Zeitschriftenartikel: „Symbolic approaches“

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Kushnarenko, Olga, und Sophie Pinchinat. „Intensional Approaches for Symbolic Methods“. Electronic Notes in Theoretical Computer Science 18 (1998): 112. http://dx.doi.org/10.1016/s1571-0661(05)80253-1.

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Sebastiani, Roberto, Stefano Tonetta und Moshe Y. Vardi. „Symbolic systems, explicit properties: on hybrid approaches for LTL symbolic model checking“. International Journal on Software Tools for Technology Transfer 13, Nr. 4 (16.09.2010): 319–35. http://dx.doi.org/10.1007/s10009-010-0168-4.

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Calegari, Roberta, Giovanni Ciatto und Andrea Omicini. „On the integration of symbolic and sub-symbolic techniques for XAI: A survey“. Intelligenza Artificiale 14, Nr. 1 (17.09.2020): 7–32. http://dx.doi.org/10.3233/ia-190036.

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The more intelligent systems based on sub-symbolic techniques pervade our everyday lives, the less human can understand them. This is why symbolic approaches are getting more and more attention in the general effort to make AI interpretable, explainable, and trustable. Understanding the current state of the art of AI techniques integrating symbolic and sub-symbolic approaches is then of paramount importance, nowadays—in particular in the XAI perspective. This is why this paper provides an overview of the main symbolic/sub-symbolic integration techniques, focussing in particular on those targeting explainable AI systems.

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UEBERLA, JOERG P., und ARUN JAGOTA. „Integrating Neural and Symbolic Approaches: A Symbolic Learning Scheme for a Connectionist Associative Memory“. Connection Science 5, Nr. 3-4 (Januar 1993): 377–93. http://dx.doi.org/10.1080/09540099308915706.

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5

Rival, Xavier. „Symbolic transfer function-based approaches to certified compilation“. ACM SIGPLAN Notices 39, Nr. 1 (Januar 2004): 1–13. http://dx.doi.org/10.1145/982962.964002.

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Wilson, Janelle L. „Negotiating Identity: Symbolic Interactionist Approaches to Social Identity“. Contemporary Sociology: A Journal of Reviews 46, Nr. 1 (Januar 2017): 108–9. http://dx.doi.org/10.1177/0094306116681813tt.

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Tofts, C. „Symbolic Approaches to Probability Distributions in Process Algebra“. Formal Aspects of Computing 12, Nr. 5 (Dezember 2000): 392–415. http://dx.doi.org/10.1007/pl00013291.

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ROLI, F., S. B. SERPICO und G. VERNAZZA. „IMAGE RECOGNITION BY INTEGRATION OF CONNECTIONIST AND SYMBOLIC APPROACHES“. International Journal of Pattern Recognition and Artificial Intelligence 09, Nr. 03 (Juni 1995): 485–515. http://dx.doi.org/10.1142/s0218001495000493.

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This paper presents a methodology for integrating connectionist and symbolic approaches to 2D image recognition. The proposed integration paradigm exploits the synergy of the two approaches for both the training and the recognition phases of an image recognition system. In the training phase, a symbolic module provides an approximate solution to a given image-recognition problem in terms of symbolic models. Such models are hierarchically organized into different abstraction levels, and include contextual descriptions. After mapping such models into a complex neural architecture, a neural training process is carried out to optimize the solution of the recognition problem. The so-obtained neural networks are used during the recognition phase for pattern classification. In this phase, the role of symbolic modules consists of managing complex aspects of information processing: abstraction levels, contextual information, and global recognition hypotheses. A hybrid system implementing the proposed integration paradigm is presented, and its advantages over single approaches are assessed. Results on Magnetic Resonance image recognition are reported, and comparisons with some well-known classifiers are made.

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Amado, Leonardo, Ramon Fraga Pereira und Felipe Meneguzzi. „Robust Neuro-Symbolic Goal and Plan Recognition“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 10 (26.06.2023): 11937–44. http://dx.doi.org/10.1609/aaai.v37i10.26408.

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Goal Recognition is the task of discerning the intended goal of an agent given a sequence of observations, whereas Plan Recognition consists of identifying the plan to achieve such intended goal. Regardless of the underlying techniques, most recognition approaches are directly affected by the quality of the available observations. In this paper, we develop neuro-symbolic recognition approaches that can combine learning and planning techniques, compensating for noise and missing observations using prior data. We evaluate our approaches in standard human-designed planning domains as well as domain models automatically learned from real-world data. Empirical experimentation shows that our approaches reliably infer goals and compute correct plans in the experimental datasets. An ablation study shows that outperform approaches that rely exclusively on the domain model, or exclusively on machine learning in problems with both noisy observations and low observability.

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Brkić, Dejan, Pavel Praks, Renáta Praksová und Tomáš Kozubek. „Symbolic Regression Approaches for the Direct Calculation of Pipe Diameter“. Axioms 12, Nr. 9 (31.08.2023): 850. http://dx.doi.org/10.3390/axioms12090850.

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This study provides novel and accurate symbolic regression-based solutions for the calculation of pipe diameter when flow rate and pressure drop (head loss) are known, together with the length of the pipe, absolute inner roughness of the pipe, and kinematic viscosity of the fluid. PySR and Eureqa, free and open-source symbolic regression tools, are used for discovering simple and accurate approximate formulas. Three approaches are used: (1) brute force of computing power, which provides results based on raw input data; (2) an improved method where input parameters are transformed through the Lambert W-function; (3) a method where the results are based on inputs and the Colebrook equation transformed through new suitable dimensionless groups. The discovered models were simplified by the WolframAlpha simplify tool and/or the equivalent Matlab Symbolic toolbox. Novel models make iterative calculus redundant; they are simple for computer coding while the relative error remains lower compared with the solution through nomograms. The symbolic-regression solutions discovered by brute force computing power discard the kinematic viscosity of the fluid as an input parameter, implying that it has the least influence.

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Hatzilygeroudis, Ioannis, und Jim Prentzas. „Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems“. International Journal of Hybrid Intelligent Systems 1, Nr. 3-4 (19.01.2005): 111–26. http://dx.doi.org/10.3233/his-2004-13-401.

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Morris, G. H., und Robert Hopper. „Symbolic action as alignment: A synthesis of rules approaches“. Research on Language & Social Interaction 21, Nr. 1-4 (Januar 1987): 1–29. http://dx.doi.org/10.1080/08351818709389283.

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de Guzmán, M., und D. Orden. „From graphs to tensegrity structures: Geometric and symbolic approaches“. Publicacions Matemàtiques 50 (01.07.2006): 279–99. http://dx.doi.org/10.5565/publmat_50206_02.

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LANGMEAD, CHRISTOPHER JAMES, und SUMIT KUMAR JHA. „SYMBOLIC APPROACHES FOR FINDING CONTROL STRATEGIES IN BOOLEAN NETWORKS“. Journal of Bioinformatics and Computational Biology 07, Nr. 02 (April 2009): 323–38. http://dx.doi.org/10.1142/s0219720009004084.

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We present an exact algorithm, based on techniques from the field of Model Checking, for finding control policies for Boolean Networks (BN) with control nodes. Given a BN, a set of starting states, I, a set of goal states, F, and a target time, t, our algorithm automatically finds a sequence of control signals that deterministically drives the BN from I to F at, or before time t, or else guarantees that no such policy exists. Despite recent hardness-results for finding control policies for BNs, we show that, in practice, our algorithm runs in seconds to minutes on over 13,400 BNs of varying sizes and topologies, including a BN model of embryogenesis in Drosophila melanogaster with 15,360 Boolean variables. We then extend our method to automatically identify a set of Boolean transfer functions that reproduce the qualitative behavior of gene regulatory networks. Specifically, we automatically learn a BN model of D. melanogaster embryogenesis in 5.3 seconds, from a space containing 6.9 × 1010 possible models.

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Nesic, D., und I. M. Mareels. „Dead beat controllability of polynomial systems: symbolic computation approaches“. IEEE Transactions on Automatic Control 43, Nr. 2 (1998): 162–75. http://dx.doi.org/10.1109/9.661065.

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Clark, Dominic A. „Numerical and symbolic approaches to uncertainty management in AI“. Artificial Intelligence Review 4, Nr. 2 (1990): 109–46. http://dx.doi.org/10.1007/bf00133189.

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BLOEM, RODERICK, ALESSANDRO CIMATTI, INGO PILL und MARCO ROVERI. „SYMBOLIC IMPLEMENTATION OF ALTERNATING AUTOMATA“. International Journal of Foundations of Computer Science 18, Nr. 04 (August 2007): 727–43. http://dx.doi.org/10.1142/s0129054107004942.

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This paper addresses the challenges of symbolic model checking and language emptiness checking where the specification is given as an alternating Büchi automaton. We introduce a novel version of Miyano and Hayashi's construction that allows us to directly convert an alternating automaton to a polynomially-sized symbolic structure. We thus avoid building an exponentially-sized explicit representation of the corresponding nondeterministic automaton. For one-weak automata, Gastin and Oddoux' construction produces smaller automata than Miyano and Hayashi's construction. We present a (symbolic) hybrid approach that combines the benefits of both: while retaining full generality, it uses the cheaper construction for those parts of the automaton that are one-weak. We performed a thorough experimental comparison of the explicit and symbolic approaches and several variants of Miyano and Hayashi's construction, using both BDD-based and SAT-based model checking techniques. The symbolic approaches clearly outperform the explicit one.

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Prentzas, Jim, und Ioannis Hatzilygeroudis. „Neurules and connectionist expert systems: Unexplored neuro-symbolic reasoning aspects“. Intelligent Decision Technologies 15, Nr. 4 (10.01.2022): 761–77. http://dx.doi.org/10.3233/idt-210211.

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Neuro-symbolic approaches combine neural and symbolic methods. This paper explores aspects regarding the reasoning mechanisms of two neuro-symbolic approaches, that is, neurules and connectionist expert systems. Both provide reasoning and explanation facilities. Neurules are a type of neuro-symbolic rules tightly integrating the neural and symbolic components, giving pre-eminence to the symbolic component. Connectionist expert systems give pre-eminence to the connectionist component. This paper explores reasoning aspects about neurules and connectionist expert systems that have not been previously addressed. As far as neurules are concerned, an aspect playing a role in conflict resolution (i.e., order of neurules) is explored. Experimental results show an improvement in reasoning efficiency. As far as connectionist expert systems are concerned, variations of the reasoning mechanism are explored. Experimental results are presented for them as well showing that one of the variations generally performs better than the others.

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Salazar, Carles. „The Complexity of Popular Religiosity: A Cognitive and Symbolic Approach“. Journal of Cognition and Culture 16, Nr. 3-4 (21.09.2016): 199–214. http://dx.doi.org/10.1163/15685373-12342177.

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Popular religiosity manifests itself alongside a heterogeneous continuum of beliefs, behaviours, modes of thinking and modes of living that flows between two poles: the experiential pole and the normative pole. A central theme in this paper will be that all forms of religiosity originate in a combination of innate predispositions and cultural upbringing; hence human religious experience needs to be approached by combining both cognitive and symbolic approaches. To this effect, this paper attempts to make use of an ethnographic research into ordinary forms of religiosity in order to show that religious ideas are not only parasitic upon evolved human cognitive abilities, as mainstream cognitive approaches to religion have claimed, but also on human symbolic languages, which in turn should be seen as a by-product of those evolved cognitive abilities.

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Allaire, Patricia R., und Robert E. Bradley. „Geometric Approaches to Quadratic Equations from Other Times and Places“. Mathematics Teacher 94, Nr. 4 (April 2001): 308–19. http://dx.doi.org/10.5951/mt.94.4.0308.

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Calegari, Roberta, Giovanni Ciatto, Enrico Denti und Andrea Omicini. „Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives“. Information 11, Nr. 3 (22.03.2020): 167. http://dx.doi.org/10.3390/info11030167.

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Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future.

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Hallett, Tim. „Symbolic Power and Organizational Culture“. Sociological Theory 21, Nr. 2 (Juni 2003): 128–49. http://dx.doi.org/10.1111/1467-9558.00181.

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With the recent wave of corporate scandals, organizational culture has regained relevance in politics and the media. However, to acquire enduring utility, the concept needs an overhaul to overcome the weaknesses of earlier approaches. As such, this paper reconceptualizes organizational culture as a negotiated order (Strauss 1978) that emerges through interactions between participants, an order influenced by those with the symbolic power to define the situation. I stress the complementary contributions of theorists of practice (Bourdieu and Swidler) and theorists of interaction (Goffman and Strauss), building upward from practice into interaction, symbolic power, and the negotiated order. Using data from initial reports on the fall of Arthur Andersen and Co., I compare this symbolic power approach to other approaches (culture as subjective beliefs and values or as context/public meaning). The symbolic power model has five virtues: an empirically observable object of study; the capacity to explain conflict and integration; the ability to explain stability and change; causal efficacy; and links between the micro-, meso-, and macrolevels of analysis. Though this paper focuses on organizational culture, the symbolic power model provides theoretical leverage for understanding many situated contexts.

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Arrotta, Luca, Gabriele Civitarese und Claudio Bettini. „Semantic Loss“. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, Nr. 4 (19.12.2023): 1–29. http://dx.doi.org/10.1145/3631407.

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Deep Learning models are a standard solution for sensor-based Human Activity Recognition (HAR), but their deployment is often limited by labeled data scarcity and models' opacity. Neuro-Symbolic AI (NeSy) provides an interesting research direction to mitigate these issues by infusing knowledge about context information into HAR deep learning classifiers. However, existing NeSy methods for context-aware HAR require computationally expensive symbolic reasoners during classification, making them less suitable for deployment on resource-constrained devices (e.g., mobile devices). Additionally, NeSy approaches for context-aware HAR have never been evaluated on in-the-wild datasets, and their generalization capabilities in real-world scenarios are questionable. In this work, we propose a novel approach based on a semantic loss function that infuses knowledge constraints in the HAR model during the training phase, avoiding symbolic reasoning during classification. Our results on scripted and in-the-wild datasets show the impact of different semantic loss functions in outperforming a purely data-driven model. We also compare our solution with existing NeSy methods and analyze each approach's strengths and weaknesses. Our semantic loss remains the only NeSy solution that can be deployed as a single DNN without the need for symbolic reasoning modules, reaching recognition rates close (and better in some cases) to existing approaches.

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Brazdil, Pavel, Shamsuddeen H. Muhammad, Fátima Oliveira, João Cordeiro, Fátima Silva, Purificação Silvano und António Leal. „Semi-Automatic Approaches for Exploiting Shifter Patterns in Domain-Specific Sentiment Analysis“. Mathematics 10, Nr. 18 (06.09.2022): 3232. http://dx.doi.org/10.3390/math10183232.

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This paper describes two different approaches to sentiment analysis. The first is a form of symbolic approach that exploits a sentiment lexicon together with a set of shifter patterns and rules. The sentiment lexicon includes single words (unigrams) and is developed automatically by exploiting labeled examples. The shifter patterns include intensification, attenuation/downtoning and inversion/reversal and are developed manually. The second approach exploits a deep neural network, which uses a pre-trained language model. Both approaches were applied to texts on economics and finance domains from newspapers in European Portuguese. We show that the symbolic approach achieves virtually the same performance as the deep neural network. In addition, the symbolic approach provides understandable explanations, and the acquired knowledge can be communicated to others. We release the shifter patterns to motivate future research in this direction.

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Zhou, Ning, Jinzhao Wu und Xinyan Gao. „Algebraic Verification Method for SEREs Properties via Groebner Bases Approaches“. Journal of Applied Mathematics 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/272781.

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This work presents an efficient solution using computer algebra system to perform linear temporal properties verification for synchronous digital systems. The method is essentially based on both Groebner bases approaches and symbolic simulation. A mechanism for constructing canonical polynomial set based symbolic representations for both circuit descriptions and assertions is studied. We then present a complete checking algorithm framework based on these algebraic representations by using Groebner bases. The computational experience result in this work shows that the algebraic approach is a quite competitive checking method and will be a useful supplement to the existent verification methods based on simulation.

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Cysarz, D., A. Porta, N. Montano, P. V. Leeuwen, J. Kurths und N. Wessel. „Quantifying heart rate dynamics using different approaches of symbolic dynamics“. European Physical Journal Special Topics 222, Nr. 2 (Juni 2013): 487–500. http://dx.doi.org/10.1140/epjst/e2013-01854-7.

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Thomson, Lisa. „Scott, Susie, Negotiating Identity: Symbolic Interactionist Approaches to Social Identity.“ Canadian Journal of Sociology 41, Nr. 3 (30.09.2016): 433–36. http://dx.doi.org/10.29173/cjs28185.

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Camurri, Antonio, Marcello Frixione und Renato Zaccaria. „A music knowledge representation system combining symbolic and analogic approaches“. Computers and the Humanities 27, Nr. 1 (Januar 1993): 25–30. http://dx.doi.org/10.1007/bf01830714.

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Simakova, Svetlana I. „ARKAIM AS AN ELEMENT OF THE SYMBOLIC CAPITAL OF THE CHELYABINSK REGION“. Sign problematic field in mediaeducation 50, Nr. 4 (21.12.2023): 112–18. http://dx.doi.org/10.47475/2070-0695-2023-50-4-112-118.

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The regions of Russia today are experiencing great diffi culties associated with migration processes within the country. People feel less and less connected to their small homeland, easily moving away and leaving in search of a better life in capital cities. Increasing the image of the region by accumulating its symbolic capital can be a solution to this problem. The article analyzes sources on the research topic, studies various approaches to the defi nition of the concept of “symbolic capital of a territory”, analyzes various approaches to the criteria of symbolic capital and methodological approaches to its study. The author has identifi ed the following criteria for symbolic capital: famous personalities, natural objects and geographic features, statuses and brands of the territory, symbols of the territory and memorable places, visual components of the territorial space, unique cultural and historical events. Based on the above criteria, the symbolic capital of the Chelyabinsk State Historical and Archaeological Museum-Reserve “Arkaim” is analyzed. The material for the study was posts from the public community on the social network “VKontakte” “Arkaim”. It is noted that thesymbolic capital of Arkaim is organically woven into the alternative branding of the region, based on historical and natural objects as opposed to the industrial past of the Chelyabinsk region.

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Singh, Gunjan, Sutapa Mondal, Sumit Bhatia und Raghava Mutharaju. „Neuro-Symbolic Techniques for Description Logic Reasoning (Student Abstract)“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 18 (18.05.2021): 15891–92. http://dx.doi.org/10.1609/aaai.v35i18.17942.

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With the goal to find scalable reasoning approaches, neuro-symbolic techniques have gained significant attention. However, the existing approaches do not take into account the inference capabilities of ontology languages that are based on expressive description logic (such as OWL 2). To fill this gap, we propose two approaches: an ontology-based embedding model for theories in EL++ description logic and a reinforcement learning-based solution for efficient tableau-based reasoning on description logic. We describe promising initial results of our efforts towards these directions and lay down the direction for future work.

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Primovich, Juraev Gulomjon, und Saparov Saidkul Khojamurodovich. „AN ALGORITHM FOR SELECTING AN INFORMATIVE SYMBOLIC COMPLEX BASED ON CLASSIFICATION ERROR COEFFICIENTS AND PROBABILISTIC INDICATORS IN THE REPRESENTATION OF SYMBOLS“. International Journal of Advance Scientific Research 4, Nr. 3 (01.03.2024): 149–55. http://dx.doi.org/10.37547/ijasr-04-03-27.

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In the primary processing of information, in particular in character recognition, an important issue is the selection and classification of an informative feature or a set of features classifying objects. Despite the fact that a number of methods and algorithms have been proposed to solve these problems, there are many problems in this direction that are waiting to be solved. This is due to the fact that many of the proposed approaches strongly depend on the nature of the object of study, the number of its features, the type of perceived values of features, the size of the study sample, etc., and impose certain requirements on the above. In addition, each method or algorithm will strongly depend on whether the criterion of informative selection of features and the defining rule determining the quality of the choice made are correctly chosen.This article presents a description of the algorithm developed taking into account the above approaches to the selection of information complexes of signs, as well as recommendations on the application of this algorithm in practical matters of the medical field, i.e. in ischemic heart disease obtained as an object of study (5 classes, 507 objects, 89 signs, including X_1 class “strenuous angina”, X_2 class “Acute myocardial infarction”, class X_3 “Arrhythmic form”, class X_4 “Postinfarction cardiosclerosis”, for class X_5 “Persistent form of atrial fibrillation”) formulated training was applied to the selection and positive results were achieved.

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Gonzalez, Cleotilde, und Christian Lebiere. „Cognitive architectures combine formal and heuristic approaches“. Behavioral and Brain Sciences 36, Nr. 3 (14.05.2013): 285–86. http://dx.doi.org/10.1017/s0140525x12002956.

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AbstractQuantum probability (QP) theory provides an alternative account of empirical phenomena in decision making that classical probability (CP) theory cannot explain. Cognitive architectures combine probabilistic mechanisms with symbolic knowledge-based representations (e.g., heuristics) to address effects that motivate QP. They provide simple and natural explanations of these phenomena based on general cognitive processes such as memory retrieval, similarity-based partial matching, and associative learning.

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Chacón, Jose Emmanuel, und Oldemar Rodríguez. „Regression Models for Symbolic Interval-Valued Variables“. Entropy 23, Nr. 4 (06.04.2021): 429. http://dx.doi.org/10.3390/e23040429.

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This paper presents new approaches to fit regression models for symbolic internal-valued variables, which are shown to improve and extend the center method suggested by Billard and Diday and the center and range method proposed by Lima-Neto, E.A.and De Carvalho, F.A.T. Like the previously mentioned methods, the proposed regression models consider the midpoints and half of the length of the intervals as additional variables. We considered various methods to fit the regression models, including tree-based models, K-nearest neighbors, support vector machines, and neural networks. The approaches proposed in this paper were applied to a real dataset and to synthetic datasets generated with linear and nonlinear relations. For an evaluation of the methods, the root-mean-squared error and the correlation coefficient were used. The methods presented herein are available in the the RSDA package written in the R language, which can be installed from CRAN.

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Gascuel, Olivier, Bernadette Bouchon-Meunier, Gilles Caraux, Patrick Gallinari, Alain Guénoche, Yann Guermeur, Yves Lechevallier et al. „Twelve Numerical, Symbolic and Hybrid Supervised Classification Methods“. International Journal of Pattern Recognition and Artificial Intelligence 12, Nr. 05 (August 1998): 517–71. http://dx.doi.org/10.1142/s0218001498000336.

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Supervised classification has already been the subject of numerous studies in the fields of Statistics, Pattern Recognition and Artificial Intelligence under various appellations which include discriminant analysis, discrimination and concept learning. Many practical applications relating to this field have been developed. New methods have appeared in recent years, due to developments concerning Neural Networks and Machine Learning. These "hybrid" approaches share one common factor in that they combine symbolic and numerical aspects. The former are characterized by the representation of knowledge, the latter by the introduction of frequencies and probabilistic criteria. In the present study, we shall present a certain number of hybrid methods, conceived (or improved) by members of the SYMENU research group. These methods issue mainly from Machine Learning and from research on Classification Trees done in Statistics, and they may also be qualified as "rule-based". They shall be compared with other more classical approaches. This comparison will be based on a detailed description of each of the twelve methods envisaged, and on the results obtained concerning the "Waveform Recognition Problem" proposed by Breiman et al.,4 which is difficult for rule based approaches.

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Rezazadegan, Dana, Shlomo Berkovsky, Juan C. Quiroz, A. Baki Kocaballi, Ying Wang, Liliana Laranjo und Enrico Coiera. „Symbolic and Statistical Learning Approaches to Speech Summarization: A Scoping Review“. Computer Speech & Language 72 (März 2022): 101305. http://dx.doi.org/10.1016/j.csl.2021.101305.

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Oyama, Tadasu. „Affective and Symbolic Meanings of Color and Form: Experimental Psychological Approaches“. Empirical Studies of the Arts 21, Nr. 2 (Juli 2003): 137–42. http://dx.doi.org/10.2190/eyft-b5ue-appw-10b6.

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Li, Shu-Chen, und Peter A. Frensch. „Revisiting Symbolic Approaches to Cognition in a New Visual Programming Environment“. Contemporary Psychology 49, Nr. 4 (August 2004): 431–32. http://dx.doi.org/10.1037/004385.

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Pacheco, Alberto, Mariano Rivera und Raymundo Torres. „Acoustic Spectral Analysis for Emergency Vehicle Detection: Symbolic and CNN Approaches“. Advances in Artificial Intelligence and Machine Learning 04, Nr. 02 (2024): 2189–200. http://dx.doi.org/10.54364/aaiml.2024.42125.

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During emergencies, ambulances on city streets face delays due to traffic obstacles. This paper addresses two efficient emergency vehicle detection (EVD) methods for restricted hardware implementation considering noisy conditions: a symbolic processing-based algorithm and a convolutional neural network (CNN) model, both of which utilize Mel spectrogram representations of Hi-Lo siren audio records. The symbolic method employs regular expressions and acceptance criteria to process text-pattern features extracted from spectrograms, offering a self-explanatory, easily tunable, and resource-efficient solution suitable for lowcost hardware platforms. On the other hand, the CNN model directly processes spectrogram representations, leveraging spatial correlation for classification with a streamlined architecture consisting of very few layers. The experimental results demonstrate that both approaches achieve high accuracy (97-98%) in classifying Hi-Lo sirens, with the CNN model exhibiting slightly better performance. Challenges such as signal noise and harmonics are addressed through iterative algorithms and signal reconstruction considerations. Future directions include identifying additional siren effects and conducting performance measurements on constrained hardware devices. Overall, this study presents viable EVD solutions suitable for real-time implementation and underscores the importance of adaptable and explainable AI methods in enhancing road safety.

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Amendola, Giovanni. „Special Issue on Logic-Based Artificial Intelligence“. Algorithms 16, Nr. 2 (13.02.2023): 106. http://dx.doi.org/10.3390/a16020106.

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Since its inception, research in the field of Artificial Intelligence (AI) has had a fundamentally logical approach; therefore, discussions have taken place to establish a way of distinguishing symbolic AI from sub-symbolic AI, basing the approach instead on the statistical approaches typical of machine learning, deep learning or Bayesian networks [...]

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Gayler, Ross W. „Vector symbolic architectures are a viable alternative for Jackendoff's challenges“. Behavioral and Brain Sciences 29, Nr. 1 (Februar 2006): 78–79. http://dx.doi.org/10.1017/s0140525x06309028.

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The authors, on the basis of brief arguments, have dismissed tensor networks as a viable response to Jackendoff's challenges. However, there are reasons to believe that connectionist approaches descended from tensor networks are actually very well suited to answering Jackendoff's challenges. I rebut their arguments for dismissing tensor networks and briefly compare the approaches.

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ARULMANI, GIDEON. „CONTEXT AND CULTURE–RESONANT APPROACHES WORK AS CULTURE“. Main Issues Of Pedagogy And Psychology 5, Nr. 2 (14.09.2014): 124–35. http://dx.doi.org/10.24234/miopap.v5i2.96.

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Anthropologists describe culture as human phenomena that cannot be attributed to genetic or biological inheritance, but reflective of a cohesive and assimilated system of learned behaviour patterns which characterize the members of a social group (Hoebel, 1966). Other features of culture include the symbolic representation of experiences and the distinct ways in which groups of people classify and represent their collective experience (Geertz, 1973; Liu & Sibley, 2009)

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Turner, Jonathan H. „The Sociology of Emotions: Basic Theoretical Arguments“. Emotion Review 1, Nr. 4 (16.09.2009): 340–54. http://dx.doi.org/10.1177/1754073909338305.

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In this article, the basic sociological approaches to theorizing human emotions are reviewed. In broad strokes, theorizing can be grouped into several schools of thought: evolutionary, symbolic interactionist, symbolic interactionist with psychoanalytic elements, interaction ritual, power and status, stratification, and exchange. All of these approaches to theorizing emotions have generated useful insights into the dynamics of emotions. There remain, however, unresolved issues in sociological approaches to emotions, including: the nature of emotions, the degree to which emotions are hard-wired neurological or socially constructed, the relevance of analyzing the biology and evolution of emotions, the relationship between cognition and emotions, the number of distinctive emotional states produced by humans, and the relationship between emotions and rationality.

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Hasim, Irfan Sabarilah, Indah Widiastuti und Iwan Sudradjat. „Symbolic interactionism in vernacular cultural landscape research“. ARTEKS : Jurnal Teknik Arsitektur 8, Nr. 1 (01.04.2023): 135–44. http://dx.doi.org/10.30822/arteks.v8i1.2080.

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Customary and traditional villages, also called vernacular cultural landscapes, are local settlement units whose inhabitants adhere to ancestral beliefs. It is important to conduct research on vernacular cultural landscapes in Indonesia, given the usual and concerning degradation of cultural landscapes. Different places have different cultures and different customary rules and habits. Each has its uniqueness and distinctiveness, so there is no one standardized approach or method that can be adapted to study the vernacular cultural landscape. Different places may require different research approaches or methods; even the same place if studied under a different topic or time frame, may also require a different approach or method. There are research approaches commonly used by the researcher of the vernacular cultural landscape, including phenomenology, narrative study, case study, grounded theory, and ethnography. This article will review one approach that can be an alternative for the researcher of the vernacular cultural landscape, namely Symbolic Interactionism. Symbolic Interactionism is an approach that can be effectively applied to study human groups, community life, and social interactions. Symbolic interactionism is able to reveal the relationships that occur naturally among members of the society, particularly the relationship between intangible symbols, rules, norms, and daily activities, with tangible things such as the formation of space, buildings, circulation, and other physical configurations.

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Shmalko, Elizaveta, und Askhat Diveev. „Control Synthesis as Machine Learning Control by Symbolic Regression Methods“. Applied Sciences 11, Nr. 12 (12.06.2021): 5468. http://dx.doi.org/10.3390/app11125468.

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The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods. The principle of small variation of the basic solution is presented to set up the neighbourhood of the search and to increase search efficiency of symbolic regression methods. Different symbolic regression methods such as genetic programming, network operator, Cartesian and binary genetic programming are presented in details. It is shown on the computational example the possibilities of symbolic regression methods as unsupervised machine learning control technique to the solution of MLC problem of control synthesis for obtaining the stabilization system for a mobile robot.

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Calegari, Roberta, Giovanni Ciatto, Stefano Mariani, Enrico Denti und Andrea Omicini. „LPaaS as Micro-Intelligence: Enhancing IoT with Symbolic Reasoning“. Big Data and Cognitive Computing 2, Nr. 3 (03.08.2018): 23. http://dx.doi.org/10.3390/bdcc2030023.

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In the era of Big Data and IoT, successful systems have to be designed to discover, store, process, learn, analyse, and predict from a massive amount of data—in short, they have to behave intelligently. Despite the success of non-symbolic techniques such as deep learning, symbolic approaches to machine intelligence still have a role to play in order to achieve key properties such as observability, explainability, and accountability. In this paper we focus on logic programming (LP), and advocate its role as a provider of symbolic reasoning capabilities in IoT scenarios, suitably complementing non-symbolic ones. In particular, we show how its re-interpretation in terms of LPaaS (Logic Programming as a Service) can work as an enabling technology for distributed situated intelligence. A possible example of hybrid reasoning—where symbolic and non-symbolic techniques fruitfully combine to produce intelligent behaviour—is presented, demonstrating how LPaaS could work in a smart energy grid scenario.

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Davydova, Darja. „Criminal networks, unfortunate circumstances, or migratory projects? Researching sex trafficking from Eastern Europe“. Cultural Dynamics 25, Nr. 2 (Juli 2013): 229–43. http://dx.doi.org/10.1177/0921374013498136.

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Eastern Europe constitutes a peripheral space of the European Union, in which political and symbolic belonging of the nations is constantly questioned. As the migration of Eastern Europeans challenges and redefines geographical and symbolic borders, sex trafficking emerges as a politicized issue related to the construction of the European identity and the surveillance of the borders. The research on sex trafficking is frequently employed by policy-makers in order to justify the increasing control of migration over the Eastern border. In this article, I explore the diversity of methodological approaches in recent research on migration for sex work from Eastern Europe and discuss its implications for maintaining physical and symbolic Eastern border of the European Union. I distinguish between different perspectives undertaken by researchers and demonstrate the relation between conceptualization of the problem of sex trafficking, methodological approaches, and the way Eastern Europe is described in research projects.

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Diveev, Askhat, und Elizaveta Shmalko. „Research of Trajectory Optimization Approaches in Synthesized Optimal Control“. Symmetry 13, Nr. 2 (18.02.2021): 336. http://dx.doi.org/10.3390/sym13020336.

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This article presents a study devoted to the emerging method of synthesized optimal control. This is a new type of control based on changing the position of a stable equilibrium point. The object stabilization system forces the object to move towards the equilibrium point, and by changing its position over time, it is possible to bring the object to the desired terminal state with the optimal value of the quality criterion. The implementation of such control requires the construction of two control contours. The first contour ensures the stability of the control object relative to some point in the state space. Methods of symbolic regression are applied for numerical synthesis of a stabilization system. The second contour provides optimal control of the stable equilibrium point position. The present paper provides a study of various approaches to find the optimal location of equilibrium points. A new problem statement with the search of function for optimal location of the equilibrium points in the second stage of the synthesized optimal control approach is formulated. Symbolic regression methods of solving the stated problem are discussed. In the presented numerical example, a piece-wise linear function is applied to approximate the location of equilibrium points.

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Romero, Oscar J., John Zimmerman, Aaron Steinfeld und Anthony Tomasic. „Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysis“. Proceedings of the AAAI Symposium Series 2, Nr. 1 (22.01.2024): 396–405. http://dx.doi.org/10.1609/aaaiss.v2i1.27706.

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This paper explores the integration of two AI subdisciplines employed in the development of artificial agents that exhibit intelligent behavior: Large Language Models (LLMs) and Cognitive Architectures (CAs). We present three integration approaches, each grounded in theoretical models and supported by preliminary empirical evidence. The modular approach, which introduces four models with varying degrees of integration, makes use of chain-of-thought prompting, and draws inspiration from augmented LLMs, the Common Model of Cognition, and the simulation theory of cognition. The agency approach, motivated by the Society of Mind theory and the LIDA cognitive architecture, proposes the formation of agent collections that interact at micro and macro cognitive levels, driven by either LLMs or symbolic components. The neuro-symbolic approach, which takes inspiration from the CLARION cognitive architecture, proposes a model where bottom-up learning extracts symbolic representations from an LLM layer and top-down guidance utilizes symbolic representations to direct prompt engineering in the LLM layer. These approaches aim to harness the strengths of both LLMs and CAs, while mitigating their weaknesses, thereby advancing the development of more robust AI systems. We discuss the tradeoffs and challenges associated with each approach.

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Becker, Katharina. „Transforming Identities – New Approaches to Bronze Age Deposition in Ireland“. Proceedings of the Prehistoric Society 79 (14.10.2013): 225–63. http://dx.doi.org/10.1017/ppr.2013.8.

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This paper explores the interpretation of the deposition of artefacts in Ireland from c. 2500 to c. 800 bc, combining a contextual analysis with post-processual ideas about materiality, artefacts, and their biographies. Hoards, single and burial finds are shown to be complementary strands of the depositional record and the result of deliberate deposition. It is argued that both the symbolic value of these items as well as economic and practical rationales determine the depositional mode. The paper attempts to infer social practices and rules that determined the differential treatment of materials and object types. The main structuring factor in the depositional record is the type-specific meanings of individual artefacts, which embody social identities beyond the utilitarian function of the object. The act of deposition facilitates and legitimates the literal and symbolic transformation of artefacts and the concepts they embody. The need for a separation between ritual and profane interpretation is removed, as deposition is understood as the reflection of prehistoric concepts rather than labelled according to modern notions of functionality. It is also argued that both dry and wet places are meaningful contexts and that different forms of wet landscapes were conceptualised differently

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Karaseva, Tatiana, und ‪Eugene Semenkin. „Evolutionary Approaches to the Identification of Dynamic Processes in the Form of Differential Equations and Their Systems“. Algorithms 15, Nr. 10 (27.09.2022): 351. http://dx.doi.org/10.3390/a15100351.

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Evolutionary approaches are widely applied in solving various types of problems. The paper considers the application of EvolODE and EvolODES approaches to the identification of dynamic systems. EvolODE helps to obtain a model in the form of an ordinary differential equation without restrictions on the type of the equation. EvolODES searches for a model in the form of an ordinary differential equation system. The algorithmic basis of these approaches is a modified genetic programming algorithm for finding the structure of ordinary differential equations and differential evolution to optimize the values of numerical constants used in the equation. Testing for these approaches on problems in the form of ordinary differential equations and their systems was conducted. The influence of noise present in the data and the sample size on the model error was considered for each of the approaches. The symbolic accuracy of the resulting equations was studied. The proposed approaches make it possible to obtain models in symbolic form. They will provide opportunities for further interpretation and application.

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