Relevant bibliographies by topics / Symbolic models

Contents

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

Academic literature on the topic 'Symbolic models'

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Published: 25 May 2024

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Journal articles on the topic "Symbolic models"

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

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Tabuada, Paulo. "Symbolic models for control systems." Acta Informatica 43, no. 7 (January 16, 2007): 477–500. http://dx.doi.org/10.1007/s00236-006-0036-6.

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Fang, Meng, Shilong Deng, Yudi Zhang, Zijing Shi, Ling Chen, Mykola Pechenizkiy, and Jun Wang. "Large Language Models Are Neurosymbolic Reasoners." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (March 24, 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 initializing the LLM agent and informing it of its role. The agent then receives observations and a set of valid actions from the text-based games, along with a specific symbolic module. With these inputs, the LLM agent chooses an action and interacts with the game environments. Our experimental results demonstrate that our method significantly enhances the capability of LLMs as automated agents for symbolic reasoning, and our LLM agent is effective in text-based games involving symbolic tasks, achieving an average performance of 88% across all tasks.
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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 (June 28, 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-sequence models in this domain, we demonstrate challenges in achieving robustness, compositionality, and out-of-distribution generalization, through both carefully constructed manual test suites and a genetic algorithm that automatically finds large collections of failures in a controllable manner. Our investigation highlights the difficulty of generalizing well with the predominant modeling and learning approach, and the importance of evaluating beyond the test set, across different aspects of generalization.
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Kelley, Troy D. "Symbolic and Sub-Symbolic Representations in Computational Models of Human Cognition." Theory & Psychology 13, no. 6 (December 2003): 847–60. http://dx.doi.org/10.1177/0959354303136005.

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

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

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Ohlsson, Stellan. "Localist models are already here." Behavioral and Brain Sciences 23, no. 4 (August 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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Docquier, N., A. Poncelet, and P. Fisette. "ROBOTRAN: a powerful symbolic gnerator of multibody models." Mechanical Sciences 4, no. 1 (May 2, 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 actuators, hydraulic devices, pneumatic suspensions, etc. and requiring specific analyses like control and optimization. Indeed, since symbolic multibody programs clearly distinguish the modeling phase from the analysis process, extracting the symbolic model, as well as some precious ingredients like analytical sensitivities, in order to export it towards any suitable environment (for control or optimization purposes) is quite straightforward. Symbolic multibody model portability is thus very attractive for the analysis of mechatronic applications. In this context, the main features and recent developments of the ROBOTRAN software developed at the Université catholique de Louvain (Belgium) are reviewed in this paper and illustrated via three multibody applications which highlight its capabilities for dealing with very large systems and coping with multiphysics issues.
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Dissertations / Theses on the topic "Symbolic models"

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Porter, Mark A. "Evolving inferential fermentation models using symbolic annealing." Thesis, University of Newcastle Upon Tyne, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275517.

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Devereux, Benet. "Finite-state models with multiplicities, symbolic representation and reasoning." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ62961.pdf.

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Townsend, Duncan Clarke McIntire. "Using a symbolic language parser to Improve Markov language models." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100621.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 31-32).
This thesis presents a hybrid approach to natural language processing that combines an n-gram (Markov) model with a symbolic parser. In concert these two techniques are applied to the problem of sentence simplification. The n-gram system is comprised of a relational database backend with a frontend application that presents a homogeneous interface for both direct n-gram lookup and Markov approximation. The query language exposed by the frontend also applies lexical information from the START natural language system to allow queries based on part of speech. Using the START natural language system's parser, English sentences are transformed into a collection of structural, syntactic, and lexical statements that are uniquely well-suited to the process of simplification. After reducing the parse of the sentence, the resulting expressions can be processed back into English. These reduced sentences are ranked by likelihood by the n-gram model.
by Duncan Clarke McIntire Townsend.
M. Eng.
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Zaner, Frederick Steven. "The development of symbolic models and their extension into space." The Ohio State University, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=osu1303495012.

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Keyton, Michael M. (Michael Murray). "The Development and Interpretation of Several Symbolic Models of Thought." Thesis, North Texas State University, 1986. https://digital.library.unt.edu/ark:/67531/metadc331860/.

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Philosophical and physiological investigations define thought to be the result of thinking. psychological Inquiry has mainly focused on discovery of the mechanisms and topology of thought. Philosophical Inquiry either has explored the mind-body problem or has analyzed the linguistics of the expression of a thought. However, neither has Investigated adequately phenomenal characteristics of thought Itself, the Intermediary between the production and the expression of a thought. The use of thought to analyze phenomenal characteristics of thought engenders a paradox. If the expression of thought requires finite series of linked words with rules governing syntax, then analysis of both the thought and the expression of the thought must necessarily transcend the linguistic level. During the last century many examples of logical paradoxes In linguistics of thought have been given. The culminating difficulty of dealing with a finite structure, a characteristic of any language, Is Godel's Incompleteness Theorem, which says in essence that in order to render all decisions about a finite system requires the use of material outside the system. Thus, a potentially complete interpretation of thought must use some technique which is basically non-linguistic . Wittgenstein proposed such a method with his "Picture theory. " This technique solves the major paradoxical problem generated by investigation of a reflective system using the system itself , but leaves unsolved the question of ultimate resolution . Using pictorial models with examples to assist in understanding phenomenal characteristics of thought, this paper investigates basic units of thought, attempting to identify properties of a basic unit of thought and of the collection of thoughts for a person, and analyzes relationships and interactions between units of thought.
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Kamienny, Pierre-Alexandre. "Efficient adaptation of reinforcement learning agents : from model-free exploration to symbolic world models." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS412.

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L'apprentissage par renforcement (RL) est un ensemble de techniques utilisées pour former des agents autonomes à interagir avec des environnements de manière à maximiser leur récompense. Pour déployer avec succès ces agents dans des scénarios réels, il est crucial qu'ils puissent généraliser à des situations inconnues. Bien que les réseaux de neurones aient montré des résultats prometteurs en permettant aux agents d'interpoler des comportements souhaités, leurs limites en termes de généralisation au-delà de la distribution d'entraînement entraînent souvent des performances sous-optimales sur des données issue d'une distribution différente. Ces défis sont encore amplifiés dans les environnements de RL caractérisés par des situations non stationnaires et des changements constants de la distribution lors du déploiement. Cette thèse présente de nouvelles stratégies dans le cadre du meta-RL visant à doter les agents RL de la capacité à s'adapter sur des tâches différentes du domaine d'entraînement. La première partie de la thèse se concentre sur les techniques model-free, c'est à dire qui ne modélisent pas explicitement l'environnement, pour apprendre des stratégies d'exploration efficaces. Nous examinons deux scénarios : dans le premier, l'agent dispose d'un ensemble de tâches d'entraînement, ce qui lui permet de modéliser explicitement les tâches et d'apprendre des représentations de tâches généralisables ; dans le second, l'agent apprend sans récompense à maximiser la couverture de l'espace des états. Dans la deuxième partie, nous explorons l'application de la régression symbolique, un outil puissant pour développer des modèles prédictifs offrant une interprétabilité et une meilleure robustesse face aux changements de distribution. Ces modèles sont ensuite intégrés aux agents model-based pour améliorer la modélisation de la dynamique. De plus, cette recherche contribue au domaine de la régression symbolique en introduisant une collection de techniques exploitant les modèles génératifs, en particulier le Transformer, ce qui améliore leur précision et leur efficacité. En résumé, cette thèse aborde abordant le défi de la généralisation et adaptation dans le RL. Elle développe des techniques visant à permettre aux agents meta-RL de s'adapter à des tâches hors domaine, facilitant ainsi leur déploiement dans des scénarios du monde réel
Reinforcement Learning (RL) encompasses a range of techniques employed to train autonomous agents to interact with environments with the purpose of maximizing their returns across various training tasks. To ensure successful deployment of RL agents in real-world scenarios, achieving generalization and adaptation to unfamiliar situations is crucial. Although neural networks have shown promise in facilitating in-domain generalization by enabling agents to interpolate desired behaviors, their limitations in generalizing beyond the training distribution often lead to suboptimal performance on out-of-distribution data. These challenges are further amplified in RL settings characterized by non-stationary environments and constant distribution shifts during deployment. This thesis presents novel strategies within the framework of Meta-Reinforcement Learning, aiming to equip RL agents with the ability to adapt at test-time to out-of-domain tasks. The first part of the thesis focuses on model-free techniques to learn effective exploration strategies. We consider two scenarios: one where the agent is provided with a set of training tasks, enabling it to explicitly model and learn generalizable task representations; and another where the agent learns without rewards to maximize its state coverage. In the second part, we investigate into the application of symbolic regression, a powerful tool for developing predictive models that offer interpretability and exhibit enhanced robustness against distribution shifts. These models are subsequently integrated within model-based RL agents to improve their performance. Furthermore, this research contributes to the field of symbolic regression by introducing a collection of techniques that leverage Transformer models, enhancing their accuracy and effectiveness. In summary, by addressing the challenges of adaptation and generalization in RL, this thesis focuses on the understanding and application of Meta-Reinforcement Learning strategies. It provides insights and techniques for enabling RL agents to adapt seamlessly to out-of-domain tasks, ultimately facilitating their successful deployment in real-world scenarios
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Lampka, Kai. "A symbolic approach to the state graph based analysis of high-level Markov reward models." [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=985513926.

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RANJAN, MUKESH. "AUTOMATED LAYOUT-INCLUSIVE SYNTHESIS OF ANALOG CIRCUITS USING SYMBOLIC PERFORMANCE MODELS." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1129922496.

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Ivanova, Elena. "Efficient Synthesis of Safety Controllers using Symbolic Models and Lazy Algorithms." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG088.

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Cette thèse porte sur le développement d'approches efficaces de synthèse de contrôleurs basées sur l'abstraction pour les systèmes cyber-physiques (CPS). Alors que les méthodes basées sur l'abstraction pour la conception de CPS ont fait l'objet de recherches intensives au cours des dernières décennies, l'évolutivité de ces techniques reste un problème. Cette thèse se concentre sur le développement d'algorithmes de synthèse paresseuse pour les spécifications de sécurité. Les spécifications de sécurité consistent à maintenir la trajectoire du système à l'intérieur d'un ensemble sûr donné. Cette spécification est de la plus haute importance dans de nombreux problèmes d'ingénierie, souvent prioritaires par rapport à d'autres exigences de performance. Les approches paresseuses surpassent l'algorithme de synthèse classique [Tabuada, 2009] en évitant les calculs, qui ne sont pas essentiels pour les objectifs de synthèse. Le chapitre 1 motive la thèse et présente l'état de l'art. Le chapitre 2 structure les approches de synthèse paresseuses existantes et met l'accent sur trois sources d'efficacité : les informations sur les états contrôlables a priori, les priorités sur les entrées et les états non accessibles à partir de l'ensemble initial. Le chapitre 3 propose un algorithme, qui explore itérativement les états à la frontière du domaine contrôlable tout en évitant l'exploration des états internes, en supposant qu'ils sont contrôlables en toute sécurité a priori. Un contrôleur de sécurité en boucle fermée pour le problème d'origine est alors défini comme suit : nous utilisons le contrôleur abstrait pour repousser le système d'un état limite vers l'intérieur, tandis que pour les états internes, toute entrée admissible est valide. Le chapitre 4 présente un algorithme qui restreint les calculs de synthèse du contrôleur aux seuls états atteignables tout en privilégiant les transitions de plus longue durée. Le système original est abstrait par un modèle symbolique avec une grille adaptative. De plus, un nouveau type d'échantillonnage temporel est également envisagé. Au lieu d'utiliser des transitions de durée prédéterminée, la durée des transitions est contrainte par des intervalles d'état qui doivent contenir l'ensemble accessible. Le chapitre 5 est consacré aux systèmes de transition monotones. L'approche de synthèse paresseuse introduite bénéficie d'une propriété monotone des systèmes de transition et de la structure ordonnée de l'espace d'état (d'entrée), et du fait que des spécifications de sécurité dirigées sont prises en compte. La classe de spécifications considérée s'enrichit alors d'intersections d'exigences de sécurité supérieures et inférieures fermées. Le chapitre 6 conclut la discussion et soulève de nouvelles questions pour les recherches futures
This thesis focuses on the development of efficient abstraction-based controller synthesis approaches for cyber-physical systems (CPS). While abstraction-based methods for CPS design have been the subject of intensive research over the last decades, the scalability of these techniques remains an issue. This thesis focus on developing lazy synthesis algorithms for safety specifications. Safety specifications consist in maintaining the trajectory of the system inside a given safe set. This specification is of the utmost importance in many engineering problems, often prioritized over other performance requirements. Lazy approaches outperform the classical synthesis algorithm [Tabuada, 2009] by avoiding computations, which are non-essential for synthesis goals. Chapter 1 motivates the thesis and discusses the state of the art. Chapter 2 structures the existing lazy synthesis approaches and emphasizes three sources of efficiency: information about a priori controllable states, priorities on inputs, and non-reachable from initial set states. Chapter 3 proposes an algorithm, which iteratively explores states on the boundary of controllable domain while avoiding exploration of internal states, supposing that they are safely controllable a priory. A closed-loop safety controller for the original problem is then defined as follows: we use the abstract controller to push the system from a boundary state back towards the interior, while for inner states, any admissible input is valid. Chapter 4 presents an algorithm that restricts the controller synthesis computations to reachable states only while prioritizing longer-duration transitions. The original system is abstracted by a symbolic model with an adaptive grid. Moreover, a novel type of time sampling is also considered. Instead of using transitions of predetermined duration, the duration of the transitions is constrained by state intervals that must contain the reachable set. Chapter 5 is dedicated to monotone transition systems. The introduced lazy synthesis approach benefits from a monotone property of transition systems and the ordered structure of the state (input) space, and the fact that directed safety specifications are considered. The considered class of specifications is then enriched by intersections of upper and lower-closed safety requirements. Chapter 6 concludes the discussion and raises new issues for future research
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Salgado, Mauricio. "More than words : computational models of emergence and evolution of symbolic communication." Thesis, University of Surrey, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556464.

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The study of symbolic communication is a key research area in both the social and natural sciences. However, little has been done in order to bridge these scientific do- mains, so an unfortunate gulf between them still persists. Even less has been done in the field of computational sociology, in which most research using agent-based mod- els has disregarded the importance of symbolic communication. It is this lacuna that the thesis addresses. In the thesis, it is claimed that the type of emergent properties that are inherent to social phenomena are likely to result from the unique fact that the participating en- tities are symbolic agents. It is proposed that symbolic communication is a threshold phenomenon that emerges in the intersections among human cognition, social inter- actions and human biology. A theoretical framework with which to clarify this con- nection is also presented. In order to test in silica some hypotheses derived from this theoretical framework, the analysis relies upon two agent-based models. Different simulation methods and techniques were used, such as reinforcement learning algorithms, genetic algorithms, graph theory, and evolutionary game theory. To investigate the simulation results, multivariate analysis techniques, social network analysis and differential equations were used. The first agent-based model was developed to study the properties of an emergent communication system, in which groups of 'speechless' agents create local lexicons and compete with each other to spread them throughout the whole population. The model results indicate that a common lexicon can emerge on the condition that a group of agents develops a communicative strategy that favours their mutual understand- ing and allows them to reach more recipients for their utterances. An analysis of the agents' social networks reveals that strong mutual relations among agents from the same group, high 'immunity' to external influence and high capability of speaking to agents from different groups play a fundamental role in the process of spreading lexicons. The second agent-based model was built to study the pre-linguistic stage of cooper- ation among individuals required for the emergence of symbolic communication. In this model, agents reproduce sexually, males and females differ in their reproductive costs and they play the iterated prisoner's dilemma. The model results show that, when male reproductive costs are less than female reproductive costs, males cooper- ate with females even when females do not reciprocate. This non-reciprocal coopera- tion, in turn, produces a sustained population growth, because females can reproduce faster despite their high reproductive costs .. Finally, a mathematical model of cumulative cultural evolution is used to investigate different patterns of population dynamics, and it is demonstrated that the artificial so- cieties in which non-reciprocal cooperation emerges are able to sustain more complex cultural artefacts, such as communicative symbols. Linking computational sociology to appropriate theories of language evolution, com- munication, evolutionary biology and cognitive research, the thesis provides concept- ually grounded mechanisms to explain the emergence and evolution of symbolic com- munication. In so doing, the thesis contributes both substantively and methodologic- ally to academic work on computational sociology, as well as agent-based models of symbolic communication. Key words: Agent-Based Modelling, Computational Sociology, Game Theory, Co- operative Breeding, Cultural Evolution, Cultural Cognition, Emergence, Lexicons, Symbolic Communication.
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Books on the topic "Symbolic models"

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Herdt, Vladimir. Complete Symbolic Simulation of SystemC Models. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-12680-3.

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Edwin, Kreuzer, ed. Computerized symbolic manipulation in mechanics. Wien: Springer-Verlag, 1994.

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Wolf, Holger C. Anti-tax revolutions and symbolic prosecutions. Cambridge, MA: National Bureau of Economic Research, 1993.

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Wolf, Holger. Anti-tax revolutions and symbolic prosecutions. Cambridge, Mass: National Bureau of EconomicResearch, 1993.

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Turner, Raymond. Computable models. London: Springer, 2009.

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Kossak, Roman. The structure of models of Peano arithmetic. Oxford: Clarendon, 2006.

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Heckel, J. S. A methodology for linking symbolic and graphical models for collaborative engineering. [Champaign, IL]: US Army Corps of Engineers, Construction Engineering Research Laboratories, 1996.

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Hees, Martin van. Rights and decisions: Formal models of law and liberalism. Dordrecht: Kluwer Academic, 1995.

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Krynicki, Michał. Quantifiers: Logics, Models and Computation: Volume Two: Contributions. Dordrecht: Springer Netherlands, 1995.

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Clote, Peter. Boolean Functions and Computation Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002.

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Book chapters on the topic "Symbolic models"

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Makridis, Odysseus. "Semantic Models for ∏: ∏⧉." In Symbolic Logic, 351–71. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67396-3_7.

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Chipere, Ngoni. "Connectionist and Symbolic Models." In Understanding Complex Sentences, 70–87. London: Palgrave Macmillan UK, 2003. http://dx.doi.org/10.1057/9780230005884_4.

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Herdt, Vladimir. "Heuristic Symbolic Subsumption." In Complete Symbolic Simulation of SystemC Models, 69–95. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-12680-3_6.

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Schreiner, Wolfgang. "Building Models." In Texts & Monographs in Symbolic Computation, 101–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80507-4_4.

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Floberg, Henrik. "Transistor Models." In Symbolic Analysis in Analog Integrated Circuit Design, 75–82. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6211-5_7.

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Deininger, David, Rayna Dimitrova, and Rupak Majumdar. "Symbolic Model Checking for Factored Probabilistic Models." In Automated Technology for Verification and Analysis, 444–60. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46520-3_28.

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Castillo, Enrique, José Manuel Gutiérrez, and Ali S. Hadi. "Symbolic Propagation of Evidence." In Expert Systems and Probabilistic Network Models, 443–80. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-2270-5_10.

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Siddiqui, Junaid Haroon, and Sarfraz Khurshid. "Symbolic Execution of Alloy Models." In Formal Methods and Software Engineering, 340–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24559-6_24.

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Przigoda, Nils, Robert Wille, Judith Przigoda, and Rolf Drechsler. "A Symbolic Formulation for Models." In Automated Validation & Verification of UML/OCL Models Using Satisfiability Solvers, 25–94. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72814-8_3.

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Aberer, Karl. "Combinatory models and symbolic computation." In Design and Implementation of Symbolic Computation Systems, 116–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57272-4_29.

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Conference papers on the topic "Symbolic models"

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Khalil, Amal, and Juergen Dingel. "Incremental symbolic execution of evolving state machines." In 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2015. http://dx.doi.org/10.1109/models.2015.7338231.

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Greenyer, Joel, and Timo Gutjahr. "Symbolic Execution for Realizability-Checking of Scenario-Based Specifications." In 2017 ACM/IEEE 20th International Conference on Model-Driven Engineering Languages and Systems (MODELS). IEEE, 2017. http://dx.doi.org/10.1109/models.2017.35.

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Chang, Felix Sheng-Ho, and Daniel Jackson. "Symbolic model checking of declarative relational models." In Proceeding of the 28th international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1134285.1134329.

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DeVries, Byron, and Betty H. C. Cheng. "Automatic detection of incomplete requirements via symbolic analysis." In MODELS '16: ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2976767.2976791.

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Cederbladh, Johan, Loek Cleophas, Eduard Kamburjan, Lucas Lima, and Hans Vangheluwe. "Symbolic Reasoning for Early Decision-Making in Model-Based Systems Engineering." In 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2023. http://dx.doi.org/10.1109/models-c59198.2023.00117.

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James, Steven. "Learning Portable Symbolic Representations." 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.2018/826.

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An open question in artificial intelligence is how to learn useful representations of the real world. One approach is to learn symbols, which represent the world and its contents, as well as models describing the effects on these symbols when interacting with the world. To date, however, research has investigated learning such representations for a single specific task. Our research focuses on approaches to learning these models in a domain-independent manner. We intend to use these symbolic models to build even higher levels of abstraction, creating a hierarchical representation which could be used to solve complex tasks. This would allow an agent to gather knowledge over the course of its lifetime, which could then be leveraged when faced with a new task, obviating the need to relearn a model every time a new unseen problem is encountered.
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Baras, Karolina, A. Moreira, and F. Meneses. "Navigation based on symbolic space models." In 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2010. http://dx.doi.org/10.1109/ipin.2010.5646810.

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Jacomme, Charlie, Steve Kremer, and Guillaume Scerri. "Symbolic Models for Isolated Execution Environments." In 2017 IEEE European Symposium on Security and Privacy (EuroS&P). IEEE, 2017. http://dx.doi.org/10.1109/eurosp.2017.16.

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Jeon, Jinseong, Xiaokang Qiu, Jonathan Fetter-Degges, Jeffrey S. Foster, and Armando Solar-Lezama. "Synthesizing framework models for symbolic execution." In ICSE '16: 38th International Conference on Software Engineering. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2884781.2884856.

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Zhao, Siang, Zhongyang Li, Zhenbang Chen, and Ji Wang. "Symbolic Verification of Fuzzy Logic Models." In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 2023. http://dx.doi.org/10.1109/ase56229.2023.00087.

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Reports on the topic "Symbolic models"

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VanLehn, Kurt. Analysis of Symbolic Parameter Models. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada261930.

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Yang, Bwolen, Reid Simmons, Randal E. Bryant, and David R. O'Hallaron. Optimizing Symbolic Model Checking for Constraint-Rich Models. Fort Belvoir, VA: Defense Technical Information Center, March 1999. http://dx.doi.org/10.21236/ada363778.

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Campos, Sergio V., and Edmund M. Clarke. Real-Time Symbolic Model Checking for Discrete Time Models. Fort Belvoir, VA: Defense Technical Information Center, May 1994. http://dx.doi.org/10.21236/ada282878.

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Gardner, Daniel. Symbolic Processor Based Models of Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada200200.

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Polk, Thad A., Kurt VanLehn, and Dirk Kalp. ASPM2: Progress Toward the Analysis of Symbolic Parameter Models. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada284437.

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VanLehn, Kurt. Analysis of Symbolic Models of Cognition Project (ASPM-Pitt). Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada255929.

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Sayer, Catherine, and Martin Doherty. The classic model room task: A symbol that doesn’t measure symbolism. Peeref, June 2023. http://dx.doi.org/10.54985/peeref.2306p4947936.

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Biere, Armin, Alessandro Cimatti, Edmund Clark, and Yunshan Zhu. Symbolic Model Checking without BDDs. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada360973.

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Govindaraju, Shankar G., and David L. Dill. Approximate Symbolic Model Checking Using Overlapping Projections. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada401014.

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Baader, Franz, and Klaus U. Schulz. Unification Theory - An Introduction. Aachen University of Technology, 1997. http://dx.doi.org/10.25368/2022.135.

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Equational unification is a generalization of syntactic unification in which semantic properties of function symbols are taken into account. For example, assume that the function symbol '+' is known to be commutative. Given the unication problem x + y ≐ a + b (where x and y are variables, and a and b are constants), an algorithm for syntactic unification would return the substitution {x ↦ a; y ↦ b} as the only (and most general) unifier: to make x + y and a + b syntactically equal, one must replace the variable x by a and y by b. However, commutativity of '+' implies that {x ↦ b; y ↦ b} also is a unifier in the sense that the terms obtained by its application, namely b + a and a + b, are equal modulo commutativity of '+'. More generally, equational unification is concerned with the problem of how to make terms equal modulo a given equational theory, which specifies semantic properties of the function symbols that occur in the terms to be unified.
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