. It is to the foundational issues that most attention is devoted. Analogies with selective processes in natural and social systems are investigated, as well as analogies with other metaheuristic techniques from artificial intelligence. An attempt is made to categorise potential variants, and to establish what kind of problems sds would be the optimal problem-solving method for. The work aims to provide an expanded but structured understanding of sds, to give guidelines for future work, and to establish how progress in other scientific disciplines can be of use in the study of sds, and vice versa. Preface All sciences characterise the essential nature of the systems they study. These characterisations are invariably qualitative in nature, for they set the terms with which more detailed knowledge can be developed. A. Newell and H. Simon (Newell and Simon, 1976) Cybernetics is the science of defensible metaphors.