Relevant bibliographies by topics / Algorithmic knowledge

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Algorithmic knowledge'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Algorithmic knowledge"

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Surowik, Dariusz. "Logic of Algorithmic Knowledge." Studies in Logic, Grammar and Rhetoric 42, no. 1 (September 1, 2015): 163–72. http://dx.doi.org/10.1515/slgr-2015-0035.

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Abstract In this paper we consider the construction of a LAK system of temporal-epistemic logic which is used to formally describe algorithmic knowledge. We propose an axiom system of LAK and discuss the basic properties of this logic.
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Pucella, Riccardo. "Deductive Algorithmic Knowledge." Journal of Logic and Computation 16, no. 2 (April 1, 2006): 287–309. http://dx.doi.org/10.1093/logcom/exi078.

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Anderson, John R. "Methodologies for studying human knowledge." Behavioral and Brain Sciences 10, no. 3 (September 1987): 467–77. http://dx.doi.org/10.1017/s0140525x00023554.

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AbstractThe appropriate methodology for psychological research depends on whether one is studying mental algorithms or their implementation. Mental algorithms are abstract specifications of the steps taken by procedures that run in the mind. Implementational issues concern the speed and reliability of these procedures. The algorithmic level can be explored only by studying across-task variation. This contrasts with psychology's dominant methodology of looking for within-task generalities, which is appropriate only for studying implementational issues.The implementation-algorithm distinction is related to a number of other “levels” considered in cognitive science. Its realization in Anderson's ACT theory of cognition is discussed. Research at the algorithmic level is more promising because it is hard to make further fundamental scientific progress at the implementational level with the methodologies available. Protocol data, which are appropriate only for algorithm-level theories, provide a richer source than data at the implementational level. Research at the algorithmic level will also yield more insight into fundamental properties of human knowledge because it is the level at which significant learning transitions are defined.The best way to study the algorithmic level is to look for differential learning outcomes in pedagogical experiments that manipulate instructional experience. This provides control and prediction in realistically complex learning situations. The intelligent tutoring paradigm provides a particularly fruitful way to implement such experiments.The implications of this analysis for the issue of modularity of mind, the status of language, research on human/computer interaction, and connectionist models are also examined.
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Carlson, Matt. "Automating judgment? Algorithmic judgment, news knowledge, and journalistic professionalism." New Media & Society 20, no. 5 (May 22, 2017): 1755–72. http://dx.doi.org/10.1177/1461444817706684.

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Journalistic judgment is both a central and fraught function of journalism. The privileging of objectivity norms and the externalization of newsworthiness in discourses about journalism leave little room for the legitimation of journalists’ subjective judgment. This tension has become more apparent in the digital news era due to the growing use of algorithms in automated news distribution and production. This article argues that algorithmic judgment should be considered distinct from journalists’ professional judgment. Algorithmic judgment presents a fundamental challenge to news judgment based on the twin beliefs that human subjectivity is inherently suspect and in need of replacement, while algorithms are inherently objective and in need of implementation. The supplanting of human judgment with algorithmic judgment has significant consequences for both the shape of news and its legitimating discourses.
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Amoore, Louise, and Rita Raley. "Securing with algorithms: Knowledge, decision, sovereignty." Security Dialogue 48, no. 1 (December 12, 2016): 3–10. http://dx.doi.org/10.1177/0967010616680753.

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Amid the deployment of algorithmic techniques for security – from the gathering of intelligence data to the proliferation of smart borders and predictive policing – what are the political and ethical stakes involved in securing with algorithms? Taking seriously the generative and world-making capacities of contemporary algorithms, this special issue draws attention to the embodied actions of algorithms as they extend cognition, agency and responsibility beyond the conventional sites of the human, the state and sovereignty. Though focusing on different modes of algorithmic security, each of the contributions to the special issue shares a concern with what it means to claim security on the terrain of incalculable and uncertain futures. To secure with algorithms is to reorient the embodied relation to uncertainty, so that human and non-human cognitive beings experimentally generate and learn what to bring to the surface of attention for a security action.
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Monahan, Torin. "Algorithmic Fetishism." Surveillance & Society 16, no. 1 (April 1, 2018): 1–5. http://dx.doi.org/10.24908/ss.v16i1.10827.

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Surveillance-infused forms of algorithmic discrimination are beginning to capture public and scholarly attention. While this is an encouraging development, this editorial questions the parameters of this emerging discussion and cautions against algorithmic fetishism. I characterize algorithmic fetishism as the pleasurable pursuit of opening the black box, discovering the code hidden inside, exploring its beauty and flaws, and explicating its intricacies. It is a technophilic desire for arcane knowledge that can never be grasped completely, so it continually lures one forward into technical realms while deferring the point of intervention. The editorial concludes with a review of the articles in this open issue.
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LOMUSCIO, ALESSIO, and MARK RYAN. "An algorithmic approach to knowledge evolution." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, no. 2 (April 1999): 119–32. http://dx.doi.org/10.1017/s0890060499132062.

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Intelligent agents must update their knowledge base as they acquire new information about their environment. The modal logic S5n has been designed for representing knowledge bases in societies of agents. Halpern and Vardi have proposed the notion of refinement of S5n Kripke models in order to solve multi-agent problems in which knowledge evolves. We argue that there are some problems with their proposal and attempt to solve them by moving from Kripke models to their corresponding trees. We define refinement of a tree with a formula, show some properties of the notion, and illustrate with the muddy children puzzle. We show how some diagnosis problems in engineering can be modelled as knowledge-based multi-agent systems, and hence how our approach can address them.
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Yakubjanovna, Turaboeva Muqaddas. "Developing Linguistic Competence Through Algorithmic Exercises In 5th Grade Mother Tongue Lessons." American Journal of Social Science and Education Innovations 02, no. 12 (December 28, 2020): 235–39. http://dx.doi.org/10.37547/tajssei/volume02issue12-42.

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This article discusses algorithmic exercises as a means of increasing the effectiveness of 5th grade mother tongue teaching. In particular, it is scientifically based on the development of pupils' knowledge of linguistic competence through the performance of algorithmic exercises.
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Holen, A. T., A. Botnen, P. Stoa, and J. J. Keronen. "Coupling between knowledge-based and algorithmic methods." Proceedings of the IEEE 80, no. 5 (May 1992): 745–57. http://dx.doi.org/10.1109/5.137229.

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Bhattarai, Pratistha. "Algorithmic Value: Cultural Encoding, Textuality, and the Myth of “Source Code”." Catalyst: Feminism, Theory, Technoscience 3, no. 1 (October 19, 2017): 1–28. http://dx.doi.org/10.28968/cftt.v3i1.28786.

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In this article, I provide a Spivakian analysis of computational algorithms. Building upon Gayatri Spivak’s claim that the value coding of late capitalism extends beyond the economic realm to the cultural and affective, I show that it seeps into the algorithmic as well. The recent proliferation of algorithmic applications has been met by an increased scholarly interest in their underlying mechanisms. Several critics of predictive algorithms, for instance, proceed as though the racial and gender discrimination that a given algorithm enacts upon execution can be positively attributed to -- and mitigated through a re-coding of – either its training data or its “source code.” There is little denying that the logic of computation undergirds much of our sociality. But, as I argue in this article, to concentrate the source of an algorithm’s action in its semiotic representations is to hide and legitimize the value codings that lend these representations their efficacy. My aim in this article is two-fold. First, to show how seemingly benign investments in algorithms can reproduce, in a larger network, the exploitative value systems that manage the worth of knowledge, epistemologies, labor, and bodies. Second, to raise a question of methodology: What antitechnocratic, nonhegemonic engagements with algorithms might feminists produce that do not privilege the algorithmic as a site of intervention
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Dissertations / Theses on the topic "Algorithmic knowledge"

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Hartland, Joanne. "The machinery of medicine : an analysis of algorithmic approaches to medical knowledge and practice." Thesis, University of Bath, 1993. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357868.

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Sjö, Kristoffer. "Semantics and Implementation of Knowledge Operators in Approximate Databases." Thesis, Linköping University, Department of Computer and Information Science, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2438.

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In order that epistemic formulas might be coupled with approximate databases, it is necessary to have a well-defined semantics for the knowledge operator and a method of reducing epistemic formulas to approximate formulas. In this thesis, two possible definitions of a semantics for the knowledge operator are proposed for use together with an approximate relational database:

* One based upon logical entailment (being the dominating notion of knowledge in literature); sound and complete rules for reduction to approximate formulas are explored and found not to be applicable to all formulas.

* One based upon algorithmic computability (in order to be practically feasible); the correspondence to the above operator on the one hand, and to the deductive capability of the agent on the other hand, is explored.

Also, an inductively defined semantics for a"know whether"-operator, is proposed and tested. Finally, an algorithm implementing the above is proposed, carried out using Java, and tested.

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Hawasly, Majd. "Policy space abstraction for a lifelong learning agent." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9931.

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This thesis is concerned with policy space abstractions that concisely encode alternative ways of making decisions; dealing with discovery, learning, adaptation and use of these abstractions. This work is motivated by the problem faced by autonomous agents that operate within a domain for long periods of time, hence having to learn to solve many different task instances that share some structural attributes. An example of such a domain is an autonomous robot in a dynamic domestic environment. Such environments raise the need for transfer of knowledge, so as to eliminate the need for long learning trials after deployment. Typically, these tasks would be modelled as sequential decision making problems, including path optimisation for navigation tasks, or Markov Decision Process models for more general tasks. Learning within such models often takes the form of online learning or reinforcement learning. However, handling issues such as knowledge transfer and multiple task instances requires notions of structure and hierarchy, and that raises several questions that form the topic of this thesis – (a) can an agent acquire such hierarchies in policies in an online, incremental manner, (b) can we devise mathematically rigorous ways to abstract policies based on qualitative attributes, (c) when it is inconvenient to employ prolonged trial and error learning, can we devise alternate algorithmic methods for decision making in a lifelong setting? The first contribution of this thesis is an algorithmic method for incrementally acquiring hierarchical policies. Working with the framework of options - temporally extended actions - in reinforcement learning, we present a method for discovering persistent subtasks that define useful options for a particular domain. Our algorithm builds on a probabilistic mixture model in state space to define a generalised and persistent form of ‘bottlenecks’, and suggests suitable policy fragments to make options. In order to continuously update this hierarchy, we devise an incremental process which runs in the background and takes care of proposing and forgetting options. We evaluate this framework in simulated worlds, including the RoboCup 2D simulation league domain. The second contribution of this thesis is in defining abstractions in terms of equivalence classes of trajectories. Utilising recently developed techniques from computational topology, in particular the concept of persistent homology, we show that a library of feasible trajectories could be retracted to representative paths that may be sufficient for reasoning about plans at the abstract level. We present a complete framework, starting from a novel construction of a simplicial complex that describes higher-order connectivity properties of a spatial domain, to methods for computing the homology of this complex at varying resolutions. The resulting abstractions are motion primitives that may be used as topological options, contributing a novel criterion for option discovery. This is validated by experiments in simulated 2D robot navigation, and in manipulation using a physical robot platform. Finally, we develop techniques for solving a family of related, but different, problem instances through policy reuse of a finite policy library acquired over the agent’s lifetime. This represents an alternative approach when traditional methods such as hierarchical reinforcement learning are not computationally feasible. We abstract the policy space using a non-parametric model of performance of policies in multiple task instances, so that decision making is posed as a Bayesian choice regarding what to reuse. This is one approach to transfer learning that is motivated by the needs of practical long-lived systems. We show the merits of such Bayesian policy reuse in simulated real-time interactive systems, including online personalisation and surveillance.
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Chen, Hsinchun, and Tobun Dorbin Ng. "An Algorithmic Approach to Concept Exploration in a Large Knowledge Network (Automatic Thesaurus Consultation): Symbolic Branch-and-Bound Search vs. Connectionist Hopfield Net Activation." Wiley Periodicals, Inc, 1995. http://hdl.handle.net/10150/105241.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
This paper presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge-based systems and to alleviate the limitations of the manual browsing approach, we have developed two spreading activation-based algorithms for concept exploration in large, heterogeneous networks of concepts (e.g., multiple thesauri). One algorithm, which is based on the symbolic Al paradigm, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The second algorithm, which is based on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify â convergentâ concepts for some initial queries (a parallel, heuristic search process). Both algorithms can be adopted for automatic, multiple-thesauri consultation. We tested these two algorithms on a large text-based knowledge network of about 13,000 nodes (terms) and 80,000 directed links in the area of computing technologies. This knowledge network was created from two external thesauri and one automatically generated thesaurus. We conducted experiments to compare the behaviors and performances of the two algorithms with the hypertext-like browsing process. Our experiment revealed that manual browsing achieved higher-term recall but lower-term precision in comparison to the algorithmic systems. However, it was also a much more laborious and cognitively demanding process. In document retrieval, there were no statistically significant differences in document recall and precision between the algorithms and the manual browsing process. In light of the effort required by the manual browsing process, our proposed algorithmic approach presents a viable option for efficiently traversing largescale, multiple thesauri (knowledge network).
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Goyder, Matthew. "Knowledge Accelerated Algorithms and the Knowledge Cache." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1339763385.

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Harispe, Sébastien. "Knowledge-based Semantic Measures : From Theory to Applications." Thesis, Montpellier 2, 2014. http://www.theses.fr/2014MON20038/document.

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Les notions de proximité, de distance et de similarité sémantiques sont depuis longtemps jugées essentielles dans l'élaboration de nombreux processus cognitifs et revêtent donc un intérêt majeur pour les communautés intéressées au développement d'intelligences artificielles. Cette thèse s'intéresse aux différentes mesures sémantiques permettant de comparer des unités lexicales, des concepts ou des instances par l'analyse de corpus de textes ou de représentations de connaissance (e.g. ontologies). Encouragées par l'essor des technologies liées à l'Ingénierie des Connaissances et au Web sémantique, ces mesures suscitent de plus en plus d'intérêt à la fois dans le monde académique et industriel. Ce manuscrit débute par un vaste état de l'art qui met en regard des travaux publiés dans différentes communautés et souligne l'aspect interdisciplinaire et la diversité des recherches actuelles dans ce domaine. Cela nous a permis, sous l'apparente hétérogénéité des mesures existantes, de distinguer certaines propriétés communes et de présenter une classification générale des approches proposées. Par la suite, ces travaux se concentrent sur les mesures qui s'appuient sur une structuration de la connaissance sous forme de graphes sémantiques, e.g. graphes RDF(S). Nous montrons que ces mesures reposent sur un ensemble réduit de primitives abstraites, et que la plupart d'entre elles, bien que définies indépendamment dans la littérature, ne sont que des expressions particulières de mesures paramétriques génériques. Ce résultat nous a conduits à définir un cadre théorique unificateur pour les mesures sémantiques. Il permet notamment : (i) d'exprimer de nouvelles mesures, (ii) d'étudier les propriétés théoriques des mesures et (iii) d'orienter l'utilisateur dans le choix d'une mesure adaptée à sa problématique. Les premiers cas concrets d'utilisation de ce cadre démontrent son intérêt en soulignant notamment qu'il permet l'analyse théorique et empirique des mesures avec un degré de détail particulièrement fin, jamais atteint jusque-là. Plus généralement, ce cadre théorique permet de poser un regard neuf sur ce domaine et ouvre de nombreuses perspectives prometteuses pour l'analyse des mesures sémantiques. Le domaine des mesures sémantiques souffre d'un réel manque d'outils logiciels génériques et performants ce qui complique à la fois l'étude et l'utilisation de ces mesures. En réponse à ce manque, nous avons développé la Semantic Measures Library (SML), une librairie logicielle dédiée au calcul et à l'analyse des mesures sémantiques. Elle permet d'utiliser des centaines de mesures issues à la fois de la littérature et des fonctions paramétriques étudiées dans le cadre unificateur introduit. Celles-ci peuvent être analysées et comparées à l'aide des différentes fonctionnalités proposées par la librairie. La SML s'accompagne d'une large documentation, d'outils logiciels permettant son utilisation par des non informaticiens, d'une liste de diffusion, et de façon plus large, se propose de fédérer les différentes communautés du domaine afin de créer une synergie interdisciplinaire autour la notion de mesures sémantiques : http://www.semantic-measures-library.org Cette étude a également conduit à différentes contributions algorithmiques et théoriques, dont (i) la définition d'une méthode innovante pour la comparaison d'instances définies dans un graphe sémantique – nous montrons son intérêt pour la mise en place de système de recommandation à base de contenu, (ii) une nouvelle approche pour comparer des concepts représentés dans des taxonomies chevauchantes, (iii) des optimisations algorithmiques pour le calcul de certaines mesures sémantiques, et (iv) une technique d'apprentissage semi-supervisée permettant de cibler les mesures sémantiques adaptées à un contexte applicatif particulier en prenant en compte l'incertitude associée au jeu de test utilisé. Travaux validés par plusieurs publications et communications nationales et internationales
The notions of semantic proximity, distance, and similarity have long been considered essential for the elaboration of numerous cognitive processes, and are therefore of major importance for the communities involved in the development of artificial intelligence. This thesis studies the diversity of semantic measures which can be used to compare lexical entities, concepts and instances by analysing corpora of texts and knowledge representations (e.g., ontologies). Strengthened by the development of Knowledge Engineering and Semantic Web technologies, these measures are arousing increasing interest in both academic and industrial fields.This manuscript begins with an extensive state-of-the-art which presents numerous contributions proposed by several communities, and underlines the diversity and interdisciplinary nature of this domain. Thanks to this work, despite the apparent heterogeneity of semantic measures, we were able to distinguish common properties and therefore propose a general classification of existing approaches. Our work goes on to look more specifically at measures which take advantage of knowledge representations expressed by means of semantic graphs, e.g. RDF(S) graphs. We show that these measures rely on a reduced set of abstract primitives and that, even if they have generally been defined independently in the literature, most of them are only specific expressions of generic parametrised measures. This result leads us to the definition of a unifying theoretical framework for semantic measures, which can be used to: (i) design new measures, (ii) study theoretical properties of measures, (iii) guide end-users in the selection of measures adapted to their usage context. The relevance of this framework is demonstrated in its first practical applications which show, for instance, how it can be used to perform theoretical and empirical analyses of measures with a previously unattained level of detail. Interestingly, this framework provides a new insight into semantic measures and opens interesting perspectives for their analysis.Having uncovered a flagrant lack of generic and efficient software solutions dedicated to (knowledge-based) semantic measures, a lack which clearly hampers both the use and analysis of semantic measures, we consequently developed the Semantic Measures Library (SML): a generic software library dedicated to the computation and analysis of semantic measures. The SML can be used to take advantage of hundreds of measures defined in the literature or those derived from the parametrised functions introduced by the proposed unifying framework. These measures can be analysed and compared using the functionalities provided by the library. The SML is accompanied by extensive documentation, community support and software solutions which enable non-developers to take full advantage of the library. In broader terms, this project proposes to federate the several communities involved in this domain in order to create an interdisciplinary synergy around the notion of semantic measures: http://www.semantic-measures-library.org This thesis also presents several algorithmic and theoretical contributions related to semantic measures: (i) an innovative method for the comparison of instances defined in a semantic graph – we underline in particular its benefits in the definition of content-based recommendation systems, (ii) a new approach to compare concepts defined in overlapping taxonomies, (iii) algorithmic optimisation for the computation of a specific type of semantic measure, and (iv) a semi-supervised learning-technique which can be used to identify semantic measures adapted to a specific usage context, while simultaneously taking into account the uncertainty associated to the benchmark in use. These contributions have been validated by several international and national publications
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何淑瑩 and Shuk-ying Ho. "Knowledge representation with genetic algorithms." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31222638.

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Ho, Shuk-ying. "Knowledge representation with genetic algorithms /." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22030256.

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Correa, Leonardo de Lima. "Uma proposta de algoritmo memético baseado em conhecimento para o problema de predição de estruturas 3-D de proteínas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/156640.

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Algoritmos meméticos são meta-heurísticas evolutivas voltadas intrinsecamente à exploração e incorporação de conhecimentos relacionados ao problema em estudo. Nesta dissertação, foi proposto um algoritmo memético multi populacional baseado em conhecimento para lidar com o problema de predição de estruturas tridimensionais de proteínas voltado à modelagem de estruturas livres de similaridades conformacionais com estruturas de proteínas determinadas experimentalmente. O algoritmo em questão, foi estruturado em duas etapas principais de processamento: (i) amostragem e inicialização de soluções; e (ii) otimização dos modelos estruturais provenientes da etapa anterior. A etapa I objetiva a geração e classificação de diversas soluções, a partir da estratégia Lista de Probabilidades Angulares, buscando a definição de diferentes grupos estruturais e a criação de melhores estruturas a serem incorporadas à meta-heurística como soluções iniciais das multi populações. A segunda etapa consiste no processo de otimização das estruturas oriundas da etapa I, realizado por meio da aplicação do algoritmo memético de otimização, o qual é fundamentado na organização da população de indivíduos em uma estrutura em árvore, onde cada nodo pode ser interpretado como uma subpopulação independente, que ao longo do processo interage com outros nodos por meio de operações de busca global voltadas a características do problema, visando o compartilhamento de informações, a diversificação da população de indivíduos, e a exploração mais eficaz do espaço de busca multimodal do problema O algoritmo engloba ainda uma implementação do algoritmo colônia artificial de abelhas, com o propósito de ser utilizado como uma técnica de busca local a ser aplicada em cada nodo da árvore. O algoritmo proposto foi testado em um conjunto de 24 sequências de aminoácidos, assim como comparado a dois métodos de referência na área de predição de estruturas tridimensionais de proteínas, Rosetta e QUARK. Os resultados obtidos mostraram a capacidade do método em predizer estruturas tridimensionais de proteínas com conformações similares a estruturas determinadas experimentalmente, em termos das métricas de avaliação estrutural Root-Mean-Square Deviation e Global Distance Total Score Test. Verificou-se que o algoritmo desenvolvido também foi capaz de atingir resultados comparáveis ao Rosetta e ao QUARK, sendo que em alguns casos, os superou. Corroborando assim, a eficácia do método.
Memetic algorithms are evolutionary metaheuristics intrinsically concerned with the exploiting and incorporation of all available knowledge about the problem under study. In this dissertation, we present a knowledge-based memetic algorithm to tackle the threedimensional protein structure prediction problem without the explicit use of template experimentally determined structures. The algorithm was divided into two main steps of processing: (i) sampling and initialization of the algorithm solutions; and (ii) optimization of the structural models from the previous stage. The first step aims to generate and classify several structural models for a determined target protein, by the use of the strategy Angle Probability List, aiming the definition of different structural groups and the creation of better structures to initialize the initial individuals of the memetic algorithm. The Angle Probability List takes advantage of structural knowledge stored in the Protein Data Bank in order to reduce the complexity of the conformational search space. The second step of the method consists in the optimization process of the structures generated in the first stage, through the applying of the proposed memetic algorithm, which uses a tree-structured population, where each node can be seen as an independent subpopulation that interacts with others, over global search operations, aiming at information sharing, population diversity, and better exploration of the multimodal search space of the problem The method also encompasses ad-hoc global search operators, whose objective is to increase the exploration capacity of the method turning to the characteristics of the protein structure prediction problem, combined with the Artificial Bee Colony algorithm to be used as a local search technique applied to each node of the tree. The proposed algorithm was tested on a set of 24 amino acid sequences, as well as compared with two reference methods in the protein structure prediction area, Rosetta and QUARK. The results show the ability of the method to predict three-dimensional protein structures with similar foldings to the experimentally determined protein structures, regarding the structural metrics Root-Mean-Square Deviation and Global Distance Total Score Test. We also show that our method was able to reach comparable results to Rosetta and QUARK, and in some cases, it outperformed them, corroborating the effectiveness of our proposal.
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Johnson, Maury E. "Planning Genetic Algorithm: Pursuing Meta-knowledge." NSUWorks, 1999. http://nsuworks.nova.edu/gscis_etd/611.

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This study focuses on improving business planning by proposing a series of artificial intelligence techniques to facilitate the integration of decision support systems and expert system paradigms. The continued evolution of the national information infrastructure, open systems interconnectivity, and electronic data interchange lends toward the future plausibility of the inclusion of a back-end genetic algorithm approach. By using a back-end genetic algorithm, meta-planning knowledge could be collected, extended to external data sources, and utilized to improve business decision making.
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Books on the topic "Algorithmic knowledge"

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Jantke, Klaus P., and Steffen Lange, eds. Algorithmic Learning for Knowledge-Based Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60217-8.

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Gdanskiy, Nikolay. Fundamentals of the theory and algorithms on graphs. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/978686.

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The textbook describes the main theoretical principles of graph theory, the main tasks to be solved using graph structures, and General methods of their solution and specific algorithms, with estimates of their complexity. I covered a lot of the examples given questions to test knowledge and tasks for independent decisions. Along with the control tasks to verify the theoretical training provided practical assignments to develop programs to study topics of graph theory. Meets the requirements of Federal state educational standards of higher education of the last generation. Designed for undergraduate and graduate programs, studying information technology, for in-depth training in analysis and design of systems of complex structure. Also the guide can be useful to specialists of the IT sphere in the study of algorithmic aspects of graph theory.
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Kanchanasut, Kanchana, and Jean-Jacques Lévy, eds. Algorithms, Concurrency and Knowledge. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60688-2.

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Richards, Debbie, and Byeong-Ho Kang, eds. Knowledge Acquisition: Approaches, Algorithms and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01715-5.

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Knowledge discovery from data streams. Boca Raton, FL: Chapman & Hall/CRC, 2010.

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Freitas, Alex A. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04923-5.

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Mallen, Jason. Utilising incomplete domain knowledge in an information theoretic guided inductive knowledge discovery algorithm. Portsmouth: University of Portsmouth, 1995.

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Ghosh, Ashish, Satchidananda Dehuri, and Susmita Ghosh, eds. Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77467-9.

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Hamel, Lutz. Knowledge discovery with support vector machines. Hoboken, N.J: John Wiley & Sons, 2009.

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Wang, Xianhua. Knowledge-based selection of databases: An algorithm and its elevation. Ann Arbor, Mich: University Microfilms International, 1991.

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Book chapters on the topic "Algorithmic knowledge"

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Dalkiran, Nuh Aygun, Moshe Hoffman, Ramamohan Paturi, Daniel Ricketts, and Andrea Vattani. "Common Knowledge and State-Dependent Equilibria." In Algorithmic Game Theory, 84–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33996-7_8.

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Zhou, Yan, Yasmeen Alufaisan, and Murat Kantarcioglu. "Data Mining with Algorithmic Transparency." In Advances in Knowledge Discovery and Data Mining, 130–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93034-3_11.

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Solans, David, Battista Biggio, and Carlos Castillo. "Poisoning Attacks on Algorithmic Fairness." In Machine Learning and Knowledge Discovery in Databases, 162–77. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67658-2_10.

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Wiehagen, Rolf, and Thomas Zeugmann. "Learning and consistency." In Algorithmic Learning for Knowledge-Based Systems, 1–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60217-8_1.

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Gasarch, William I., Mark G. Pleszkoch, and Mahendran Velauthapillai. "Classification using information." In Algorithmic Learning for Knowledge-Based Systems, 162–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60217-8_10.

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Wiehagen, Rolf, Carl H. Smith, and Thomas Zeugmann. "Classifying recursive predicates and languages." In Algorithmic Learning for Knowledge-Based Systems, 174–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60217-8_11.

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Zeugmann, Thomas, and Steffen Lange. "A guided tour across the boundaries of learning recursive languages." In Algorithmic Learning for Knowledge-Based Systems, 190–258. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60217-8_12.

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Shinohara, Takeshi, and Setsuo Arikawa. "Pattern inference." In Algorithmic Learning for Knowledge-Based Systems, 259–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60217-8_13.

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Watson, Phil. "Inductive learning of recurrence-term languages from positive data." In Algorithmic Learning for Knowledge-Based Systems, 292–315. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60217-8_14.

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Takada, Yuji. "Learning formal languages based on control sets." In Algorithmic Learning for Knowledge-Based Systems, 316–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60217-8_15.

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Conference papers on the topic "Algorithmic knowledge"

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Halpern, Joseph Y., and Riccardo Pucella. "Probabilistic algorithmic knowledge." In the 9th conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/846241.846258.

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Petride, Sabina, and Riccardo Pucella. "Perfect cryptography, S5 knowledge, and algorithmic knowledge." In the 11th conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1324249.1324281.

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Wilkie, Colin, and Leif Azzopardi. "Algorithmic Bias." In CIKM '17: ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3132847.3133135.

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Hajian, Sara, Francesco Bonchi, and Carlos Castillo. "Algorithmic Bias." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2945386.

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Mahdian, Mohammad, Okke Schrijvers, and Sergei Vassilvitskii. "Algorithmic Cartography." In KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2783258.2783375.

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Lynch, K. J. "Knowledge discovery from historical data: an algorithmic approach." In Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences. IEEE, 1992. http://dx.doi.org/10.1109/hicss.1992.183466.

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Buß, Sebastian, Hendrik Molter, Rolf Niedermeier, and Maciej Rymar. "Algorithmic Aspects of Temporal Betweenness." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403259.

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Wilder, Bryan. "Algorithmic Social Intervention." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/840.

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Abstract:
Social and behavioral interventions are a critical tool for governments and communities to tackle deep-rooted societal challenges such as homelessness, disease, and poverty. However, real-world interventions are almost always plagued by limited resources and limited data, which creates a computational challenge: how can we use algorithmic techniques to enhance the targeting and delivery of social and behavioral interventions? The goal of my thesis is to provide a unified study of such questions, collectively considered under the name "algorithmic social intervention". This proposal introduces algorithmic social intervention as a distinct area with characteristic technical challenges, presents my published research in the context of these challenges, and outlines open problems for future work. A common technical theme is decision making under uncertainty: how can we find actions which will impact a social system in desirable ways under limitations of knowledge and resources? The primary application area for my work thus far is public health, e.g. HIV or tuberculosis prevention. For instance, I have developed a series of algorithms which optimize social network interventions for HIV prevention. Two of these algorithms have been pilot-tested in collaboration with LA-area service providers for homeless youth, with preliminary results showing substantial improvement over status-quo approaches. My work also spans other topics in infectious disease prevention and underlying algorithmic questions in robust and risk-aware submodular optimization.
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Xu, Renzhe, Peng Cui, Kun Kuang, Bo Li, Linjun Zhou, Zheyan Shen, and Wei Cui. "Algorithmic Decision Making with Conditional Fairness." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403263.

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Meystel, A., R. Bhatt, D. Gaw, P. Graglia, and S. Waldon. "Multiresolutional Pyramidal Knowledge Representation And Algorithmic Basis Of IMAS-2." In Robotics and IECON '87 Conferences, edited by Wendell H. Chun and William J. Wolfe. SPIE, 1987. http://dx.doi.org/10.1117/12.968238.

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Reports on the topic "Algorithmic knowledge"

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Lichtenstein, Sarah. Retrieval of Knowledge through Algorithmic Decomposition. Fort Belvoir, VA: Defense Technical Information Center, June 1990. http://dx.doi.org/10.21236/ada225667.

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Constable, Robert L. Building Interactive Digital Libraries of Formal Algorithmic Knowledge. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada403617.

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Constable, Robert L. Building Interactive Digital Libraries of Formal Algorithmic Knowledge. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada426580.

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Constable, Robert L. Building Interactive Digital Libraries of Formal Algorithmic Knowledge. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada414364.

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Zakay, Dan, Colin Kessel, and Lydia Bekhor. Training for Retrieval of Knowledge under Stress through Algorithmic Decomposition. Fort Belvoir, VA: Defense Technical Information Center, October 1986. http://dx.doi.org/10.21236/ada178756.

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Domingos, Pedro. Algorithms for Collective Knowledge Acquisition. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada561728.

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Cordeiro de Amorim, Renato. A survey on feature weighting based K-Means algorithms. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ser.v1i2.79.

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In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means
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Zhao, Feng. Practical Control Algorithms for Nonlinear Dynamical Systems Using Phase-Space Knowledge and Mixed Numeric and Geometric Computation. Fort Belvoir, VA: Defense Technical Information Center, October 1997. http://dx.doi.org/10.21236/ada330093.

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Zhao, Feng. Practical Control Algorithms for Nonlinear Dynamical Systems Using Phase-Space Knowledge and Mixed Numeric and Geometric Computation. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada353610.

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Bringsjord, Selmer, Konstantine Arkoudas, and Yingrui Yang. New Architectures, Algorithms And Designs That Lead To Implemented Machine Reasoning Over Knowledge In Epistemic And Deontic Formats, In The Service Of Advanced Wargaming. Fort Belvoir, VA: Defense Technical Information Center, August 2006. http://dx.doi.org/10.21236/ada456936.

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