52 resultados para Analogical reasoning


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Discussion of the national interest often focuses on how Britain's influence can be maximized, rather than on the goals that influence serves. Yet what gives content to claims about the national interest is the means-ends reasoning which links interests to deeper goals. In ideal-typical terms, this can take two forms. The first, and more common, approach is conservative: it infers national interests and the goals they advance from existing policies and commitments. The second is reformist: it starts by specifying national goals and then asks how they are best advanced under particular conditions. New Labour's foreign policy discourse is notable for its explicit use of a reformist approach. Indeed, Gordon Brown's vision of a 'new global society' not only identifies global reform as a key means of fulfilling national goals, but also thereby extends the concept of the national interest well beyond a narrow concern with national security.

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Information can be interpreted as in-formation, which refers to the potential of the form for a mediation of meaning. In this paper we focus on reasoning information and consider the question how form involved in reasoning can be used for an analysis of accounting narratives in corporate disclosures. An evaluation of experimental results is included.

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This paper reports the current state of work to simplify our previous model-based methods for visual tracking of vehicles for use in a real-time system intended to provide continuous monitoring and classification of traffic from a fixed camera on a busy multi-lane motorway. The main constraints of the system design were: (i) all low level processing to be carried out by low-cost auxiliary hardware, (ii) all 3-D reasoning to be carried out automatically off-line, at set-up time. The system developed uses three main stages: (i) pose and model hypothesis using 1-D templates, (ii) hypothesis tracking, and (iii) hypothesis verification, using 2-D templates. Stages (i) & (iii) have radically different computing performance and computational costs, and need to be carefully balanced for efficiency. Together, they provide an effective way to locate, track and classify vehicles.

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Competency management is a very important part of a well-functioning organisation. Unfortunately competency descriptions are not uniformly specified nor defined across borders: National, sectorial or organisational, leading to an opaque competency description market with a multitude of competency frameworks and competency benchmarks. An ontology is a formalised description of a domain, which enables automated reasoning engines to be built which by utilising the interrelations between entities can make “intelligent” choices in different situations within the domain. Introducing formalised competency ontologies automated tools, such as skill gap analysis, training suggestion generation, job search and recruitment, can be developed, which compare and contrast different competency descriptions on the semantic level. The major problem with defining a common formalised ontology for competencies is that there are so many viewpoints of competencies and competency frameworks. Work within the TRACE project has focused on finding common trends within different competency frameworks in order to allow an intermediate competency description to be made, which other frameworks can reference. This research has shown that competencies can be divided up into “knowledge”, “skills” and what we call “others”. An ontology has been created based on this with a simple structure of different “kinds” of “knowledges” and “skills” using semantic interrelations to define the basic semantic structure of the ontology. A prototype tool for analysing a skill gap analysis has been developed. Personal profiles can be produced using the tool and a skill gap analysis is performed on a desired competency profile by using an ontologically based inference engine, which is able to list closest fit and possible proficiency gaps

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Currently many ontologies are available for addressing different domains. However, it is not always possible to deploy such ontologies to support collaborative working, so that their full potential can be exploited to implement intelligent cooperative applications capable of reasoning over a network of context-specific ontologies. The main problem arises from the fact that presently ontologies are created in an isolated way to address specific needs. However we foresee the need for a network of ontologies which will support the next generation of intelligent applications/devices, and, the vision of Ambient Intelligence. The main objective of this paper is to motivate the design of a networked ontology (Meta) model which formalises ways of connecting available ontologies so that they are easy to search, to characterise and to maintain. The aim is to make explicit the virtual and implicit network of ontologies serving the Semantic Web.

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Context: Learning can be regarded as knowledge construction in which prior knowledge and experience serve as basis for the learners to expand their knowledge base. Such a process of knowledge construction has to take place continuously in order to enhance the learners’ competence in a competitive working environment. As the information consumers, the individual users demand personalised information provision which meets their own specific purposes, goals, and expectations. Objectives: The current methods in requirements engineering are capable of modelling the common user’s behaviour in the domain of knowledge construction. The users’ requirements can be represented as a case in the defined structure which can be reasoned to enable the requirements analysis. Such analysis needs to be enhanced so that personalised information provision can be tackled and modelled. However, there is a lack of suitable modelling methods to achieve this end. This paper presents a new ontological method for capturing individual user’s requirements and transforming the requirements onto personalised information provision specifications. Hence the right information can be provided to the right user for the right purpose. Method: An experiment was conducted based on the qualitative method. A medium size of group of users participated to validate the method and its techniques, i.e. articulates, maps, configures, and learning content. The results were used as the feedback for the improvement. Result: The research work has produced an ontology model with a set of techniques which support the functions for profiling user’s requirements, reasoning requirements patterns, generating workflow from norms, and formulating information provision specifications. Conclusion: The current requirements engineering approaches provide the methodical capability for developing solutions. Our research outcome, i.e. the ontology model with the techniques, can further enhance the RE approaches for modelling the individual user’s needs and discovering the user’s requirements.

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On 15-17 February 2008, a CME with an approximately circular cross section was tracked through successive images obtained by the Heliospheric Imager (HI) instrument onboard the STEREO-A spacecraft. Reasoning that an idealised flux rope is cylindrical in shape with a circular cross-section, best fit circles are used to determine the radial width of the CME. As part of the process the radial velocity and longitude of propagation are determined by fits to elongation-time maps as 252±5 km/s and 70±5° respectively. With the longitude known, the radial size is calculated from the images, taking projection effects into account. The radial width of the CME, S (AU), obeys a power law with heliocentric distance, R, as the CME travels between 0.1 and 0.4 AU, such that S=0.26 R0.6±0.1. The exponent value obtained is compared to published studies based on statistical surveys of in situ spacecraft observations of ICMEs between 0.3 and 1.0 AU, and general agreement is found. This paper demonstrates the new opportunities provided by HI to track the radial width of CMEs through the previously unobservable zone between the LASCO field of view and Helios in situ measurements.

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This paper focuses on improving computer network management by the adoption of artificial intelligence techniques. A logical inference system has being devised to enable automated isolation, diagnosis, and even repair of network problems, thus enhancing the reliability, performance, and security of networks. We propose a distributed multi-agent architecture for network management, where a logical reasoner acts as an external managing entity capable of directing, coordinating, and stimulating actions in an active management architecture. The active networks technology represents the lower level layer which makes possible the deployment of code which implement teleo-reactive agents, distributed across the whole network. We adopt the Situation Calculus to define a network model and the Reactive Golog language to implement the logical reasoner. An active network management architecture is used by the reasoner to inject and execute operational tasks in the network. The integrated system collects the advantages coming from logical reasoning and network programmability, and provides a powerful system capable of performing high-level management tasks in order to deal with network fault.

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Two experiments, using a mock legal case, confirmed the causal role of arguments in verdict decisions and explored the process involved. Experiment I showed that verdicts varied with the strength of counter-arguments and Experiment 2 showed that the use of background information that undermined such arguments determined the verdict reached. Such results confirm the causal role of arguments but do not speak to the representations constructed. In both experiments we analysed the reasons proposed for verdicts. Participants generally represented the state of affairs, and conjectured state of affairs, to which the arguments referred. Experiment 2 also asked participants about the number of causal possibilities they envisaged. Confidence in the verdict was moderated by the strength of counter-arguments but in different ways for those who envisaged a single causal account as opposed to two causal accounts. In the former case, confidence decreased with the rated strength of counter-arguments. In the latter case, confidence increased. We suggest that verdicts are abductive explanations of the events generated through a process of mental simulation.

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The comparison of cognitive and linguistic skills in individuals with developmental disorders is fraught with methodological and psychometric difficulties. In this paper, we illustrate some of these issues by comparing the receptive vocabulary knowledge and non-verbal reasoning abilities of 41 children with Williams syndrome, a genetic disorder in which language abilities are often claimed to be relatively strong. Data from this group were compared with data from typically developing children, children with Down syndrome, and children with non-specific learning difficulties using a number of approaches including comparison of age-equivalent scores, matching, analysis of covariance, and regression-based standardization. Across these analyses children with Williams syndrome consistently demonstrated relatively good receptive vocabulary knowledge, although this effect appeared strongest in the oldest children.

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We argue that hyper-systemizing predisposes individuals to show talent, and review evidence that hyper-systemizing is part of the cognitive style of people with autism spectrum conditions (ASC). We then clarify the hyper-systemizing theory, contrasting it to the weak central coherence (WCC) and executive dysfunction (ED) theories. The ED theory has difficulty explaining the existence of talent in ASC. While both hyper-systemizing and WCC theories postulate excellent attention to detail, by itself excellent attention to detail will not produce talent. By contrast, the hyper-systemizing theory argues that the excellent attention to detail is directed towards detecting 'if p, then q' rules (or [input-operation-output] reasoning). Such law-based pattern recognition systems can produce talent in systemizable domains. Finally, we argue that the excellent attention to detail in ASC is itself a consequence of sensory hypersensitivity. We review an experiment from our laboratory demonstrating sensory hypersensitivity detection thresholds in vision. We conclude that the origins of the association between autism and talent begin at the sensory level, include excellent attention to detail and end with hyper-systemizing.

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This study examines how children integrate information about counterfactual alternatives in making judgments. Previous research in adults had shown that they make judgments on the basis of comparisons between factual events and counterfactual alternatives. We suggest that children adopt a summative strategy instead, where they focus on the presented outcomes, both real and counterfactual, and base their judgments on the overall affective quality of these outcomes. Results from a single experiment comparing adults’ and children’s responses to a counterfactual judgment task show that children do tend to use a summative strategy as opposed to the comparative strategy adopted by adults. These results were further supported by participants’ justifications of their judgments, which were alternative focused for the adults, but outcome focused for the children. The results are discussed in relation to complexity-based theories of the development of human reasoning.

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Password Authentication Protocol (PAP) is widely used in the Wireless Fidelity Point-to-Point Protocol to authenticate an identity and password for a peer. This paper uses a new knowledge-based framework to verify the PAP protocol and a fixed version. Flaws are found in both the original and the fixed versions. A new enhanced protocol is provided and the security of it is proved The whole process is implemented in a mechanical reasoning platform, Isabelle. It only takes a few seconds to find flaws in the original and the fixed protocol and to verify that the enhanced version of the PAP protocol is secure.

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Computer music usually sounds mechanical; hence, if musicality and music expression of virtual actors could be enhanced according to the user’s mood, the quality of experience would be amplified. We present a solution that is based on improvisation using cognitive models, case based reasoning (CBR) and fuzzy values acting on close-to-affect-target musical notes as retrieved from CBR per context. It modifies music pieces according to the interpretation of the user’s emotive state as computed by the emotive input acquisition componential of the CALLAS framework. The CALLAS framework incorporates the Pleasure-Arousal-Dominance (PAD) model that reflects emotive state of the user and represents the criteria for the music affectivisation process. Using combinations of positive and negative states for affective dynamics, the octants of temperament space as specified by this model are stored as base reference emotive states in the case repository, each case including a configurable mapping of affectivisation parameters. Suitable previous cases are selected and retrieved by the CBR subsystem to compute solutions for new cases, affect values from which control the music synthesis process allowing for a level of interactivity that makes way for an interesting environment to experiment and learn about expression in music.