27 resultados para Knowledge (Theory)


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The departure point for the paper is the need to scrutinise previously unconsidered dimensions which are fundamental to understanding the dynamics of the planning enforcement system. Drawing upon emerging themes in regulation theory the paper fuses these with knowledge constructs. The rationale is that regulatory regimes must be informed by knowledge imparted from a range of sources and the resultant quality of decision making is inextricably linked to the robustness and completeness of the evidence base collated.
The theoretical analysis, coupled with proposed radical legislative changes, provides a lens for an empirical investigation which scrutinises tactics, strategies, operational mechanisms, attitudinal dimensions and ethics with a view to identifying key factors impacting upon enforcement efficacy. Prizes and pitfalls are identified in the course of the analysis and evaluation, with evidence-based remedies suggested where appropriate. The paper concludes by reflecting on the importance of theoretical synergy, epistemological advancement, taking cognisance of ethical and attitudinal challenges facing the planning profession; and, stresses the importance of identifying and bringing to book those who flagrantly breach the Code of Professional Conduct.

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Knowledge is an important component in many intelligent systems.
Since items of knowledge in a knowledge base can be conflicting, especially if
there are multiple sources contributing to the knowledge in this base, significant
research efforts have been made on developing inconsistency measures for
knowledge bases and on developing merging approaches. Most of these efforts
start with flat knowledge bases. However, in many real-world applications, items
of knowledge are not perceived with equal importance, rather, weights (which
can be used to indicate the importance or priority) are associated with items of
knowledge. Therefore, measuring the inconsistency of a knowledge base with
weighted formulae as well as their merging is an important but difficult task. In
this paper, we derive a numerical characteristic function from each knowledge
base with weighted formulae, based on the Dempster-Shafer theory of evidence.
Using these functions, we are able to measure the inconsistency of the knowledge
base in a convenient and rational way, and are able to merge multiple knowledge
bases with weighted formulae, even if knowledge in these bases may be
inconsistent. Furthermore, by examining whether multiple knowledge bases are
dependent or independent, they can be combined in different ways using their
characteristic functions, which cannot be handled (or at least have never been
considered) in classic knowledge based merging approaches in the literature.

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Critical decisions are made by decision-makers throughout
the life-cycle of large-scale projects. These decisions are crucial as they
have a direct impact upon the outcome and the success of projects. To aid
decision-makers in the decision making process we present an evidential
reasoning framework. This approach utilizes the Dezert-Smarandache
theory to fuse heterogeneous evidence sources that suffer from levels
of uncertainty, imprecision and conflicts to provide beliefs for decision
options. To analyze the impact of source reliability and priority upon
the decision making process, a reliability discounting technique and a
priority discounting technique, are applied. A maximal consistent subset
is constructed to aid in dening where discounting should be applied.
Application of the evidential reasoning framework is illustrated using a
case study based in the Aerospace domain.

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Combination rules proposed so far in the Dempster-Shafer theory of evidence, especially Dempster rule, rely on a basic assumption, that is, pieces of evidence being combined are considered to be on a par, i.e. play the same role. When a source of evidence is less reliable than another, it is possible to discount it and then a symmetric combination operation is still used. In the case of revision, the idea is to let prior knowledge of an agent be altered by some input information. The change problem is thus intrinsically asymmetric. Assuming the input information is reliable, it should be retained whilst the prior information should
be changed minimally to that effect. Although belief revision is already an important subfield of artificial intelligence, so far, it has been little addressed in evidence theory. In this paper, we define the notion of revision for the theory of evidence and propose several different revision rules, called the inner and outer
revisions, and a modified adaptive outer revision, which better corresponds to the idea of revision. Properties of these revision rules are also investigated.

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Complex collaboration in rapidly changing business environments create challenges for management capability in Utility Horizontal Supply Chains (UHSCs) involving the deploying and evolving of performance measures. The aim of the study is twofold. First, there is a need to explore how management capability can be developed and used to deploy and evolve Performance Measurement (PM), both across a UHSC and within its constituent organisations, drawing upon a theoretical nexus of Dynamic Capability (DC) theory and complementary Goal Theory. Second, to make a contribution to knowledge by empirically building theory using these constructs to show the management motivations and behaviours within PM-based DCs. The methodology uses an interpretive theory building, multiple case based approach (n=3) as part of a USHC. The data collection methods include, interviews (n=54), focus groups (n=10), document analysis and participant observation (reflective learning logs) over a five-year period giving longitudinal data. The empirical findings lead to the development of a conceptual framework showing that management capabilities in driving PM deployment and evolution can be represented as multilevel renewal and incremental Dynamic Capabilities, which can be further understood in terms of motivation and behaviour by Goal-Theoretic constructs. In addition three interrelated cross cutting themes of management capabilities in consensus building, goal setting and resource change were identified. These management capabilities require carefully planned development and nurturing within the UHSC. 

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Increased understanding of knowledge transfer (KT) from universities to the wider regional knowledge ecosystem offers opportunities for increased regional innovation and commercialisation. The aim of this article is to improve the understanding of the KT phenomena in an open innovation context where multiple diverse quadruple helix stakeholders are interacting. An absorptive capacity-based conceptual framework is proposed, using a priori constructs which portrays the multidimensional process of KT between universities and its constituent stakeholders in pursuit of open innovation and commercialisation. Given the lack of overarching theory in the field, an exploratory, inductive theory building methodology was adopted using semi-structured interviews, document analysis and longitudinal observation data over a three-year period. The findings identify five factors, namely human centric factors, organisational factors, knowledge characteristics, power relationships and network characteristics, which mediate both the ability of stakeholders to engage in KT and the effectiveness of knowledge acquisition, assimilation, transformation and exploitation. This research has implications for policy makers and practitioners by identifying the need to implement interventions to overcome the barriers to KT effectiveness between regional quadruple helix stakeholders within an open innovation ecosystem.

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There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work.

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It is widely accepted that knowledge of certain of one’s own mental states is authoritative in being epistemically more secure than knowledge of the mental states of others, and theories of self-knowledge have largely appealed to one or the other of two sources to explain this special epistemic status. The first, ‘detectivist’, position, appeals to an inner perception-like basis, whereas the second, ‘constitutivist’, one, appeals to the view that the special security awarded to certain self-knowledge is a conceptual matter. I argue that there is a fundamental class of cases of authoritative self-knowledge, ones in which subjects are consciously thinking about their current, conscious intentional states, that is best accounted for in terms of a theory that is,
broadly speaking, introspectionist and detectivist. The position developed has an intuitive plausibility that has inspired many who work in the Cartesian tradition, and the potential to yield a single treatment of the basis of authoritative self-knowledge for both intentional states and sensation states.

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It is widely accepted that knowledge of certain of one’s own mental states is authoritative in being epistemically more secure than knowledge of the mental states of others, and theories of self-knowledge have largely appealed to one or the other of two sources to explain this special epistemic status. The first, ‘detectivist’, position, appeals to an inner perception-like basis, whereas the second, ‘constitutivist’, one, appeals to the view that the special security awarded to certain self-knowledge is a conceptual matter. I argue that there is a fundamental class of cases of authoritative self-knowledge, ones in which subjects are consciously thinking about their current, conscious intentional states, that is best accounted for in terms of a theory that is, broadly speaking, introspectionist and detectivist. The position developed has an intuitive plausibility that has inspired many who work in the Cartesian tradition, and the potential to yield a single treatment of the basis of authoritative self-knowledge for both intentional states and sensation states.