Download or read book Computational Complexity of some Optimization Problems in Planning written by Meysam Aghighi and published by Linköping University Electronic Press. This book was released on 2017-05-17 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automated planning is known to be computationally hard in the general case. Propositional planning is PSPACE-complete and first-order planning is undecidable. One method for analyzing the computational complexity of planning is to study restricted subsets of planning instances, with the aim of differentiating instances with varying complexity. We use this methodology for studying the computational complexity of planning. Finding new tractable (i.e. polynomial-time solvable) problems has been a particularly important goal for researchers in the area. The reason behind this is not only to differentiate between easy and hard planning instances, but also to use polynomial-time solvable instances in order to construct better heuristic functions and improve planners. We identify a new class of tractable cost-optimal planning instances by restricting the causal graph. We study the computational complexity of oversubscription planning (such as the net-benefit problem) under various restrictions and reveal strong connections with classical planning. Inspired by this, we present a method for compiling oversubscription planning problems into the ordinary plan existence problem. We further study the parameterized complexity of cost-optimal and net-benefit planning under the same restrictions and show that the choice of numeric domain for the action costs has a great impact on the parameterized complexity. We finally consider the parameterized complexity of certain problems related to partial-order planning. In some applications, less restricted plans than total-order plans are needed. Therefore, a partial-order plan is being used instead. When dealing with partial-order plans, one important question is how to achieve optimal partial order plans, i.e. having the highest degree of freedom according to some notion of flexibility. We study several optimization problems for partial-order plans, such as finding a minimum deordering or reordering, and finding the minimum parallel execution length.
Download or read book Computational Complexity written by Sanjeev Arora and published by Cambridge University Press. This book was released on 2009-04-20 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.
Download or read book ECAI 2006 written by Gerhard Brewka and published by IOS Press. This book was released on 2006 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Red Plenty written by Francis Spufford and published by Graywolf Press. This book was released on 2012-02-14 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Spufford cunningly maps out a literary genre of his own . . . Freewheeling and fabulous." —The Times (London) Strange as it may seem, the gray, oppressive USSR was founded on a fairy tale. It was built on the twentieth-century magic called "the planned economy," which was going to gush forth an abundance of good things that the lands of capitalism could never match. And just for a little while, in the heady years of the late 1950s, the magic seemed to be working. Red Plenty is about that moment in history, and how it came, and how it went away; about the brief era when, under the rash leadership of Khrushchev, the Soviet Union looked forward to a future of rich communists and envious capitalists, when Moscow would out-glitter Manhattan and every Lada would be better engineered than a Porsche. It's about the scientists who did their genuinely brilliant best to make the dream come true, to give the tyranny its happy ending. Red Plenty is history, it's fiction, it's as ambitious as Sputnik, as uncompromising as an Aeroflot flight attendant, and as different from what you were expecting as a glass of Soviet champagne.
Download or read book Completion of Ontologies and Ontology Networks written by Zlatan Dragisic and published by Linköping University Electronic Press. This book was released on 2017-08-22 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: The World Wide Web contains large amounts of data, and in most cases this data has no explicit structure. The lack of structure makes it difficult for automated agents to understand and use such data. A step towards a more structured World Wide Web is the Semantic Web, which aims at introducing semantics to data on the World Wide Web. One of the key technologies in this endeavour are ontologies, which provide a means for modeling a domain of interest and are used for search and integration of data. In recent years many ontologies have been developed. To be able to use multiple ontologies it is necessary to align them, i.e., find inter-ontology relationships. However, developing and aligning ontologies is not an easy task and it is often the case that ontologies and their alignments are incorrect and incomplete. This can be a problem for semantically-enabled applications. Incorrect and incomplete ontologies and alignments directly influence the quality of the results of such applications, as wrong results can be returned and correct results can be missed. This thesis focuses on the problem of completing ontologies and ontology networks. The contributions of the thesis are threefold. First, we address the issue of completing the is-a structure and alignment in ontologies and ontology networks. We have formalized the problem of completing the is-a structure in ontologies as an abductive reasoning problem and developed algorithms as well as systems for dealing with the problem. With respect to the completion of alignments, we have studied system performance in the Ontology Alignment Evaluation Initiative, a yearly evaluation campaign for ontology alignment systems. We have also addressed the scalability of ontology matching, which is one of the current challenges, by developing an approach for reducing the search space when generating the alignment.Second, high quality completion requires user involvement. As users' time and effort are a limited resource we address the issue of limiting and facilitating user interaction in the completion process. We have conducted a broad study of state-of-the-art ontology alignment systems and identified different issues related to the process. We have also conducted experiments to assess the impact of user errors in the completion process. While the completion of ontologies and ontology networks can be done at any point in the life-cycle of ontologies and ontology networks, some of the issues can be addressed already in the development phase. The third contribution of the thesis addresses this by introducing ontology completion and ontology alignment into an existing ontology development methodology.
Download or read book Beyond Recognition written by Le Minh-Ha and published by Linköping University Electronic Press. This book was released on 2024-05-06 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis addresses the need to balance the use of facial recognition systems with the need to protect personal privacy in machine learning and biometric identification. As advances in deep learning accelerate their evolution, facial recognition systems enhance security capabilities, but also risk invading personal privacy. Our research identifies and addresses critical vulnerabilities inherent in facial recognition systems, and proposes innovative privacy-enhancing technologies that anonymize facial data while maintaining its utility for legitimate applications. Our investigation centers on the development of methodologies and frameworks that achieve k-anonymity in facial datasets; leverage identity disentanglement to facilitate anonymization; exploit the vulnerabilities of facial recognition systems to underscore their limitations; and implement practical defenses against unauthorized recognition systems. We introduce novel contributions such as AnonFACES, StyleID, IdDecoder, StyleAdv, and DiffPrivate, each designed to protect facial privacy through advanced adversarial machine learning techniques and generative models. These solutions not only demonstrate the feasibility of protecting facial privacy in an increasingly surveilled world, but also highlight the ongoing need for robust countermeasures against the ever-evolving capabilities of facial recognition technology. Continuous innovation in privacy-enhancing technologies is required to safeguard individuals from the pervasive reach of digital surveillance and protect their fundamental right to privacy. By providing open-source, publicly available tools, and frameworks, this thesis contributes to the collective effort to ensure that advancements in facial recognition serve the public good without compromising individual rights. Our multi-disciplinary approach bridges the gap between biometric systems, adversarial machine learning, and generative modeling to pave the way for future research in the domain and support AI innovation where technological advancement and privacy are balanced.
Download or read book Gated Bayesian Networks written by Marcus Bendtsen and published by Linköping University Electronic Press. This book was released on 2017-06-08 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relationships among random variables, but they also allow for (potentially) fewer parameters to estimate, and enable more efficient inference. The random variables and the relationships among them decide the structure of the directed acyclic graph that represents the Bayesian network. It is the stasis over time of these two components that we question in this thesis. By introducing a new type of probabilistic graphical model, which we call gated Bayesian networks, we allow for the variables that we include in our model, and the relationships among them, to change overtime. We introduce algorithms that can learn gated Bayesian networks that use different variables at different times, required due to the process which we are modelling going through distinct phases. We evaluate the efficacy of these algorithms within the domain of algorithmic trading, showing how the learnt gated Bayesian networks can improve upon a passive approach to trading. We also introduce algorithms that detect changes in the relationships among the random variables, allowing us to create a model that consists of several Bayesian networks, thereby revealing changes and the structure by which these changes occur. The resulting models can be used to detect the currently most appropriate Bayesian network, and we show their use in real-world examples from both the domain of sports analytics and finance.
Download or read book Content Ontology Design Patterns Qualities Methods and Tools written by Karl Hammar and published by Linköping University Electronic Press. This book was released on 2017-09-06 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ontologies are formal knowledge models that describe concepts and relationships and enable data integration, information search, and reasoning. Ontology Design Patterns (ODPs) are reusable solutions intended to simplify ontology development and support the use of semantic technologies by ontology engineers. ODPs document and package good modelling practices for reuse, ideally enabling inexperienced ontologists to construct high-quality ontologies. Although ODPs are already used for development, there are still remaining challenges that have not been addressed in the literature. These research gaps include a lack of knowledge about (1) which ODP features are important for ontology engineering, (2) less experienced developers' preferences and barriers for employing ODP tooling, and (3) the suitability of the eXtreme Design (XD) ODP usage methodology in non-academic contexts. This dissertation aims to close these gaps by combining quantitative and qualitative methods, primarily based on five ontology engineering projects involving inexperienced ontologists. A series of ontology engineering workshops and surveys provided data about developer preferences regarding ODP features, ODP usage methodology, and ODP tooling needs. Other data sources are ontologies and ODPs published on the web, which have been studied in detail. To evaluate tooling improvements, experimental approaches provide data from comparison of new tools and techniques against established alternatives. The analysis of the gathered data resulted in a set of measurable quality indicators that cover aspects of ODP documentation, formal representation or axiomatisation, and usage by ontologists. These indicators highlight quality trade-offs: for instance, between ODP Learnability and Reusability, or between Functional Suitability and Performance Efficiency. Furthermore, the results demonstrate a need for ODP tools that support three novel property specialisation strategies, and highlight the preference of inexperienced developers for template-based ODP instantiation---neither of which are supported in prior tooling. The studies also resulted in improvements to ODP search engines based on ODP-specific attributes. Finally, the analysis shows that XD should include guidance for the developer roles and responsibilities in ontology engineering projects, suggestions on how to reuse existing ontology resources, and approaches for adapting XD to project-specific contexts.
Download or read book System Level Analysis and Design under Uncertainty written by Ivan Ukhov and published by Linköping University Electronic Press. This book was released on 2017-11-16 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: One major problem for the designer of electronic systems is the presence of uncertainty, which is due to phenomena such as process and workload variation. Very often, uncertainty is inherent and inevitable. If ignored, it can lead to degradation of the quality of service in the best case and to severe faults or burnt silicon in the worst case. Thus, it is crucial to analyze uncertainty and to mitigate its damaging consequences by designing electronic systems in such a way that they effectively and efficiently take uncertainty into account. We begin by considering techniques for deterministic system-level analysis and design of certain aspects of electronic systems. These techniques do not take uncertainty into account, but they serve as a solid foundation for those that do. Our attention revolves primarily around power and temperature, as they are of central importance for attaining robustness and energy efficiency. We develop a novel approach to dynamic steady-state temperature analysis of electronic systems and apply it in the context of reliability optimization. We then proceed to develop techniques that address uncertainty. The first technique is designed to quantify the variability of process parameters, which is induced by process variation, across silicon wafers based on indirect and potentially incomplete and noisy measurements. The second technique is designed to study diverse system-level characteristics with respect to the variability originating from process variation. In particular, it allows for analyzing transient temperature profiles as well as dynamic steady-state temperature profiles of electronic systems. This is illustrated by considering a problem of design-space exploration with probabilistic constraints related to reliability. The third technique that we develop is designed to efficiently tackle the case of sources of uncertainty that are less regular than process variation, such as workload variation. This technique is exemplified by analyzing the effect that workload units with uncertain processing times have on the timing-, power-, and temperature-related characteristics of the system under consideration. We also address the issue of runtime management of electronic systems that are subject to uncertainty. In this context, we perform an early investigation of the utility of advanced prediction techniques for the purpose of finegrained long-range forecasting of resource usage in large computer systems. All the proposed techniques are assessed by extensive experimental evaluations, which demonstrate the superior performance of our approaches to analysis and design of electronic systems compared to existing techniques.
Download or read book Robust Stream Reasoning Under Uncertainty written by Daniel de Leng and published by Linköping University Electronic Press. This book was released on 2019-11-08 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vast amounts of data are continually being generated by a wide variety of data producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, the ability to make sense of these streams of data through reasoning is of great importance. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in physical environments. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and their refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this work, we integrate techniques for logic-based stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over uncertain streaming data and the problem of robustly managing streaming data and their refinement. The main contributions of this work are (1) a logic-based temporal reasoning technique based on path checking under uncertainty that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt to situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in a case study on run-time adaptive reconfiguration. The results show that the proposed system - by combining reasoning over and reasoning about streams - can robustly perform stream reasoning, even when the availability of streaming resources changes.
Download or read book Studying Simulations with Distributed Cognition written by Jonas Rybing and published by Linköping University Electronic Press. This book was released on 2018-03-20 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simulations are frequently used techniques for training, performance assessment, and prediction of future outcomes. In this thesis, the term “human-centered simulation” is used to refer to any simulation in which humans and human cognition are integral to the simulation’s function and purpose (e.g., simulation-based training). A general problem for human-centered simulations is to capture the cognitive processes and activities of the target situation (i.e., the real world task) and recreate them accurately in the simulation. The prevalent view within the simulation research community is that cognition is internal, decontextualized computational processes of individuals. However, contemporary theories of cognition emphasize the importance of the external environment, use of tools, as well as social and cultural factors in cognitive practice. Consequently, there is a need for research on how such contemporary perspectives can be used to describe human-centered simulations, re-interpret theoretical constructs of such simulations, and direct how simulations should be modeled, designed, and evaluated. This thesis adopts distributed cognition as a framework for studying human-centered simulations. Training and assessment of emergency medical management in a Swedish context using the Emergo Train System (ETS) simulator was adopted as a case study. ETS simulations were studied and analyzed using the distributed cognition for teamwork (DiCoT) methodology with the goal of understanding, evaluating, and testing the validity of the ETS simulator. Moreover, to explore distributed cognition as a basis for simulator design, a digital re-design of ETS (DIGEMERGO) was developed based on the DiCoT analysis. The aim of the DIGEMERGO system was to retain core distributed cognitive features of ETS, to increase validity, outcome reliability, and to provide a digital platform for emergency medical studies. DIGEMERGO was evaluated in three separate studies; first, a usefulness, usability, and facevalidation study that involved subject-matter-experts; second, a comparative validation study using an expert-novice group comparison; and finally, a transfer of training study based on self-efficacy and management performance. Overall, the results showed that DIGEMERGO was perceived as a useful, immersive, and promising simulator – with mixed evidence for validity – that demonstrated increased general self-efficacy and management performance following simulation exercises. This thesis demonstrates that distributed cognition, using DiCoT, is a useful framework for understanding, designing and evaluating simulated environments. In addition, the thesis conceptualizes and re-interprets central constructs of human-centered simulation in terms of distributed cognition. In doing so, the thesis shows how distributed cognitive processes relate to validity, fidelity, functionality, and usefulness of human-centered simulations. This thesis thus provides a new understanding of human-centered simulations that is grounded in distributed cognition theory.
Download or read book Scalable and Efficient Probabilistic Topic Model Inference for Textual Data written by Måns Magnusson and published by Linköping University Electronic Press. This book was released on 2018-04-27 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic topic models have proven to be an extremely versatile class of mixed-membership models for discovering the thematic structure of text collections. There are many possible applications, covering a broad range of areas of study: technology, natural science, social science and the humanities. In this thesis, a new efficient parallel Markov Chain Monte Carlo inference algorithm is proposed for Bayesian inference in large topic models. The proposed methods scale well with the corpus size and can be used for other probabilistic topic models and other natural language processing applications. The proposed methods are fast, efficient, scalable, and will converge to the true posterior distribution. In addition, in this thesis a supervised topic model for high-dimensional text classification is also proposed, with emphasis on interpretable document prediction using the horseshoe shrinkage prior in supervised topic models. Finally, we develop a model and inference algorithm that can model agenda and framing of political speeches over time with a priori defined topics. We apply the approach to analyze the evolution of immigration discourse in the Swedish parliament by combining theory from political science and communication science with a probabilistic topic model. Probabilistiska ämnesmodeller (topic models) är en mångsidig klass av modeller för att estimera ämnessammansättningar i större corpusar. Applikationer finns i ett flertal vetenskapsområden som teknik, naturvetenskap, samhällsvetenskap och humaniora. I denna avhandling föreslås nya effektiva och parallella Markov Chain Monte Carlo algoritmer för Bayesianska ämnesmodeller. De föreslagna metoderna skalar väl med storleken på corpuset och kan användas för flera olika ämnesmodeller och liknande modeller inom språkteknologi. De föreslagna metoderna är snabba, effektiva, skalbara och konvergerar till den sanna posteriorfördelningen. Dessutom föreslås en ämnesmodell för högdimensionell textklassificering, med tonvikt på tolkningsbar dokumentklassificering genom att använda en kraftigt regulariserande priorifördelningar. Slutligen utvecklas en ämnesmodell för att analyzera "agenda" och "framing" för ett förutbestämt ämne. Med denna metod analyserar vi invandringsdiskursen i Sveriges Riksdag över tid, genom att kombinera teori från statsvetenskap, kommunikationsvetenskap och probabilistiska ämnesmodeller.
Download or read book Machine Learning Based Bug Handling in Large Scale Software Development written by Leif Jonsson and published by Linköping University Electronic Press. This book was released on 2018-05-17 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis investigates the possibilities of automating parts of the bug handling process in large-scale software development organizations. The bug handling process is a large part of the mostly manual, and very costly, maintenance of software systems. Automating parts of this time consuming and very laborious process could save large amounts of time and effort wasted on dealing with bug reports. In this thesis we focus on two aspects of the bug handling process, bug assignment and fault localization. Bug assignment is the process of assigning a newly registered bug report to a design team or developer. Fault localization is the process of finding where in a software architecture the fault causing the bug report should be solved. The main reason these tasks are not automated is that they are considered hard to automate, requiring human expertise and creativity. This thesis examines the possi- bility of using machine learning techniques for automating at least parts of these processes. We call these automated techniques Automated Bug Assignment (ABA) and Automatic Fault Localization (AFL), respectively. We treat both of these problems as classification problems. In ABA, the classes are the design teams in the development organization. In AFL, the classes consist of the software components in the software architecture. We focus on a high level fault localization that it is suitable to integrate into the initial support flow of large software development organizations. The thesis consists of six papers that investigate different aspects of the AFL and ABA problems. The first two papers are empirical and exploratory in nature, examining the ABA problem using existing machine learning techniques but introducing ensembles into the ABA context. In the first paper we show that, like in many other contexts, ensembles such as the stacked generalizer (or stacking) improves classification accuracy compared to individual classifiers when evaluated using cross fold validation. The second paper thor- oughly explore many aspects such as training set size, age of bug reports and different types of evaluation of the ABA problem in the context of stacking. The second paper also expands upon the first paper in that the number of industry bug reports, roughly 50,000, from two large-scale industry software development contexts. It is still as far as we are aware, the largest study on real industry data on this topic to this date. The third and sixth papers, are theoretical, improving inference in a now classic machine learning tech- nique for topic modeling called Latent Dirichlet Allocation (LDA). We show that, unlike the currently dominating approximate approaches, we can do parallel inference in the LDA model with a mathematically correct algorithm, without sacrificing efficiency or speed. The approaches are evaluated on standard research datasets, measuring various aspects such as sampling efficiency and execution time. Paper four, also theoretical, then builds upon the LDA model and introduces a novel supervised Bayesian classification model that we call DOLDA. The DOLDA model deals with both textual content and, structured numeric, and nominal inputs in the same model. The approach is evaluated on a new data set extracted from IMDb which have the structure of containing both nominal and textual data. The model is evaluated using two approaches. First, by accuracy, using cross fold validation. Second, by comparing the simplicity of the final model with that of other approaches. In paper five we empirically study the performance, in terms of prediction accuracy, of the DOLDA model applied to the AFL problem. The DOLDA model was designed with the AFL problem in mind, since it has the exact structure of a mix of nominal and numeric inputs in combination with unstructured text. We show that our DOLDA model exhibits many nice properties, among others, interpretability, that the research community has iden- tified as missing in current models for AFL.
Download or read book Empirical Studies in Machine Psychology written by Robert Johansson and published by Linköping University Electronic Press. This book was released on 2024-10-09 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents Machine Psychology as an interdisciplinary paradigm that integrates learning psychology principles with an adaptive computer system for the development of Artificial General Intelligence (AGI). By synthesizing behavioral psychology with a formal intelligence model, the Non-Axiomatic Reasoning System (NARS), this work explores the potential of operant conditioning paradigms to advance AGI research. The thesis begins by introducing the conceptual foundations of Machine Psychology, detailing its alignment with the theoretical constructs of learning psychology and the formalism of NARS. It then progresses through a series of empirical studies designed to systematically investigate the emergence of increasingly complex cognitive behaviors as NARS interacts with its environment. Initially, operant conditioning is established as a foundational principle for developing adaptive behavior with NARS. Subsequent chapters explore increasingly sophisticated cognitive capabilities, all studied with NARS using experimental paradigms from operant learning psychology: Generalized identity matching, Functional equivalence, and Arbitrarily Applicable Relational Responding. Throughout this research, Machine Psychology is demonstrated to be a promising framework for guiding AGI research, allowing both the manipulation of environmental contingencies and the system’s intrinsic logical processes. The thesis contributes to AGI research by showing how using operant psychological paradigms with NARS can enable cognitive abilities similar to human cognition. These findings set the stage for AGI systems that learn and adapt more like humans, potentially advancing the creation of more general and flexible AI. Denna avhandling introducerar Maskinpsykologi som ett tvärvetenskapligt område där principer från inlärningspsykologi integreras med ett adaptivt datorsystem. Genom att kombinera forskning från beteendepsykologi med en formell modell för intelligens (Non-Axiomatic Reasoning System; NARS), undersöker avhandlingen hur operant betingning kan användas för att driva utvecklingen av Artificiell General Intelligens (AGI) framåt. Avhandlingen börjar med att förklara grunderna i Maskinpsykologi och hur dessa relaterar till både inlärningspsykologi och NARS. Därefter presenteras en serie experiment som systematiskt undersöker hur allt mer komplexa kognitiva beteenden kan uppstå när NARS interagerar med sin omgivning. Till att börja med etableras operant betingning som en central metod för att utveckla adaptiva beteenden med NARS. I de följande kapitlen utforskas hur NARS, genom experiment inspirerade av operant inlärningspsykologi, kan utveckla mer avancerade kognitiva förmågor som till exempel generaliserad identitetsmatchning, funktionell ekvivalens och så kallade arbiträrt applicerbara relationsresponser. Denna forskning visar att Maskinpsykologi är ett lovande verktyg för att vägleda AGI-forskning, eftersom det möjliggör att både påverka omgivningsfaktorer och styra systemets interna logiska processer. Avhandlingen bidrar till AGI-forskning genom att visa hur operanta psykologiska metoder, tillämpade på NARS, kan möjliggöra kognitiva förmågor som liknar mänskligt tänkande. Dessa insikter öppnar nya möjligheter för att utveckla AI-system som kan lära sig och anpassa sig på ett mer mänskligt sätt, vilket kan leda till skapandet av mer generell och flexibel AI.
Download or read book Designing for Resilience written by Vanessa Rodrigues and published by Linköping University Electronic Press. This book was released on 2020-05-05 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Services are prone to change in the form of expected and unexpected variations and disruptions, more so given the increasing interconnectedness and complexity of service systems today. These changes require service systems to be resilient and designed to adapt, to ensure that services continue to work smoothly. This thesis problematises the prevailing view and assumptions underpinning the current understanding of resilience in services. Drawing on literature from service management, service design, systems thinking and social-ecological resilience theory, this work investigates how service design can foster resilience in service systems. Supported by empirical input from three research projects in healthcare, the findings show service design can contribute to the adaptability and transformability of service systems through its holistic, human-centred, participatory and experimental approaches. Through the analysis, this research identifies key intervention points for cultivating service systems resilience through service design, including the design of service interactions, processes, enabling structures and multi-level governance. The study makes two important contributions. First, it extends the understanding of service systems resilience as the collective capacity for intentional action in responding to ongoing change, coordinated across scales in order to create value. This is supported by offering alternative assumptions about resilience in service. Second, it positions service design as an enabler of service resilience by explicitly linking design practice(s) to processes that contribute to resilience. By extending the understanding of service systems resilience, this thesis lays the groundwork for future research at the intersection of service design, systemic change and resilience.
Download or read book Fostering User Involvement in Ontology Alignment and Alignment Evaluation written by Valentina Ivanova and published by Linköping University Electronic Press. This book was released on 2018-01-04 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: The abundance of data at our disposal empowers data-driven applications and decision making. The knowledge captured in the data, however, has not been utilized to full potential, as it is only accessible to human interpretation and data are distributed in heterogeneous repositories. Ontologies are a key technology unlocking the knowledge in the data by providing means to model the world around us and infer knowledge implicitly captured in the data. As data are hosted by independent organizations we often need to use several ontologies and discover the relationships between them in order to support data and knowledge transfer. Broadly speaking, while ontologies provide formal representations and thus the basis, ontology alignment supplies integration techniques and thus the means to turn the data kept in distributed, heterogeneous repositories into valuable knowledge. While many automatic approaches for creating alignments have already been developed, user input is still required for obtaining the highest-quality alignments. This thesis focuses on supporting users during the cognitively intensive alignment process and makes several contributions. We have identified front- and back-end system features that foster user involvement during the alignment process and have investigated their support in existing systems by user interface evaluations and literature studies. We have further narrowed down our investigation to features in connection to the, arguably, most cognitively demanding task from the users’ perspective—manual validation—and have also considered the level of user expertise by assessing the impact of user errors on alignments’ quality. As developing and aligning ontologies is an error-prone task, we have focused on the benefits of the integration of ontology alignment and debugging. We have enabled interactive comparative exploration and evaluation of multiple alignments at different levels of detail by developing a dedicated visual environment—Alignment Cubes—which allows for alignments’ evaluation even in the absence of reference alignments. Inspired by the latest technological advances we have investigated and identified three promising directions for the application of large, high-resolution displays in the field: improving the navigation in the ontologies and their alignments, supporting reasoning and collaboration between users.
