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Children face an enormously difficult task in learning their native language. It is widely believed that they do not receive or make little use of negative evidence (Marcus, 1993), and yet it has been proven that many classes of languages less powerful than natural languages cannot be learned in the absence of negative evidence (Gold, 1964). In this paper we present an approach to learning good approximations to members of one such class of languages, the regular languages, based on positive evidence alone.

We show how an agent can acquire conceptual knowledge by sensorimotor interaction with its environment. The method has much in common with the notion of image-schemas, which are central to Mandler's theory of conceptual development. We show that Mandler's approach is feasible in an artificial agent.

This article explores the rationale for the 2011-2013 review of the National Curriculum in England, a rationale informed by the work of an Expert Panel chaired by the author. The focus is on the fundamental principles that determine the nature and content of the curriculum. The approach adopted by the review is contrasted with that adopted when the Qualifications and Curriculum Authority revised the curriculum in 2007. The article discusses the question of who can legitimately drive change in curriculum content and what the implications are for the cycle of curriculum change.

Background: In this article we address some of the challenges posed by the development of national assessment systems and discuss the need for high quality information on trends in attainment; support for school improvement processes and ways in which learning should be enhanced through valid assessment. Purpose: Key elements are explored, including the need to: monitor national standards; provide accountability data; give feedback to learners, teachers and parents. The dangers of multi-purpose testing are outlined in the context of political and educational objectives and the need to separate out the functions in an educationally valid way are considered. Evidence: Experiences in England are used to illustrate some of the key issues and problems that need to be taken into account when designing effective national assessment models. We refer to the national debate and policy reviews on assessment issues. We also draw on a body of literature related to this field of knowledge. Main argument: A range of possible models of assessment are outlined and their advantages and disadvantages discussed. The aim is to offer potential systems for consideration in an attempt to promote the design and development of assessment processes that will generate valid and reliable data on attainment for individuals, schools and at a national level. Conclusion: The challenge is to find ways of achieving these objectives within a framework of educational quality through the enhancement of teaching, learning and assessment.

The analysis presented in this paper offers different examples of how timing issues have adversely affected a series of innovations in education and training. The term "'temporal discontinuity" is used since the problems cannot simply be characterised as "'undue rushing" or "impatience on the part of policy makers." Whilst over-hasty scheduling of innovation and a general shortage of time plays a part in the problems on which the paper focuses, it is suggested that the sequencing of design, piloting, evaluation and implementation can break down, falling out of adequate synchronisation. This is more complex than simple shortage of time; remedying this requires more than just an (often-called-for) slower pace in evaluating and implementing innovations. The paper uses three carefully-selected case studies from the English education system to illustrate significant variants of temporal discontinuity: the history of the Assessment of Performance Unit; the implementation of system-level reforms in academic and vocational qualifications for 18 year olds in 2000; and the history of General National Vocational Qualifications. The paper suggests that genuine reform and innovation are seriously impeded by problems resulting from temporal discontinuity. It also suggests that the problems are in part rooted in the timing of political cycles, and calls for a radical examination of the management of large-scale innovation in education and training. (Contains 1 figure.)

This article analyses the increasingly diverse and sophisticated critique of "outcomes approaches" in vocational qualifications; critique which has now moved well beyond the early claims of reductivism and behaviourism. Avoiding a naive position on extraction of points of consensus, this article attempts to extract key issues which have purchase on policy and practice relating to National Vocational Qualifications. It outlines not only the ways in which current policy and development processes associated with the qualifications may be defective, but also ways in which they might be enhanced. Crucially, the article points to the severe limitations of the concept of "competence" which lies at the heart of outcomes approaches, and emphasises the need to reconsider a "formation" model for initial vocational education and training, rather than a "competence" model. (Contains 1 table and 1 figure.)

Focusing on the definition of skill transfer, this article examines the relationship between research work on transfer and various curriculum initiatives designed to promote it. Competing theories of transfer are explored, examples from medical and scientific education are described, and the National Council for Vocational Qualifications core skills are discussed. (25 references) (LRW)

Trail bridges can improve access to critical services such as health care, schools, and markets. In order to evaluate the impact of trail bridges in rural Rwanda, it is helpful to objectively know how and when they are being used. In this study, we deployed motion-activated digital cameras across several trail bridges installed by the non-profit Bridges to Prosperity. We conducted and validated manual counting of bridge use to establish a ground truth. We adapted an open source computer vision algorithm to identify and count bridge use reflected in the digital images. We found a reliable correlation with less than 3% error bias of bridge crossings per hour between manual counting and those sites at which the cameras logged short video clips. We applied this algorithm across 186 total days of observation at four sites in fall 2019, and observed a total of 33,800 daily bridge crossings ranging from about 20 to over 1,100 individual uses per day, with no apparent correlation between daily or total weekly rainfall and bridge use, potentially indicating that transportation behaviors, after a bridge is installed, are no longer impacted by rainfall conditions. Higher bridge use was observed in the late afternoons, on market and church days, and roughly equal use of the bridge crossings in each direction. These trends are consistent with the design-intent of these bridges. [ABSTRACT FROM AUTHOR]

The representations formed by the ventral and dorsal streams of a prelinguistic agent will tend to be too qualitatively similar to support the distinct roles required by PREDICATE(x) structure. We suggest that the attachment of qualities to objects is not a product of the combination of these separate processing streams, but is instead a part of the processing required in each. In addition, we suggest that the formation of objective predicates is inextricably bound up with the emergence of language itself, and so cannot be cleanly identified with any prelinguistic cognitive capacities.

Administrations worldwide are seeing assessment as a policy tool to achieve educational aims and objectives. The outcomes of public examinations are increasingly viewed not only as important for individual learners - to enable them to access the labour market and further educational opportunities - but as a means of pursuing a series of aims, including assessing the quality of schools, influencing teaching and learning, and affecting curriculum content. This article looks at some of the challenges this raises, and some of the steps that can be taken to move things forward. [ABSTRACT FROM AUTHOR]

Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. While many approaches involve manual behavior specification via code or reliance on a defined taxonomy of possible behaviors, the meta-modeling framework in Miner [2010] generates mapping functions between agent-level parameters and swarm-level parameters, which are re-usable once generated. This work builds on that framework by integrating demonstration by image or video. The demonstrator specifies spatial motion of the agents over time and retrieves agent-level parameters required to execute that motion. The framework, at its core, uses computationally cheap image-processing algorithms. Our work is tested with a combination of primitive visual feature extraction methods (contour area and shape) and features generated using a pre-trained deep neural network in different stages of image featurization. The framework is also evaluated for its potential using complex visual features for all image featurization stages. Experimental results show significant coherence between demonstrated behavior and predicted behavior based on estimated agent-level parameters specific to the spatial arrangement of agents. [ABSTRACT FROM AUTHOR]

Though graph grammars have been widely investigated for 40 years, few learning results exist for them. The main reasons come from complexity issues that are inherent when graphs, and a fortiori graph grammars, are considered. The picture is however different if one considers drawings of graphs, rather than the graphs themselves. For instance, it has recently been proved that the isomorphism and pattern searching problems could be solved in polynomial time for plane graphs, that is, planar embeddings of planar graphs. In this paper, we introduce the Plane Graph Grammars (PGG) and detail how they differ to usual graph grammar formalism's while at the same time they share important properties with string context-free grammars. In particular, though exponential in the general case, we provide an appropriate restriction on languages that allows the parsing of a graph with a given PGG in polynomial time. We demonstrate that PGG are well-shaped for learning: we show how recent results on string grammars can be extended to PGG by providing a learning algorithm that identifies in the limit the class of substitutable plane graph languages. Our algorithm runs in polynomial time assuming the same restriction used for polynomial parsing, and the amount of data needed for convergence is comparable to the one required in the case of strings. [ABSTRACT FROM AUTHOR]

This brief presents a low-power, flexible, and multichannel electroencephalography (EEG) feature extractor and classifier for the purpose of personalized seizure detection. Various features and classifiers were explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. Additionally, algorithmic and hardware optimizations were identified to further improve performance. The classifiers studied include $k$-nearest neighbor, support vector machines, naïve Bayes, and logistic regression (LR) . All feature and classifier pairs were able to obtain F1 scores over 80% and onset sensitivity of 100% when tested on ten patients. A fully flexible hardware system was implemented that offers parameters for the number of EEG channels, the number of features, the classifier type, and various word width resolutions. Five seizure detection processors with different classifiers have been fully placed and routed on a Virtex-5 field-programmable gate array and been compared. It was found that five features per channel with LR proved to be the best solution for the application of personalized seizure detection. LR had the best average F1 score of 91%, the smallest area and power footprint, and the lowest latency. The ASIC implementation of the same combination in 65-nm CMOS shows that the processor occupies 0.008 mm2 and dissipates 19 nJ at 484 Hz. [ABSTRACT FROM AUTHOR]

In this article, the author focuses on the establishment of a Royal College of Teaching and rules and regulations which will govern it. He talks about a paper published in 2007 by Mary James, professor at the University of Cambridge, and Pauline Perry of Southwark, England. The author informs that the idea gained momentum when he proposed the royal college at a conference of the Prince's Teaching Institute (PTI) in 2010.

A foreword to "Curriculum Journal," is presented.

Information about the topics discussed during the 2009 Spring Symposium Series by the Association for the Advancement of Artificial Intelligence at Stanford University in California on March 23-25, 2009 is presented. It states that the event aims to initiate the development of a problem repository in the field of qualitative spatial and temporal reasoning. Moreover, presenters of the event identified the challenges for the future midterm and long-term research.

The article discusses efforts in the field of artificial intelligence (AI) to develop metacognitive loops that give automated devices the ability to detect, evaluate, and remedy flaws in their operation. Central to their function is the ability to discern a gap between actual and intended results. The author dilates upon both metacognitive-enhanced reinforcement learning and metacognitive-enhanced human-computer dialogue, and notes progress that has been made towards creating a domain-independent application.

Background: Airway wall remodelling and inflammation are features of chronic asthma. Transforming growth factor beta (TGF-beta) has been implicated in these processes.Aim: To determine the effect of allergen challenge on airway inflammation and remodelling and whether TGF-beta isoforms and the Smad signalling pathways are involved.Methods: Thirteen patients with atopic asthma underwent inhalational challenge with 0.9% saline, followed by allergen 3-4 weeks later. After both challenges, fibreoptic bronchoscopy was undertaken to obtain bronchial biopsies and tissue samples were processed for immunohistochemistry and examined by microscopy.Results: Forced expiratory volume in 1 s (FEV(1)) fell after allergen challenge (mean (SE) -28.1 (0.9)% at 30 min with a late response at 7 hours (-23.0 (1.2)%). Allergen challenge caused an increase in neutrophils and eosinophils in the bronchial mucosa compared with saline. Sub-basement membrane (SBM) thickness did not change after allergen, but tenascin deposition in SBM was increased. Intranuclear (activated) Smad 2/3 and Smad 4 detected by immunohistochemistry were increased after allergen challenge in epithelial and subepithelial cells of bronchial biopsies. No inhibitory Smad (Smad 7) protein was detected. TGF-beta isoforms 1, 2 and 3 were expressed predominantly in bronchial epithelium after saline and allergen challenges, but only TGF-beta(2) expression was increased after allergen. Double immunostaining showed an increase in TGF-beta(2) positive eosinophils and neutrophils but not in TGF-beta(1) positive eosinophils and neutrophils after allergen challenge.Conclusions: TGF-beta(2) may contribute to the remodelling changes in allergic asthma following single allergen exposure. [ABSTRACT FROM AUTHOR]

Recent years have seen a resurgence of interest in the use of metacognition in intelligent systems. This article is part of a small section meant to give interested researchers an overview and sampling of the kinds of work currently being pursued in this broad area. The current article offers a review of recent research in two main topic areas: the monitoring and control of reasoning (metareasoning) and the monitoring and control of learning (metalearning). [ABSTRACT FROM AUTHOR]

Maintaining adequate performance in dynamic and uncertain settings has been a perennial stumbling block for intelligent systems. Nevertheless, any system intended for real-world deployment must be able to accommodate unexpected change—that is, it must be perturbation tolerant . We have found that metacognitive monitoring and control—the ability of a system to self-monitor its own decision-making processes and ongoing performance, and to make targeted changes to its beliefs and action-determining components—can play an important role in helping intelligent systems cope with the perturbations that are the inevitable result of real-world deployment. In this article we present the results of several experiments demonstrating the efficacy of metacognition in improving the perturbation tolerance of reinforcement learners, and discuss a general theory of metacognitive monitoring and control, in a form we call the metacognitive loop . ||This research is supported in part by the AFOSR and ONR. [ABSTRACT FROM AUTHOR]

The American Association for Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2004 Spring Symposium Series, Monday through Wednesday, March 22-24, at Stanford University. The titles of the eight symposia were (1) Accessible Hands-on Artificial Intelligence and Robotics Education; (2) Architectures for Modeling Emotion: Cross-Disciplinary Foundations; (3) Bridging the Multiagent and Multirobotic Research Gap; (4) Exploring Attitude and Affect in Text: Theories and Applications; (5) Interaction between Humans and Autonomous Systems over Extended Operation; (6) Knowledge Representation and Ontologies for Autonomous Systems; (7) Language Learning: An Interdisciplinary Perspective; and (8) Semantic Web Services. Each symposium had limited attendance. Most symposia chairs elected to create AAAI technical reports of their symposium, which are available as pa- perbound reports or (for AAAI members) are downloadable on the AAAI members-only Web site. This report includes summaries of the eight symposia, written by the symposia chairs. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one by Valter Crescenzi and Paolo Merialdo, in their article "Wrapper Inference for Ambiguous Web Pages," and another titled "The QSM Algorithm and its Application to Software Behavior Model Induction," by Pierre Dupont and colleagues.

This debate is not as evident in academic writing as might be expected. In journal articles, there is a clear tendency to examine and advocate ‘new skills' or explore areas such as ‘multiple intelligence' without situating these in a wider debate about the aims of the school curriculum. Articles land on either side of the debate without acknowledging that the debate is occurring. There are exceptions. Coincident with the work laying down theprinciples for the new National Curriculum.

The article reports on the symposium titled "Computational Approaches to Representation Change during Learning and Development." the symposium is developed by the Association for the Advancement of Artificial Intelligence (AAAI). It aims to understand how representation change can be transferred to artificial intelligence systems. It also intends to turn representation change processes into automation algorithms. The symposium also includes the issues of hierarchical Bayes method, feature learning, machine learning and robotics.

The article focuses on the significance of metacognition in computation. A component that could measure and monitor the value of ongoing deliberation/computation, stopping it and forcing action when it ceases to have positive value, could help an agent become more rational to more effectively marshal and deploy its resources for maximal benefit. Similar benefits would be expected in the case of an agent that could monitor and control its learning components. Among the fascinating topics that emerged during the symposium were the relation between metacognition and emotion. To help frame these questions and explore these topics, the symposium featured two tutorial-style keynotes in addition to the opening address, and a guided brainstorming session.

The article offers the author's insights regarding the review of the National Curriculum for science-related subjects in Great Britain. The author notes that conceptual and factual content must be specified by the curriculum and that teachers should select the core scientific topics. He states that the curriculum must secure the motivation of pupils to learn through the skills of teachers and the construction of learning programmes by the schools.

The author discusses a case of academic serendipity. Repeated applications of a procedure he used to eliminate bad data from a data set seemed to generate increasingly better results. After his algorithm indicated that 80% of his data were bad, however, a closer look at the process revealed it to flawed. Further study of this unexpected result culminated in several published papers.

The American Association for Artificial Intelligence presented its 2005 Spring Symposium Series on Monday through Wednesday, March 21-23, 2005 at Stanford University in Stanford, California. The topics of the eight symposia in this symposium series were (1) Al Technologies for Homeland Security; (2) Challenges to Decision Support in a Changing World; (3) Developmental Robotics; (4) Dialogical Robots: Verbal Interaction with Embodied Agents and Situated Devices; (5) Knowledge Collection from Volunteer Contributors; (6) Metacognition in Computation; (7) Persistent Assistants: Living and Working with Al; and (8) Reasoning with Mental and External Diagrams: Computational Modeling and Spatial Assistance. [ABSTRACT FROM AUTHOR]