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Journal articles on the topic 'Symbolic models'

Author: Grafiati

Published: 25 May 2024

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1

Weng, Juyang. "Symbolic Models and Emergent Models: A Review." IEEE Transactions on Autonomous Mental Development 4, no. 1 (2012): 29–53. http://dx.doi.org/10.1109/tamd.2011.2159113.

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2

Tabuada, Paulo. "Symbolic models for control systems." Acta Informatica 43, no. 7 (2007): 477–500. http://dx.doi.org/10.1007/s00236-006-0036-6.

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3

Fang, Meng, Shilong Deng, Yudi Zhang, et al. "Large Language Models Are Neurosymbolic Reasoners." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 17985–93. http://dx.doi.org/10.1609/aaai.v38i16.29754.

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A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning. This paper investigates the potential application of Large Language Models (LLMs) as symbolic reasoners. We focus on text-based games, significant benchmarks for agents with natural language capabilities, particularly in symbolic tasks like math, map reading, sorting, and applying common sense in text-based worlds. To facilitate these agents, we propose an LLM agent designed to tackle symbolic challenges and achieve in-game objectives. We begin by initial

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4

Welleck, Sean, Peter West, Jize Cao, and Yejin Choi. "Symbolic Brittleness in Sequence Models: On Systematic Generalization in Symbolic Mathematics." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8629–37. http://dx.doi.org/10.1609/aaai.v36i8.20841.

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Neural sequence models trained with maximum likelihood estimation have led to breakthroughs in many tasks, where success is defined by the gap between training and test performance. However, their ability to achieve stronger forms of generalization remains unclear. We consider the problem of symbolic mathematical integration, as it requires generalizing systematically beyond the training set. We develop a methodology for evaluating generalization that takes advantage of the problem domain's structure and access to a verifier. Despite promising in-distribution performance of sequence-to-sequenc

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5

Kelley, Troy D. "Symbolic and Sub-Symbolic Representations in Computational Models of Human Cognition." Theory & Psychology 13, no. 6 (2003): 847–60. http://dx.doi.org/10.1177/0959354303136005.

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6

Pasula, H. M., L. S. Zettlemoyer, and L. P. Kaelbling. "Learning Symbolic Models of Stochastic Domains." Journal of Artificial Intelligence Research 29 (July 21, 2007): 309–52. http://dx.doi.org/10.1613/jair.2113.

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In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics.

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7

Lunze, J., and J. Schröder. "Diagnosis Based on Symbolic Dynamical Models." IFAC Proceedings Volumes 33, no. 11 (2000): 285–90. http://dx.doi.org/10.1016/s1474-6670(17)37374-3.

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8

Brookes, A., and K. A. Stevens. "Symbolic grouping versus simple cell models." Biological Cybernetics 65, no. 5 (1991): 375–80. http://dx.doi.org/10.1007/bf00216971.

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9

Ohlsson, Stellan. "Localist models are already here." Behavioral and Brain Sciences 23, no. 4 (2000): 486–87. http://dx.doi.org/10.1017/s0140525x00443359.

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Localist networks are symbolic models, because their nodes refer to extra-mental objects and events. Hence, localist networks can be combined with symbolic computations to form hybrid models. Such models are already familiar and they are likely to represent the dominant type of cognitive model in the next few decades.

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10

Docquier, N., A. Poncelet, and P. Fisette. "ROBOTRAN: a powerful symbolic gnerator of multibody models." Mechanical Sciences 4, no. 1 (2013): 199–219. http://dx.doi.org/10.5194/ms-4-199-2013.

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Abstract. The computational efficiency of symbolic generation was at the root of the emergence of symbolic multibody programs in the eighties. At present, it remains an attractive feature of it since the exponential increase in modern computer performances naturally provides the opportunity to investigate larger systems and more sophisticated models for which real-time computation is a real asset. Nowadays, in the context of mechatronic multibody systems, another interesting feature of the symbolic approach appears when dealing with enlarged multibody models, i.e. including electrical actuator

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11

Chacón, Jose Emmanuel, and Oldemar Rodríguez. "Regression Models for Symbolic Interval-Valued Variables." Entropy 23, no. 4 (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 p

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12

Phong, Phạm Hồng, and Bùi Công Cường. "Symbolic Computational Models for Intuitionistic Linguistic Information." Journal of Computer Science and Cybernetics 32, no. 1 (2016): 31–45. http://dx.doi.org/10.15625/1813-9663/32/1/5984.

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In \cite{Cuong14, Phong14}, we first introduced the notion of intuitionistic linguistic labels. In this paper, we develop two symbolic computational models for intuitionistic linguistic labels (intuitionistic linguistic information). Various operators are proposed, their properties are also examined. Then, an application in group decision making using intuitionistic linguistic preference relations is discussed.

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13

Fisette, P., T. Postiau, L. Sass, and J. C. Samin. "FULLY SYMBOLIC GENERATION OF COMPLEX MULTIBODY MODELS*." Mechanics of Structures and Machines 30, no. 1 (2002): 31–82. http://dx.doi.org/10.1081/sme-120001477.

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14

Zhanatauov, S. U. "VERBAL, SYMBOLIC, MATHEMATICAL, SEMANTIC, BEHAVIORAL, COGNITIVE MODELS." Theoretical & Applied Science 113, no. 09 (2022): 169–74. http://dx.doi.org/10.15863/tas.2022.09.113.32.

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15

Kwatny, H. G., and G. L. Blankenship. "Symbolic construction of models for multibody dynamics." IEEE Transactions on Robotics and Automation 11, no. 2 (1995): 271–81. http://dx.doi.org/10.1109/70.370509.

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16

Pola, Giordano, Pierdomenico Pepe, and Maria Domenica Di Benedetto. "Symbolic Models for Networks of Control Systems." IEEE Transactions on Automatic Control 61, no. 11 (2016): 3663–68. http://dx.doi.org/10.1109/tac.2016.2528046.

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17

Jagtap, Pushpak, and Majid Zamani. "Symbolic models for retarded jump–diffusion systems." Automatica 111 (January 2020): 108666. http://dx.doi.org/10.1016/j.automatica.2019.108666.

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18

Zeng, Yifeng, and Kim-leng Poh. "Symbolic verification of multiagent graphical decision models." International Journal of Intelligent Systems 23, no. 11 (2008): 1177–95. http://dx.doi.org/10.1002/int.20313.

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19

Navarro, Vicente, Arianna Pucci, Erik Tengblad, Francesca Casini, and Laura Asensio. "Symbolic algebra integration of soil elastoplastic models." Computers and Geotechnics 164 (December 2023): 105834. http://dx.doi.org/10.1016/j.compgeo.2023.105834.

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20

do Nascimento, Rafaella L. S., Renata M. C. R. de Souza, and Francisco José de A. Cysneiros. "Generalized linear models for symbolic polygonal data." Knowledge-Based Systems 290 (April 2024): 111569. http://dx.doi.org/10.1016/j.knosys.2024.111569.

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21

Han, Sung Ho, Bang Nguyen, and Lyndon Simkin. "The dynamic models of consumers’ symbolic needs: in the context of restaurant brands." European Journal of Marketing 50, no. 7/8 (2016): 1348–76. http://dx.doi.org/10.1108/ejm-03-2015-0144.

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Purpose The purpose of this paper is to investigate the dynamic process and the meaning of symbolic consumption according to the three symbolic needs (i.e. status needs, social needs, status and social needs) to understand how symbolic messages are conveyed when consumers choose a brand. Design/methodology/approach This paper develops three dynamic models, categorized according to the consumers’ needs. The conceptual framework consists of the six constructs: collectivism/individualism, brand reputation, self-congruence, brand affect, brand identification and brand loyalty. Twelve hypotheses we

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22

LUCAS, PETER. "Symbolic diagnosis and its formalisation." Knowledge Engineering Review 12, no. 2 (1997): 109–46. http://dx.doi.org/10.1017/s0269888997002026.

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Diagnosis was among the first subjects investigated when digital computers became available. It still remains an important research area, in which several new developments have taken place in the last decade. One of these new developments is the use of detailed domain models in knowledge-based systems for the purpose of diagnosis, often referred to as “model-based” diagnosis. Typically, such models embody knowledge of the normal or abnormal structure and behaviour of the modelled objects in a domain. Models of the structure and workings of technical devices, and causal models of disease proces

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23

Stepanovic, Ivana, Dragica Pavlovic-Babic, and Zora Krnjaic. "The analysis of actual and symbolic models of secondary school students in Serbia." Zbornik Instituta za pedagoska istrazivanja 41, no. 2 (2009): 401–17. http://dx.doi.org/10.2298/zipi0902401s.

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This paper deals with role models of secondary school students in Serbia. In the course of adolescence, there is a gradual separation from parental figures, and other persons become role models for behavior. For that reason, secondary school population is of interest when analyzing this phenomenon, particularly bearing in mind that role models influence not only social, but also other aspects of development. We analyzed role models from students' personal (actual models) and public life (symbolic models). The main aim was to determine who their actual and symbolic models are, and why secondary

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24

Swikir, Abdalla, Navid Noroozi, and Majid Zamani. "Compositional Synthesis of Symbolic Models for Infinite Networks." IFAC-PapersOnLine 53, no. 2 (2020): 1868–73. http://dx.doi.org/10.1016/j.ifacol.2020.12.2356.

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25

Neto, Eufrásio de A. Lima, Gauss M. Cordeiro, and Francisco de A. T. de Carvalho. "Bivariate symbolic regression models for interval-valued variables." Journal of Statistical Computation and Simulation 81, no. 11 (2011): 1727–44. http://dx.doi.org/10.1080/00949655.2010.500470.

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26

Swikir, Abdalla, Antoine Girard, and Majid Zamani. "Symbolic Models for a Class of Impulsive Systems." IEEE Control Systems Letters 5, no. 1 (2021): 247–52. http://dx.doi.org/10.1109/lcsys.2020.3001501.

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27

Jackson, Janet L., Aladin Akyürek, and John A. Michon. "Symbolic and other Cognitive Models of Temporal Reality." Time & Society 2, no. 2 (1993): 241–56. http://dx.doi.org/10.1177/0961463x93002002006.

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28

Kirćanski, N., T. Davidović, and M. Vukobratović. "A contribution to parallelization of symbolic robot models." Robotica 13, no. 4 (1995): 411–21. http://dx.doi.org/10.1017/s0263574700018841.

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SummaryThis paper is focused on task scheduling in multiprocessor robot controllers. To minimize the input-output time delay our consideration is restricted to parallel architectures that include complete crossbar interconnection networks. In this paper, an efficient scheduling algorithm based on a heuristic function is considered. This function takes into account delays caused by interprocessor communication and minimizes both the execution time and the communication cost. Robot control computation based on a highly efficient customized symbolic method is decomposed into a large number of sim

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29

D�hmen, Gert, Ronald Herrmann, and Hergen Pargmann. "Translating VHDL into functional symbolic finite-state models." Formal Methods in System Design 7, no. 1-2 (1995): 125–48. http://dx.doi.org/10.1007/bf01383876.

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30

Pola, Giordano, Antoine Girard, and Paulo Tabuada. "Approximately bisimilar symbolic models for nonlinear control systems." Automatica 44, no. 10 (2008): 2508–16. http://dx.doi.org/10.1016/j.automatica.2008.02.021.

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31

Charrier, Tristan, Sophie Pinchinat, and FranÇois Schwarzentruber. "Symbolic model checking of public announcement protocols." Journal of Logic and Computation 29, no. 8 (2019): 1211–49. http://dx.doi.org/10.1093/logcom/exz023.

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Abstract We study the symbolic model checking problem against public announcement protocol logic (PAPL), featuring protocols with public announcements, arbitrary public announcements and group announcements. Technically, symbolic models are Kripke models whose accessibility relations are presented as programs described in a dynamic logic style with propositional assignments. We highlight the relevance of such symbolic models and show that the symbolic model checking problem against PAPL is A$_{\textrm{pol}}$Exptime-complete as soon as announcement protocols allow for either arbitrary announcem

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32

Marra, Giuseppe. "Bridging symbolic and subsymbolic reasoning with minimax entropy models." Intelligenza Artificiale 15, no. 2 (2022): 71–90. http://dx.doi.org/10.3233/ia-210088.

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In this paper, we investigate MiniMax Entropy models, a class of neural symbolic models where symbolic and subsymbolic features are seamlessly integrated. We show how these models recover classical algorithms from both the deep learning and statistical relational learning scenarios. Novel hybrid settings are defined and experimentally explored, showing state-of-the-art performance in collective classification, knowledge base completion and graph (molecular) data generation.

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33

Desain, Peter. "A connectionist and a traditional AI quantizer, symbolic versus sub-symbolic models of rhythm perception." Contemporary Music Review 9, no. 1-2 (1993): 239–54. http://dx.doi.org/10.1080/07494469300640471.

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34

Feistel, Rainer. "Self-Organisation of Prediction Models." Entropy 25, no. 12 (2023): 1596. http://dx.doi.org/10.3390/e25121596.

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Living organisms are active open systems far from thermodynamic equilibrium. The ability to behave actively corresponds to dynamical metastability: minor but supercritical internal or external effects may trigger major substantial actions such as gross mechanical motion, dissipating internally accumulated energy reserves. Gaining a selective advantage from the beneficial use of activity requires a consistent combination of sensual perception, memorised experience, statistical or causal prediction models, and the resulting favourable decisions on actions. This information processing chain origi

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35

Nørreklit, Hanne. "Managing individuality: myths versus art." Proceedings of Pragmatic Constructivism 1, no. 2 (2011): 35–44. http://dx.doi.org/10.7146/propracon.v1i2.16664.

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The purpose of this article is to establish the symbolic forms that are presently used in selected mainstream management models and to assess whether the connection between leadership and individual human reality would be improved if the management models were fundamentally inspired by those used by a successful manager and artist.The theoretical starting point of this article is Cassirer’s (Cassirer 1999) philosophy of symbolic forms. A symbolic form is “a way of having a life world” (own translation) (Cassirer 1999). In a symbolic form, a person discovers and unfolds an ability to build his

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36

Cline, Terry, Harold Abelson, and Warren Harris. "Symbolic computing in engineering design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 3, no. 3 (1989): 195–206. http://dx.doi.org/10.1017/s0890060400001220.

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Computer programs that combine traditional numeric methods with symbolic algebra and with specific knowledge of application-based techniques can provide new levels of computational support for engineering design. We illustrate this with a computer-based ‘control engineer’s assistant’. Although this program is focussed on control system design, it demonstrates techniques that should be widely applicable across many engineering disciplines. In particular, we show how, with symbolic computing, a computer-aided design system can usefully simulate engineering models early in the design process, bef

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37

Dickens, Charles, Connor Pryor, and Lise Getoor. "Modeling Patterns for Neural-Symbolic Reasoning Using Energy-based Models." Proceedings of the AAAI Symposium Series 3, no. 1 (2024): 90–99. http://dx.doi.org/10.1609/aaaiss.v3i1.31187.

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Neural-symbolic (NeSy) AI strives to empower machine learning and large language models with fast, reliable predictions that exhibit commonsense and trustworthy reasoning by seamlessly integrating neural and symbolic methods. With such a broad scope, several taxonomies have been proposed to categorize this integration, emphasizing knowledge representation, reasoning algorithms, and applications. We introduce a knowledge representation-agnostic taxonomy focusing on the neural-symbolic interface capturing methods that reason with probability, logic, and arithmetic constraints. Moreover, we deriv

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38

Liu, Chuang. "Symbolic versus Modelistic Elements in Scientific Modeling." THEORIA. An International Journal for Theory, History and Foundations of Science 30, no. 2 (2015): 287. http://dx.doi.org/10.1387/theoria.12863.

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In this paper, we argue that symbols (or the symbolic) are conventional vehicles whose chief function is denotation, while models (or the modelistic) are epistemic vehicles, and their chief function is to show what their targets are like in the relevant aspects. Although the same object may serve both functions, the two vehicles are <em>conceptually distinct</em> and <em>most</em> models employ both elements. Together with this point we offer an alternative to the deflationary view on scientific models. In addition, we point out there are non-referring models in which s

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39

Giannarakis, Nick, Alexandra Silva, and David Walker. "ProbNV: probabilistic verification of network control planes." Proceedings of the ACM on Programming Languages 5, ICFP (2021): 1–30. http://dx.doi.org/10.1145/3473595.

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ProbNV is a new framework for probabilistic network control plane verification that strikes a balance between generality and scalability. ProbNV is general enough to encode a wide range of features from the most common protocols (eBGP and OSPF) and yet scalable enough to handle challenging properties, such as probabilistic all-failures analysis of medium-sized networks with 100-200 devices. When there are a small, bounded number of failures, networks with up to 500 devices may be verified in seconds. ProbNV operates by translating raw CISCO configurations into a probabilistic and functional pr

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40

Coelho, A. A., J. Evans, I. Evans, A. Kern, and S. Parsons. "The TOPAS symbolic computation system." Powder Diffraction 26, S1 (2011): S22—S25. http://dx.doi.org/10.1154/1.3661087.

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Computer algebra removes much of the drudgery from mathematics; it allows users to formulate models by using the language of mathematics and to have those models evaluated with little effort. This symbolic form of representation is often thought of as being separate to dedicated computational programs such as Rietveld refinement. These dedicated programs are often written in low level languages; they are relatively inflexible in what they do and modifying them to change functionality in a small manner is often a major programming task. This paper describes a symbolic system that is integrated

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41

Sanner, Scott, and Ehsan Abbasnejad. "Symbolic Variable Elimination for Discrete and Continuous Graphical Models." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1954–60. http://dx.doi.org/10.1609/aaai.v26i1.8406.

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Probabilistic reasoning in the real-world often requires inference incontinuous variable graphical models, yet there are few methods for exact, closed-form inference when joint distributions are non-Gaussian. To address this inferential deficit, we introduce SVE -- a symbolic extension of the well-known variable elimination algorithm to perform exact inference in an expressive class of mixed discrete and continuous variable graphical models whose conditional probability functions can be well-approximated as piecewise combinations of polynomials with bounded support. Using this representation,

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42

Ben Hassan Saïdi, Ismaïl, Martin Schmelzer, Paola Cinnella, and Francesco Grasso. "CFD-driven symbolic identification of algebraic Reynolds-stress models." Journal of Computational Physics 457 (May 2022): 111037. http://dx.doi.org/10.1016/j.jcp.2022.111037.

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43

Girard, A., G. Pola, and P. Tabuada. "Approximately Bisimilar Symbolic Models for Incrementally Stable Switched Systems." IEEE Transactions on Automatic Control 55, no. 1 (2010): 116–26. http://dx.doi.org/10.1109/tac.2009.2034922.

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44

Zamani, Majid, Giordano Pola, Manuel Mazo, and Paulo Tabuada. "Symbolic Models for Nonlinear Control Systems Without Stability Assumptions." IEEE Transactions on Automatic Control 57, no. 7 (2012): 1804–9. http://dx.doi.org/10.1109/tac.2011.2176409.

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45

Meyer, Pierre-Jean, Antoine Girard, and Emmanuel Witrant. "Compositional Abstraction and Safety Synthesis Using Overlapping Symbolic Models." IEEE Transactions on Automatic Control 63, no. 6 (2018): 1835–41. http://dx.doi.org/10.1109/tac.2017.2753039.

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46

Bozzano, Marco, Alessandro Cimatti, Oleg Lisagor, et al. "Safety assessment of AltaRica models via symbolic model checking." Science of Computer Programming 98 (February 2015): 464–83. http://dx.doi.org/10.1016/j.scico.2014.06.003.

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47

Krupski, Vladimir N. "On symbolic models for Single-Conclusion Logic of Proofs." Sbornik: Mathematics 202, no. 5 (2011): 683–95. http://dx.doi.org/10.1070/sm2011v202n05abeh004161.

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48

Balarin, Felice. "Concurrent Symbolic Verification of Liveness Properties for Interleaved Models." Electronic Notes in Theoretical Computer Science 23, no. 2 (2001): 1–10. http://dx.doi.org/10.1016/s1571-0661(04)80664-9.

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49

Prudêncio, Ricardo B. C., Teresa B. Ludermir, and Francisco de A. T. de Carvalho. "A Modal Symbolic Classifier for selecting time series models." Pattern Recognition Letters 25, no. 8 (2004): 911–21. http://dx.doi.org/10.1016/j.patrec.2004.02.004.

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50

Pola, Giordano, and Paulo Tabuada. "Symbolic Models for Nonlinear Control Systems: Alternating Approximate Bisimulations." SIAM Journal on Control and Optimization 48, no. 2 (2009): 719–33. http://dx.doi.org/10.1137/070698580.

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