Relevant bibliographies by topics / Artificial symbol learning

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

Academic literature on the topic 'Artificial symbol learning'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Artificial symbol learning"

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Pollack, Courtney. "Same-different judgments with alphabetic characters: The case of literal symbol processing." Journal of Numerical Cognition 5, no. 2 (August 22, 2019): 241–59. http://dx.doi.org/10.5964/jnc.v5i2.163.

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Learning mathematics requires fluency with symbols that convey numerical magnitude. Algebra and higher-level mathematics involve literal symbols, such as "x", that often represent numerical magnitude. Compared to other symbols, such as Arabic numerals, literal symbols may require more complex processing because they have strong pre-existing associations in literacy. The present study tested this notion using same-different tasks that produce less efficient judgments for different magnitudes that are closer together compared to farther apart (i.e., same-different distance effects). Twenty-four adolescents completed three same-different tasks using Arabic numerals, literal symbols, and artificial symbols. All three symbolic formats produced same-different distance effects, showing literal and artificial symbol processing of numerical magnitude. Importantly, judgments took longer for literal symbols than artificial symbols on average, suggesting a cost specific to literal symbol processing. Taken together, results suggest that literal symbol processing differs from processing of other symbols that represent numerical magnitude.
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Ahmetoglu, Alper, M. Yunus Seker, Justus Piater, Erhan Oztop, and Emre Ugur. "DeepSym: Deep Symbol Generation and Rule Learning for Planning from Unsupervised Robot Interaction." Journal of Artificial Intelligence Research 75 (November 6, 2022): 709–45. http://dx.doi.org/10.1613/jair.1.13754.

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Symbolic planning and reasoning are powerful tools for robots tackling complex tasks. However, the need to manually design the symbols restrict their applicability, especially for robots that are expected to act in open-ended environments. Therefore symbol formation and rule extraction should be considered part of robot learning, which, when done properly, will offer scalability, flexibility, and robustness. Towards this goal, we propose a novel general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them for non-trivial action planning. Our robot interacts with objects using an initial action repertoire that is assumed to be acquired earlier and observes the effects it can create in the environment. To form action-grounded object, effect, and relational categories, we employ a binary bottleneck layer in a predictive, deep encoderdecoder network that takes the image of the scene and the action applied as input, and generates the resulting effects in the scene in pixel coordinates. After learning, the binary latent vector represents action-driven object categories based on the interaction experience of the robot. To distill the knowledge represented by the neural network into rules useful for symbolic reasoning, a decision tree is trained to reproduce its decoder function. Probabilistic rules are extracted from the decision paths of the tree and are represented in the Probabilistic Planning Domain Definition Language (PPDDL), allowing off-the-shelf planners to operate on the knowledge extracted from the sensorimotor experience of the robot. The deployment of the proposed approach for a simulated robotic manipulator enabled the discovery of discrete representations of object properties such as ‘rollable’ and ‘insertable’. In turn, the use of these representations as symbols allowed the generation of effective plans for achieving goals, such as building towers of the desired height, demonstrating the effectiveness of the approach for multi-step object manipulation. Finally, we demonstrate that the system is not only restricted to the robotics domain by assessing its applicability to the MNIST 8-puzzle domain in which learned symbols allow for the generation of plans that move the empty tile into any given position.
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Baek, Sung-Bum, Jin-Gon Shon, and Ji-Su Park. "CAC: A Learning Context Recognition Model Based on AI for Handwritten Mathematical Symbols in e-Learning Systems." Mathematics 10, no. 8 (April 12, 2022): 1277. http://dx.doi.org/10.3390/math10081277.

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The e-learning environment should support the handwriting of mathematical expressions and accurately recognize inputted handwritten mathematical expressions. To this end, expression-related information should be fully utilized in e-learning environments. However, pre-existing handwritten mathematical expression recognition models mainly utilize the shape of handwritten mathematical symbols, thus limiting the models from improving the recognition accuracy of a vaguely represented symbol. Therefore, in this paper, a context-aided correction (CAC) model is proposed that adjusts an output of handwritten mathematical symbol (HMS) recognition by additionally utilizing information related to the HMS in an e-learning system. The CAC model collects learning contextual data associated with the HMS and converts them into learning contextual information. Next, contextual information is recognized through artificial intelligence to adjust the recognition output of the HMS. Finally, the CAC model is trained and tested using a dataset similar to that of a real learning situation. The experiment results show that the recognition accuracy of handwritten mathematical symbols is improved when using the CAC model.
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YANG, DERSHUNG, LARRY A. RENDELL, JULIE L. WEBSTER, DORIS S. SHAW, and JAMES H. GARRETT. "SYMBOL RECOGNITION IN A CAD ENVIRONMENT USING A NEURAL NETWORK." International Journal on Artificial Intelligence Tools 03, no. 02 (June 1994): 157–85. http://dx.doi.org/10.1142/s0218213094000091.

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A new neural network called AUGURS is designed to assist a user of a Computer-Aided Design system in utilizing standard graphic symbols. With AUGURS, the CAD user can avoid searching for standard symbols in a large library and rely on AUGURS to automatically retrieve those symbols resembling the user’s drawing. More specifically, AUGURS inputs a bitmap image normalized with respect to location, size, and orientation, and outputs a list of standard symbols ranked by its assessment of the similarity between the symbol and the input image. Only the top ranked symbols are presented to the user for selection. AUGURS encodes geometric knowledge into its network structure and carefully balances its discriminant power and noise tolerance. The encoded knowledge enables AUGURS to learn reasonably well despite the limited number of training examples, the most serious challenge for the CAD domain. We have compared AUGURS with the Zipcode Net, a traditional layered feed-forward network with an unconstrained structure, and a network that inputs either Zernike or pseudo-Zernike moments. The experimental results conclude that AUGURS can achieve the best recognition performance among all networks being compared with reasonable recognition and learning efficiency.
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Crespo, Kimberly, and Margarita Kaushanskaya. "The Role of Attention, Language Ability, and Language Experience in Children's Artificial Grammar Learning." Journal of Speech, Language, and Hearing Research 65, no. 4 (April 4, 2022): 1574–91. http://dx.doi.org/10.1044/2021_jslhr-21-00112.

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Purpose: The current study examined the role of attention and language ability in nonverbal rule induction performance in a demographically diverse sample of school-age children. Method: The participants included 43 English-speaking monolingual and 65 Spanish–English bilingual children between the ages of 5 and 9 years. Core Language Index standard scores from the Clinical Evaluation of Language Fundamentals–Fourth Edition indexed children's language skills. Rule induction was measured via a visual artificial grammar learning task. Two equally complex finite-state artificial grammars were used. Children learned one grammar in a low attention condition (where children were exposed to symbol sequences with no distractors) and another grammar in a high attention condition (where distractor symbols were presented around the perimeter of the target symbol sequences). Results: Overall, performance in the high attention condition was significantly worse than performance in the low attention condition. Children with robust language skills performed significantly better in the high attention condition than children with weaker language skills. Despite group differences in socioeconomic status, English language skills, and nonverbal intelligence, monolingual and bilingual children performed similarly to each other in both conditions. Conclusion: The results suggest that the ability to extract rules from visual input is attenuated by the presence of competing visual information and that language ability, but not bilingualism, may influence rule induction.
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KITANI, KRIS M., YOICHI SATO, and AKIHIRO SUGIMOTO. "RECOVERING THE BASIC STRUCTURE OF HUMAN ACTIVITIES FROM NOISY VIDEO-BASED SYMBOL STRINGS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 08 (December 2008): 1621–46. http://dx.doi.org/10.1142/s0218001408006776.

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In recent years stochastic context-free grammars have been shown to be effective in modeling human activities because of the hierarchical structures they represent. However, most of the research in this area has yet to address the issue of learning the activity grammars from a noisy input source, namely, video. In this paper, we present a framework for identifying noise and recovering the basic activity grammar from a noisy symbol string produced by video. We identify the noise symbols by finding the set of non-noise symbols that optimally compresses the training data, where the optimality of compression is measured using an MDL criterion. We show the robustness of our system to noise and its effectiveness in learning the basic structure of human activity, through experiments with artificial data and a real video sequence from a local convenience store.
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Inkelas, Sharon, Keith Johnson, Charles Lee, Emil Minas, George Mulcaire, Gek Yong Keng, and Tomomi Yuasa. "Testing the Learnability of Writing Systems." Annual Meeting of the Berkeley Linguistics Society 39, no. 1 (December 16, 2013): 75. http://dx.doi.org/10.3765/bls.v39i1.3871.

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In lieu of an abstract, here is a brief excerpt:The world’s sound-based writing systems differ according to the size of the typical speech chunk which is mapped to a symbol: the phone, in so-called alphabetic writing systems, and the mora, demisyllable or syllable, in so-called syllabaries. This paper reports the results of an artificial learning study designed to test whether the acoustic stability of the speech chunks mapped to symbols is a factor in subjects’ ability to learn a novel writing system.
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Latapie, Hugo, Ozkan Kilic, Gaowen Liu, Ramana Kompella, Adam Lawrence, Yuhong Sun, Jayanth Srinivasa, Yan Yan, Pei Wang, and Kristinn R. Thórisson. "A Metamodel and Framework for Artificial General Intelligence From Theory to Practice." Journal of Artificial Intelligence and Consciousness 08, no. 02 (April 22, 2021): 205–27. http://dx.doi.org/10.1142/s2705078521500119.

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This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation. While interest in hybrid machine learning/symbolic AI systems leveraging, for example, reasoning and knowledge graphs, is gaining popularity, we find there remains a need for both a clear definition of knowledge and a metamodel to guide the creation and manipulation of knowledge. Some of the benefits of the metamodel we introduce in this paper include a solution to the symbol grounding problem, cumulative learning and federated learning. We have applied the metamodel to problems ranging from time series analysis, computer vision and natural language understanding and have found that the metamodel enables a wide variety of learning mechanisms ranging from machine learning, to graph network analysis and learning by reasoning engines to interoperate in a highly synergistic way. Our metamodel-based projects have consistently exhibited unprecedented accuracy, performance, and ability to generalize. This paper is inspired by the state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular computing community, as well as Alfred Korzybski’s general semantics. One surprising consequence of the metamodel is that it not only enables a new level of autonomous learning and optimal functioning for machine intelligences, but may also shed light on a path to better understanding how to improve human cognition.
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Raue, Federico, Andreas Dengel, Thomas M. Breuel, and Marcus Liwicki. "Symbol Grounding Association in Multimodal Sequences with Missing Elements." Journal of Artificial Intelligence Research 61 (April 11, 2018): 787–806. http://dx.doi.org/10.1613/jair.5736.

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In this paper, we extend a symbolic association framework for being able to handle missing elements in multimodal sequences. The general scope of the work is the symbolic associations of object-word mappings as it happens in language development in infants. In other words, two different representations of the same abstract concepts can associate in both directions. This scenario has been long interested in Artificial Intelligence, Psychology, and Neuroscience. In this work, we extend a recent approach for multimodal sequences (visual and audio) to also cope with missing elements in one or both modalities. Our method uses two parallel Long Short-Term Memories (LSTMs) with a learning rule based on EM-algorithm. It aligns both LSTM outputs via Dynamic Time Warping (DTW). We propose to include an extra step for the combination with the max operation for exploiting the common elements between both sequences. The motivation behind is that the combination acts as a condition selector for choosing the best representation from both LSTMs. We evaluated the proposed extension in the following scenarios: missing elements in one modality (visual or audio) and missing elements in both modalities (visual and sound). The performance of our extension reaches better results than the original model and similar results to individual LSTM trained in each modality.
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Kocabas, S. "A review of learning." Knowledge Engineering Review 6, no. 3 (September 1991): 195–222. http://dx.doi.org/10.1017/s0269888900005804.

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AbstractLearning is one of the important research fields in artificial intelligence. This paper begins with an outline of the definitions of learning and intelligence, followed by a discussion of the aims of machine learning as an emerging science, and an historical outline of machine learning. The paper then examines the elements and various classifications of learning, and then introduces a new classification of learning based on the levels of representation and learning as knowledge-, symboland device-level learning. Similarity- and explanation-based generalization and conceptual clustering are described as knowledge level learning methods. Learning in classifiers, genetic algorithms and classifier systems are described as symbol level learning, and neural networks are described as device level systems. In accordance with this classification, methods of learning are described in terms of inputs, learning algorithms or devices, and outputs. Then there follows a discussion on the relationships between knowledge representation and learning, and a discussion on the limits of learning in knowledge systems. The paper concludes with a summary of the results drawn from this review.
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Dissertations / Theses on the topic "Artificial symbol learning"

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dePalma, Nicholas Brian. "Task transparency in learning by demonstration : gaze, pointing, and dialog." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34702.

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This body of work explores an emerging aspect of human-robot interaction, transparency. Socially guided machine learning has proven that highly immersive robotic behaviors have yielded better results than lesser interactive behaviors for performance and shorter training time. While other work explores this transparency in learning by demonstration using non-verbal cues to point out the importance or preference users may have towards behaviors, my work follows this argument and attempts to extend it by offering cues to the internal task representation. What I show is that task-transparency, or the ability to connect and discuss the task in a fluent way implores the user to shape and correct the learned goal in ways that may be impossible by other present day learning by demonstration methods. Additionally, some participants are shown to prefer task-transparent robots which appear to have the ability of "introspection" in which it can modify the learned goal by other methods than just demonstration.
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Drummond, Chris. "A symbol's role in learning low-level control functions." Thesis, University of Ottawa (Canada), 1999. http://hdl.handle.net/10393/8886.

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This thesis demonstrates how the power of symbolic processing can be exploited in the learning of low level control functions. It proposes a novel hybrid architecture with a tight coupling between a variant of symbolic planning and reinforcement learning. This architecture combines the strengths of the function approximation of subsymbolic learning with the more abstract compositional nature of symbolic learning. The former is able to represent mappings of world states to actions in an accurate way. The latter allows a more rapid solution to problems by exploiting structure within the domain. A control function is learnt over time through interaction with the world. Symbols are attached to features in the functions. The symbolic attachments act as anchor points used to transform the function of a previously learnt task to that of a new task. The solution of more complex tasks is achieved through composing simpler functions, using the symbolic attachments to determine the composition. The result is used as the initial control function of the new task and then modified through further learning. This is shown to produce a significant speed up over basic reinforcement learning.
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Chen, Hsinchun. "Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms." Wiley Periodicals, Inc, 1995. http://hdl.handle.net/10150/106427.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence- based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution- based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users’ information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keywordbased, probabilistic, and knowledge-based techniques.
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Chen, Hsinchun, P. Buntin, Linlin She, S. Sutjahjo, C. Sommer, and D. Neely. "Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing." IEEE, 1994. http://hdl.handle.net/10150/105472.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
For our research, we investigated a different problem-solving scenario called game playing, which is unstructured, complex, and seldom-studied. We considered several real-life game-playing scenarios and decided on greyhound racing. The large amount of historical information involved in the search poses a challenge for both human experts and machine-learning algorithms. The questions then become: Can machine-learning techniques reduce the uncertainty in a complex game-playing scenario? Can these methods outperform human experts in prediction? Our research sought to answer these questions.
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Fusting, Christopher Winter. "Temporal Feature Selection with Symbolic Regression." ScholarWorks @ UVM, 2017. http://scholarworks.uvm.edu/graddis/806.

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Building and discovering useful features when constructing machine learning models is the central task for the machine learning practitioner. Good features are useful not only in increasing the predictive power of a model but also in illuminating the underlying drivers of a target variable. In this research we propose a novel feature learning technique in which Symbolic regression is endowed with a ``Range Terminal'' that allows it to explore functions of the aggregate of variables over time. We test the Range Terminal on a synthetic data set and a real world data in which we predict seasonal greenness using satellite derived temperature and snow data over a portion of the Arctic. On the synthetic data set we find Symbolic regression with the Range Terminal outperforms standard Symbolic regression and Lasso regression. On the Arctic data set we find it outperforms standard Symbolic regression, fails to beat the Lasso regression, but finds useful features describing the interaction between Land Surface Temperature, Snow, and seasonal vegetative growth in the Arctic.
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Alquézar, Mancho René. "Symbolic and connectionist learning techniques for grammatical inference." Doctoral thesis, Universitat Politècnica de Catalunya, 1997. http://hdl.handle.net/10803/6651.

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This thesis is structured in four parts for a total of ten chapters.

The first part, introduction and review (Chapters 1 to 4), presents an extensive state-of-the-art review of both symbolic and connectionist GI methods, that serves also to state most of the basic material needed to describe later the contributions of the thesis. These contributions constitute the contents of the rest of parts (Chapters 5 to 10).

The second part, contributions on symbolic and connectionist techniques for regular grammatical inference (Chapters 5 to 7), describes the contributions related to the theory and methods for regular GI, which include other lateral subjects such as the representation oí. finite-state machines (FSMs) in recurrent neural networks (RNNs).

The third part of the thesis, augmented regular expressions and their inductive inference, comprises Chapters 8 and 9. The augmented regular expressions (or AREs) are defined and proposed as a new representation for a subclass of CSLs that does not contain all the context-free languages but a large class of languages capable of describing patterns with symmetries and other (context-sensitive) structures of interest in pattern recognition problems.

The fourth part of the thesis just includes Chapter 10: conclusions and future research. Chapter 10 summarizes the main results obtained and points out the lines of further research that should be followed both to deepen in some of the theoretical aspects raised and to facilitate the application of the developed GI tools to real-world problems in the area of computer vision.
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Galassi, Andrea. "Symbolic versus sub-symbolic approaches: a case study on training Deep Networks to play Nine Men’s Morris game." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/12859/.

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Le reti neurali artificiali, grazie alle nuove tecniche di Deep Learning, hanno completamente rivoluzionato il panorama tecnologico degli ultimi anni, dimostrandosi efficaci in svariati compiti di Intelligenza Artificiale e ambiti affini. Sarebbe quindi interessante analizzare in che modo e in quale misura le deep network possano sostituire le IA simboliche. Dopo gli impressionanti risultati ottenuti nel gioco del Go, come caso di studio è stato scelto il gioco del Mulino, un gioco da tavolo largamente diffuso e ampiamente studiato. È stato quindi creato il sistema completamente sub-simbolico Neural Nine Men’s Morris, che sfrutta tre reti neurali per scegliere la mossa migliore. Le reti sono state addestrate su un dataset di più di 1.500.000 coppie (stato del gioco, mossa migliore), creato in base alle scelte di una IA simbolica. Il sistema ha dimostrato di aver imparato le regole del gioco proponendo una mossa valida in più del 99% dei casi di test. Inoltre ha raggiunto un’accuratezza del 39% rispetto al dataset e ha sviluppato una propria strategia di gioco diversa da quella della IA addestratrice, dimostrandosi un giocatore peggiore o migliore a seconda dell’avversario. I risultati ottenuti in questo caso di studio mostrano che, in questo contesto, la chiave del successo nella progettazione di sistemi AI allo stato dell’arte sembra essere un buon bilanciamento tra tecniche simboliche e sub-simboliche, dando più rilevanza a queste ultime, con lo scopo di raggiungere la perfetta integrazione di queste tecnologie.
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Giuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.

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Informed Machine Learning is an umbrella term that comprises a set of methodologies in which domain knowledge is injected into a data-driven system in order to improve its level of accuracy, satisfy some external constraint, and in general serve the purposes of explainability and reliability. The said topid has been widely explored in the literature by means of many different techniques. Moving Targets is one such a technique particularly focused on constraint satisfaction: it is based on decomposition and bi-level optimization and proceeds by iteratively refining the target labels through a master step which is in charge of enforcing the constraints, while the training phase is delegated to a learner. In this work, we extend the algorithm in order to deal with semi-supervised learning and soft constraints. In particular, we focus our empirical evaluation on both regression and classification tasks involving monotonicity shape constraints. We demonstrate that our method is robust with respect to its hyperparameters, as well as being able to generalize very well while reducing the number of violations on the enforced constraints. Additionally, the method can even outperform, both in terms of accuracy and constraint satisfaction, other state-of-the-art techniques such as Lattice Models and Semantic-based Regularization with a Lagrangian Dual approach for automatic hyperparameter tuning.
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Milaré, Claudia Regina. ""Extração de conhecimento de redes neurais artificiais utilizando sistemas de aprendizado simbólico e algoritmos genéticos"." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-11082004-004358/.

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Em Aprendizado de Máquina - AM não existe um único algoritmo que é sempre melhor para todos os domínios de aplicação. Na prática, diversas pesquisas mostram que Redes Neurais Artificiais - RNAs têm um 'bias' indutivo apropriado para diversos domínios. Em razão disso, RNAs têm sido aplicadas na resolução de vários problemas com desempenho satisfatório. Sistemas de AM simbólico possuem um 'bias' indutivo menos flexível do que as RNAs. Enquanto que as RNAs são capazes de aprender qualquer função, sistemas de AM simbólico geralmente aprendem conceitos que podem ser descritos na forma de hiperplanos. Por outro lado, sistemas de AM simbólico representam o conceito induzido por meio de estruturas simbólicas, as quais são geralmente compreensíveis pelos seres humanos. Assim, sistemas de AM simbólico são preferíveis quando é essencial a compreensibilidade do conceito induzido. RNAs carecem da capacidade de explicar suas decisões, uma vez que o conhecimento é codificado na forma de valores de seus pesos e 'thresholds'. Essa codificação é difícil de ser interpretada por seres humanos. Em diversos domínios de aplicação, tal como aprovação de crédito e diagnóstico médico, prover uma explicação sobre a classificação dada a um determinado caso é de crucial importância. De um modo similar, diversos usuários de sistemas de AM simbólico desejam validar o conhecimento induzido, com o objetivo de assegurar que a generalização feita pelo algoritmo é correta. Para que RNAs sejam aplicadas em um maior número de domínios, diversos pesquisadores têm proposto métodos para extrair conhecimento compreensível de RNAs. As principais contribuições desta tese são dois métodos que extraem conhecimento simbólico de RNAs. Os métodos propostos possuem diversas vantagens sobre outros métodos propostos previamente, tal como ser aplicáveis a qualquer arquitetura ou algoritmo de aprendizado de RNAs supervisionadas. O primeiro método proposto utiliza sistemas de AM simbólico para extrair conhecimento de RNAs, e o segundo método proposto estende o primeiro, combinado o conhecimento induzido por diversos sistemas de AM simbólico por meio de um Algoritmo Genético - AG. Os métodos propostos são analisados experimentalmente em diversos domínios de aplicação. Ambos os métodos são capazes de extrair conhecimento simbólico com alta fidelidade em relação à RNA treinada. Os métodos propostos são comparados com o método TREPAN, apresentando resultados promissores. TREPAN é um método bastante conhecido para extrair conhecimento de RNAs.
In Machine Learning - ML there is not a single algorithm that is the best for all application domains. In practice, several research works have shown that Artificial Neural Networks - ANNs have an appropriate inductive bias for several domains. Thus, ANNs have been applied to a number of data sets with high predictive accuracy. Symbolic ML algorithms have a less flexible inductive bias than ANNs. While ANNs can learn any input-output mapping, i.e., ANNs have the universal approximation property, symbolic ML algorithms frequently learn concepts describing them using hyperplanes. On the other hand, symbolic algorithms are needed when a good understating of the decision process is essential, since symbolic ML algorithms express the knowledge induced using symbolic structures that can be interpreted and understood by humans. ANNs lack the capability of explaining their decisions since the knowledge is encoded as real-valued weights and biases of the network. This encoding is difficult to be interpreted by humans. In several application domains, such as credit approval and medical diagnosis, providing an explanation related to the classification given to a certain case is of crucial importance. In a similar way, several users of ML algorithms desire to validate the knowledge induced, in order to assure that the generalization made by the algorithm is correct. In order to apply ANNs to a larger number of application domains, several researches have proposed methods to extract comprehensible knowledge from ANNs. The primary contribution of this thesis consists of two methods that extract symbolic knowledge, expressed as decision rules, from ANNs. The proposed methods have several advantages over previous methods, such as being applicable to any architecture and supervised learning algorithm of ANNs. The first method uses standard symbolic ML algorithm to extract knowledge from ANNs, and the second method extends the first method by combining the knowledge induced by several symbolic ML algorithms through the application of a Genetic Algorithm - GA. The proposed methods are experimentally analyzed in a number of application domains. Results show that both methods are capable to extract symbolic knowledge having high fidelity with trained ANNs. The proposed methods are compared with TREPAN, showing promising results. TREPAN is a well known method to extract knowledge from ANNs.
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Misino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.

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Many different extensions of the VAE framework have been introduced in the past. How­ ever, the vast majority of them focused on pure sub­-symbolic approaches that are not sufficient for solving generative tasks that require a form of reasoning. In this thesis, we propose the probabilistic logic VAE (PLVAE), a neuro-­symbolic deep generative model that combines the representational power of VAEs with the reasoning ability of probabilistic ­logic programming. The strength of PLVAE resides in its probabilistic ­logic prior, which provides an interpretable structure to the latent space that can be easily changed in order to apply the model to different scenarios. We provide empirical results of our approach by training PLVAE on a base task and then using the same model to generalize to novel tasks that involve reasoning with the same set of symbols.
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Books on the topic "Artificial symbol learning"

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Apolloni, Bruno. From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data. Boston, MA: Springer US, 2002.

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Conference on Data Analysis, Learning Symbolic and Numeric Knowledge (1989 Antibes, France). Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.

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E, Diday, and Institut national de recherche en informatique et en automatique (France), eds. Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.

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Gabbay, Dov M. Abductive Reasoning and Learning. Dordrecht: Springer Netherlands, 2000.

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Pascal, Hitzler, and SpringerLink (Online service), eds. Perspectives of Neural-Symbolic Integration. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2007.

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International School on Neural Nets "E.R. Caianiello" Fifth Course: From Synapses to Rules: Discovering Symbolic Rules From Neural Processed Data (2002 Erice, Italy). From synapses to rules: Discovering symbolic rules from neural processed data. New York: Kluwer Academic/Plenum Pub., 2002.

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Laurent, Miclet, De la Higuera Colin, and International Colloquium on Grammatical Inference (3rd : 1996 : Montpellier, France), eds. Grammatical inference: Learning syntax from sentences : Third International Colloquium, ICGI-96, Montpellier, France, September 25-27, 1996 : proceedings. Berlin: Springer, 1996.

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Pieter, Adriaans, Fernau Henning 1965-, and Zaanen Menno van 1972-, eds. Grammatical inference: Algorithms and applications : 6th international colloquium, ICGI 2002, Amsterdam, The Netherlands, September 23-25, 2002 : proceedings. New York: Springer, 2002.

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Proudfoot, Diane, and B. Jack Copeland. Artificial Intelligence. Edited by Eric Margolis, Richard Samuels, and Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0007.

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In this article the central philosophical issues concerning human-level artificial intelligence (AI) are presented. AI largely changed direction in the 1980s and 1990s, concentrating on building domain-specific systems and on sub-goals such as self-organization, self-repair, and reliability. Computer scientists aimed to construct intelligence amplifiers for human beings, rather than imitation humans. Turing based his test on a computer-imitates-human game, describing three versions of this game in 1948, 1950, and 1952. The famous version appears in a 1950 article inMind, ‘Computing Machinery and Intelligence’ (Turing 1950). The interpretation of Turing's test is that it provides an operational definition of intelligence (or thinking) in machines, in terms of behavior. ‘Intelligent Machinery’ sets out the thesis that whether an entity is intelligent is determined in part by our responses to the entity's behavior. Wittgenstein frequently employed the idea of a human being acting like a reliable machine. A ‘living reading-machine’ is a human being or other creature that is given written signs, for example Chinese characters, arithmetical symbols, logical symbols, or musical notation, and who produces text spoken aloud, solutions to arithmetical problems, and proofs of logical theorems. Wittgenstein mentions that an entity that manipulates symbols genuinely reads only if he or she has a particular history, involving learning and training, and participates in a social environment that includes normative constraints and further uses of the symbols.
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Neural-Symbolic Learning Systems. Springer, 2002.

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Book chapters on the topic "Artificial symbol learning"

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Jiang, Yaqing, Petros Papapanagiotou, and Jacques Fleuriot. "Machine Learning for Inductive Theorem Proving." In Artificial Intelligence and Symbolic Computation, 87–103. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99957-9_6.

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Eppler, Wolfgang. "Symbolic Learning in Connectionist Production Systems." In Artificial Neural Nets and Genetic Algorithms, 257–64. Vienna: Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-7533-0_38.

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Roque, Waldir L. "Learning qualitative physics reasoning from regime analysis." In Artificial Intelligence and Symbolic Mathematical Computing, 277–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57322-4_20.

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Grumbach, Alain. "Learning at subsymbolic and symbolic levels." In Neural Networks: Artificial Intelligence and Industrial Applications, 91–94. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3087-1_18.

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Strannegård, Claes, Abdul Rahim Nizamani, and Ulf Persson. "A General System for Learning and Reasoning in Symbolic Domains." In Artificial General Intelligence, 174–85. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09274-4_17.

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Esposito, F., D. Malerba, and G. Semeraro. "Incorporating statistical techniques into empirical symbolic learning systems." In Artificial Intelligence Frontiers in Statistics, 168–81. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4537-2_14.

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Kolonin, Anton. "Neuro-Symbolic Architecture for Experiential Learning in Discrete and Functional Environments." In Artificial General Intelligence, 106–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93758-4_12.

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Ayeb, Kawther Khazri, Yosra Meguebli, and Afef Kacem Echi. "Deep Learning Architecture for Off-Line Recognition of Handwritten Math Symbols." In Pattern Recognition and Artificial Intelligence, 200–214. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71804-6_15.

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Raue, Federico, Sebastian Palacio, Thomas M. Breuel, Wonmin Byeon, Andreas Dengel, and Marcus Liwicki. "Symbolic Association Using Parallel Multilayer Perceptron." In Artificial Neural Networks and Machine Learning – ICANN 2016, 347–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44781-0_41.

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Dillmann, R., and H. Friedrich. "Programming by demonstration: A machine learning approach to support skill acquision for robots." In Artificial Intelligence and Symbolic Mathematical Computation, 87–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61732-9_52.

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Conference papers on the topic "Artificial symbol learning"

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Liang, Xiaoyuan, Martin Renqiang Min, Hongyu Guo, and Guiling Wang. "Learning K-way D-dimensional Discrete Embedding for Hierarchical Data Visualization and Retrieval." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/411.

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Traditional embedding approaches associate a real-valued embedding vector with each symbol or data point, which is equivalent to applying a linear transformation to ``one-hot" encoding of discrete symbols or data objects. Despite simplicity, these methods generate storage-inefficient representations and fail to effectively encode the internal semantic structure of data, especially when the number of symbols or data points and the dimensionality of the real-valued embedding vectors are large. In this paper, we propose a regularized autoencoder framework to learn compact Hierarchical K-way D-dimensional (HKD) discrete embedding of symbols or data points, aiming at capturing essential semantic structures of data. Experimental results on synthetic and real-world datasets show that our proposed HKD embedding can effectively reveal the semantic structure of data via hierarchical data visualization and greatly reduce the search space of nearest neighbor retrieval while preserving high accuracy.
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Alomari, Muhannad, Paul Duckworth, Nils Bore, Majd Hawasly, David C. Hogg, and Anthony G. Cohn. "Grounding of Human Environments and Activities for Autonomous Robots." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/193.

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With the recent proliferation of human-oriented robotic applications in domestic and industrial scenarios, it is vital for robots to continually learn about their environments and about the humans they share their environments with. In this paper, we present a novel, online, incremental framework for unsupervised symbol grounding in real-world, human environments for autonomous robots. We demonstrate the flexibility of the framework by learning about colours, people names, usable objects and simple human activities, integrating state-of-the-art object segmentation, pose estimation, activity analysis along with a number of sensory input encodings into a continual learning framework. Natural language is grounded to the learned concepts, enabling the robot to communicate in a human-understandable way. We show, using a challenging real-world dataset of human activities as perceived by a mobile robot, that our framework is able to extract useful concepts, ground natural language descriptions to them, and, as a proof-of-concept, generate simple sentences from templates to describe people and the activities they are engaged in.
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Gurina, Ekaterina, Ksenia Antipova, Nikita Klyuchnikov, and Dmitry Koroteev. "Machine Learning Microservice for Identification of Accident Predecessors." In SPE Middle East Oil & Gas Show and Conference. SPE, 2021. http://dx.doi.org/10.2118/204707-ms.

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Abstract Drilling accidents prediction is the important task in well construction. Drilling support software allows observing the drilling parameters for multiple wells at the same time and artificial intelligence helps detecting the drilling accident predecessor ahead the emergency situation. We present machine learning (ML) algorithm for prediction of such accidents as stuck, mud loss, fluid show, washout, break of drill string and shale collar. The model for forecasting the drilling accidents is based on the "Bag-of-features" approach, which implies the use of distributions of the directly recorded data as the main features. Bag-of-features implies the labeling of small parts of data by the particular symbol, named codeword. Building histograms of symbols for the data segment, one could use the histogram as an input for the machine learning algorithm. Fragments of real-time mud log data were used to create the model. We define more than 1000 drilling accident predecessors for more than 60 real accidents and about 2500 normal drilling cases as a training set for ML model. The developed model analyzes real-time mud log data and calculates the probability of accident. The result is presented as a probability curve for each type of accident; if the critical probability value is exceeded, the user is notified of the risk of an accident. The Bag-of-features model shows high performance by validation both on historical data and in real time. The prediction quality does not vary field to field and could be used in different fields without additional training of the ML model. The software utilizing the ML model has microservice architecture and is integrated with the WITSML data server. It is capable of real-time accidents forecasting without human intervention. As a result, the system notifies the user in all cases when the situation in the well becomes similar to the pre-accident one, and the engineer has enough time to take the necessary actions to prevent an accident.
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Dang, Long Hoang, Thao Minh Le, Vuong Le, and Truyen Tran. "Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/88.

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Video Question Answering (Video QA) is a powerful testbed to develop new AI capabilities. This task necessitates learning to reason about objects, relations, and events across visual and linguistic domains in space-time. High-level reasoning demands lifting from associative visual pattern recognition to symbol like manipulation over objects, their behavior and interactions. Toward reaching this goal we propose an object-oriented reasoning approach in that video is abstracted as a dynamic stream of interacting objects. At each stage of the video event flow, these objects interact with each other, and their interactions are reasoned about with respect to the query and under the overall context of a video. This mechanism is materialized into a family of general-purpose neural units and their multi-level architecture called Hierarchical Object-oriented Spatio-Temporal Reasoning (HOSTR) networks. This neural model maintains the objects' consistent lifelines in the form of a hierarchically nested spatio-temporal graph. Within this graph, the dynamic interactive object-oriented representations are built up along the video sequence, hierarchically abstracted in a bottom-up manner, and converge toward the key information for the correct answer. The method is evaluated on multiple major Video QA datasets and establishes new state-of-the-arts in these tasks. Analysis into the model's behavior indicates that object-oriented reasoning is a reliable, interpretable and efficient approach to Video QA.
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Dumancic, Sebastijan, Alberto Garcia-Duran, and Mathias Niepert. "A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/843.

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Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally been addressed by symbolic approaches such as Statistical relational learning, recent methods in (deep) representation learning have shown promising results for specialised tasks such as knowledge base completion. These approaches, also known as distributional, abandon the traditional symbolic paradigm by replacing symbols with vectors in Euclidean space. With few exceptions, symbolic and distributional approaches are explored in different communities and little is known about their respective strengths and weaknesses. In this work, we compare distributional and symbolic relational learning approaches on various standard relational classification and knowledge base completion tasks. Furthermore, we analyse the properties of the datasets and relate them to the performance of the methods in the comparison. The results reveal possible indicators that could help in choosing one approach over the other for particular knowledge graphs.
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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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Raczaszek-Leonardi, Joanna, and Terrence W. Deacon. "Ungrounding symbols in language development: implications for modeling emergent symbolic communication in artificial systems." In 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE, 2018. http://dx.doi.org/10.1109/devlrn.2018.8761016.

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Yang, Fangkai, Daoming Lyu, Bo Liu, and Steven Gustafson. "PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making." 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.2018/675.

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Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which may not be robust to domain uncertainties and changes. In this paper we present a unified framework PEORL that integrates symbolic planning with hierarchical reinforcement learning (HRL) to cope with decision-making in dynamic environment with uncertainties. Symbolic plans are used to guide the agent's task execution and learning, and the learned experience is fed back to symbolic knowledge to improve planning. This method leads to rapid policy search and robust symbolic plans in complex domains. The framework is tested on benchmark domains of HRL.
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Dai, Wang-Zhou, and Stephen Muggleton. "Abductive Knowledge Induction from Raw Data." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/254.

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For many reasoning-heavy tasks with raw inputs, it is challenging to design an appropriate end-to-end pipeline to formulate the problem-solving process. Some modern AI systems, e.g., Neuro-Symbolic Learning, divide the pipeline into sub-symbolic perception and symbolic reasoning, trying to utilise data-driven machine learning and knowledge-driven problem-solving simultaneously. However, these systems suffer from the exponential computational complexity caused by the interface between the two components, where the sub-symbolic learning model lacks direct supervision, and the symbolic model lacks accurate input facts. Hence, they usually focus on learning the sub-symbolic model with a complete symbolic knowledge base while avoiding a crucial problem: where does the knowledge come from? In this paper, we present Abductive Meta-Interpretive Learning (MetaAbd) that unites abduction and induction to learn neural networks and logic theories jointly from raw data. Experimental results demonstrate that MetaAbd not only outperforms the compared systems in predictive accuracy and data efficiency but also induces logic programs that can be re-used as background knowledge in subsequent learning tasks. To the best of our knowledge, MetaAbd is the first system that can jointly learn neural networks from scratch and induce recursive first-order logic theories with predicate invention.
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Raedt, Luc de, Sebastijan Dumančić, Robin Manhaeve, and Giuseppe Marra. "From Statistical Relational to Neuro-Symbolic Artificial Intelligence." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/688.

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Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning. This survey identifies several parallels across seven different dimensions between these two fields. These cannot only be used to characterize and position neuro-symbolic artificial intelligence approaches but also to identify a number of directions for further research.
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