Bibliography: p. 121-125. / xiv, 125 p. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / One of the central issues in Artificial Intelligence (AI) is common sense reasoning. This includes logics of knowledge and belief, non-monotonic reasoning, truth-maintenance and belief revision. Within these fields the notion of a consistent belief state is the crucial one. The issues of inconsistency and partiality of information are central to this thesis which proposes a logical knowledge representation formalism employing partial objects and partial worlds on its semantic side. The syntax includes a language, formulae, and partial theories. Partial worlds and theories are consistent, and contradictory information is assumed to arise in multiple agent situations. Relevant mathematical structures are discussed, in particular partial theories are related to partial worlds. A multiple agent case is considered. Partial theories can be partially ordered by an information ordering and the obtained lattice structure facilitates the theory selection process based on information value and truthness of theories. / Thesis (Ph.D.)--University of Adelaide, Dept. of Computer Science, 1998

Bibliography: p. 121-125. / xiv, 125 p. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / One of the central issues in Artificial Intelligence (AI) is common sense reasoning. This includes logics of knowledge and belief, non-monotonic reasoning, truth-maintenance and belief revision. Within these fields the notion of a consistent belief state is the crucial one. The issues of inconsistency and partiality of information are central to this thesis which proposes a logical knowledge representation formalism employing partial objects and partial worlds on its semantic side. The syntax includes a language, formulae, and partial theories. Partial worlds and theories are consistent, and contradictory information is assumed to arise in multiple agent situations. Relevant mathematical structures are discussed, in particular partial theories are related to partial worlds. A multiple agent case is considered. Partial theories can be partially ordered by an information ordering and the obtained lattice structure facilitates the theory selection process based on information value and truthness of theories. / Thesis (Ph.D.)--University of Adelaide, Dept. of Computer Science, 1998

Thesis (Ph. D.)--University of North Texas, Aug., 2007. / Title from title page display. Includes bibliographical references.

Thesis (Ph.D.)--University of Adelaide, Dept. of Computer Science, 1998. / Bibliography: p. 121-125.

Thesis (Ph. D.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on Oct. 20, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy, Department of Electrical and Computer Engineering, University of Alberta." Includes bibliographical references.

<p>Finding cause and effect relationships is one of the quintessential questions throughout many of the empirical sciences, AI, and Machine Learning. This dissertation develops graphical conditions and efficient algorithms for two problems, linear identification and imitation learning. For the first problem, it is well-known that correlation does not imply causation, so linear regression doesn’t necessarily find causal relations even in the limit of a large sample size. Over the past century, a plethora of methods has been developed for identifying interventional distributions given a combination of assumptions about the underlying mechanisms (e.g., linear functional dependence, causal diagram) and observational data. We characterize the computational complexity of several existing graphical criteria and develop new polynomial-time algorithms that subsume existing disparate efficient approaches. The proposed methods constitute the current state of the art in terms of polynomial-time identification coverage. In words, our methods have the capability of identifying the maximal set of structural coefficients when compared to any other efficient algorithms found in the literature.</p> <p>The second problem studied in the dissertation is Causal Sequential Imitation Learning, which is concerned with an agent that aims to learn a policy by observing an expert acting in the environment, and mimicking this expert's observed behavior. Sometimes, the agent (imitator) does not have access to the same set of observations or sensors as the expert, which gives rise to challenges in correctly interpreting expert actions. We develop necessary and sufficient conditions for the imitator to obtain identical performance to the expert in sequential settings given the domain’s causal diagram, and create a polynomial-time algorithm for finding the covariates to include when generating an imitating policy.</p> <p><br></p>

No description available.

A model is designed for the restructuring of knowledge. By way of suitably designed ontologies knowledge can be analysed to facilitate the creation of new knowledge and to render the knowledge suitable for reuse and for linkage to word-wide ontologies. / SUMMARY The need for this research emanated from the requirement for learning and adaptation in the fast-changing world we live in today. The changing world goes along with developments in communication means, whereby information becomes more accessible and sophisticated daily. A vast number of resources is available and accessible, distributing an enormous amount of information. The need is to turn these vast amounts of information into usable knowledge for use by an engineer in practice. Restructuring of knowledge is one way of approaching this need and is addressed in this study. This process can be facilitated by experienced persons who know what knowledge is needed in practice. There is a decline in the numbers of experienced civil engineers, leaving a gap between the supply and demand for suitably qualified and experienced civil engineers. The objective of this study is to meet the need for the restructuring of knowledge by the design a model (referred to as a logic base in this study) for the acquisition, reuse and the creation of engineering knowledge in a civil engineering environment. The main research question posed in this study is as follows: What are the key characteristics of a model (termed a “logic base” in this study) for the acquisition, reuse and the creation of knowledge in a civil engineering environment? This research commences with a set of research questions, followed by a literature review. Consideration is given to theories of knowledge, various methods of knowledge creation and knowledge acquisition. Several problem-solving techniques are reviewed. The structuring and architecture of knowledge and ontologies are researched and the role of systems engineering is studied. Various research methods are investigated and it is shown that case study research is the most suitable for the development of ontologies in civil engineering. The ontology of the logic base is therefore based on typical topics of case studies. Concept maps are employed to structure knowledge. This is done by defining appropriate concepts and classifying these into several ontological levels. The relationships among concepts and other influencing domains are studied. Knowledge of these relationships enables the application of several problem-solving techniques that enhance and stimulate the creation of knowledge. A logic base is designed containing three modules, namely an input module whereby concept maps are used to capture and structure knowledge entities. The second module consists of an analysis module where problem-solving can be done. The third module contains the output of work and processes where engineering knowledge can be documented for reuse. The contribution of this research lies in the design of an application in knowledge management in the field of civil engineering. Integration is done of ontologies, knowledge theories, knowledge acquisition and knowledge creation through problem-solving techniques. Knowledge is structured that can be linked to other external civil engineering taxonomies and ontologies. This enhancement of knowledge makes knowledge explicit and renders it suitable for reuse. When engineers are equipped in the use of the logic base, problems can be addressed in a holistic way and the underlying thought processes can be documented. This may be of great value to inexperienced engineers and for the preservation of valuable knowledge. Some case studies are analysed to demonstrate the functioning of the model. / Thesis (DPhil) University of Pretoria 2018. / Information Science / DPhil / Unrestricted

<p>Causality is central to scientific inquiry across many disciplines including epidemiology, medicine, and economics, to name a few. Researchers are usually interested not only in knowing how two events are correlated, but also in whether one causes the other and, if so, how. In general, the scientific practice seeks not just a surface description of the observed data, but rather deeper explanations, such as predicting the effects of interventions. The answer to such questions does not lie in the data alone and requires a qualitative understanding of the underlying data-generating process; a knowledge that is articulated in a causal diagram.</p> <p>And yet, delineating the true, underlying causal diagram requires knowledge and assumptions that are usually not available in many non-trivial and large-scale situations. Hence, this dissertation develops necessary theory and algorithms towards realizing a data-driven framework for causal inference. More specifically, this work provides fundamental treatments of the following research questions:</p> <p><br></p> <p><strong>Effect Identification under Markov Equivalence.</strong> One common task in many data sciences applications is to answer questions about the effect of new interventions, like: 'what would happen to <em>Y</em> while observing <em>Z=z</em> if we force <em>X</em> to take the value <em>x</em>?'. Formally, this is known as <em>causal effect identification</em>, where the goal is to determine whether a post-interventional distribution is computable from the combination of an observational distribution and assumptions about the underlying domain represented by a causal diagram. In this dissertation, we assume as the input of the task a less informative structure known as a partial ancestral graph (PAG), which represents a Markov equivalence class of causal diagrams, learnable from observational data. We develop tools and algorithms for this relaxed setting and characterize identifiable effects under necessary and sufficient conditions.</p> <p><br></p> <p><strong>Causal Discovery from Interventions.</strong> A causal diagram imposes constraints on the corresponding generated data; conditional independences are one such example. Given a mixture of observational and experimental data, the goal is to leverage the constraints imprinted in the data to infer the set of causal diagrams that are compatible with such constraints. In this work, we consider soft interventions, such that the mechanism of an intervened variable is modified without fully eliminating the effect of its direct causes, and investigate two settings where the targets of the interventions could be known or unknown to the data scientist. Accordingly, we introduce the first general graphical characterizations to test whether two causal diagrams are indistinguishable given the constraints in the available data. We also develop algorithms that, given a mixture of observational and interventional data, learn a representation of the equivalence class.</p>

The IEC Common Information Model (CIM) is of central importance in enabling smart grid interoperability. Its continual development aims to meet the needs of the smart grid for semantic understanding and knowledge representation for a widening domain of resources and processes. With smart grid evolution the importance of information and data management has become an increasingly pressing issue not only because far more data is being generated using modern sensing, control and measuring devices but also because information is now becoming recognised as the ‘integral component’ that facilitates the optimal flexibility required of the smart grid. This thesis looks at the impacts of CIM implementation upon the landscape of smart grid issues and presents research from within National Grid contributing to three key areas in support of further CIM deployment. Taking the issue of Enterprise Information Management first, an information management framework is presented for CIM deployment at National Grid. Following this the development and demonstration of a novel secure cloud computing platform to handle such information is described. Power system application (PSA) models of the grid are partial knowledge representations of a shared reality. To develop the completeness of our understanding of this reality it is necessary to combine these representations. The second research contribution reports on a novel methodology for a CIM-based model repository to align PSA representations and provide a knowledge resource for building utility business intelligence of the grid. The third contribution addresses the need for greater integration of information relating to energy storage, an essential aspect of smart energy management. It presents the strategic rationale for integrated energy modeling and a novel extension to the existing CIM standards for modeling grid-scale energy storage. Significantly, this work has already contributed to a larger body of work on modeling Distributed Energy Resources currently under development at the Electric Power Research Institute (EPRI) in the USA.