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Journal articles on the topic 'Neuro-symbolic'

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Published: 10 December 2022

Last updated: 28 January 2023

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1

Burattini, Ernesto, Antonio de Francesco, and Massimo de Gregorio. "NSL: A Neuro–Symbolic Language for a Neuro–Symbolic Processor (NSP)." International Journal of Neural Systems 13, no. 02 (April 2003): 93–101. http://dx.doi.org/10.1142/s0129065703001480.

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A Neuro–Symbolic Language for monotonic and non–monotonic parallel logical inference by means of artificial neural networks (ANNs) is presented. Both the language and its compiler have been designed and implemented in order to translate the neural representation of a given problem into a VHDL software, which in turn can set devices such as FPGA. The result of this operation leads to an electronic circuit that we call NSP (Neuro–Symbolic Processor).

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Arabshahi, Forough, Jennifer Lee, Mikayla Gawarecki, Kathryn Mazaitis, Amos Azaria, and Tom Mitchell. "Conversational Neuro-Symbolic Commonsense Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 4902–11. http://dx.doi.org/10.1609/aaai.v35i6.16623.

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In order for conversational AI systems to hold more natural and broad-ranging conversations, they will require much more commonsense, including the ability to identify unstated presumptions of their conversational partners. For example, in the command "If it snows at night then wake me up early because I don't want to be late for work" the speaker relies on commonsense reasoning of the listener to infer the implicit presumption that they wish to be woken only if it snows enough to cause traffic slowdowns. We consider here the problem of understanding such imprecisely stated natural language commands given in the form of if-(state), then-(action), because-(goal) statements. More precisely, we consider the problem of identifying the unstated presumptions of the speaker that allow the requested action to achieve the desired goal from the given state (perhaps elaborated by making the implicit presumptions explicit). We release a benchmark data set for this task, collected from humans and annotated with commonsense presumptions. We present a neuro-symbolic theorem prover that extracts multi-hop reasoning chains, and apply it to this problem. Furthermore, to accommodate the reality that current AI commonsense systems lack full coverage, we also present an interactive conversational framework built on our neuro-symbolic system, that conversationally evokes commonsense knowledge from humans to complete its reasoning chains.

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Sathasivam, Saratha. "Acceleration technique for neuro symbolic integration." Applied Mathematical Sciences 9 (2015): 409–17. http://dx.doi.org/10.12988/ams.2015.48670.

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4

Rosemarie Velik. "The Neuro-Symbolic Code of Perception." Journal of Cognitive Science 11, no. 2 (December 2010): 161–80. http://dx.doi.org/10.17791/jcs.2010.11.2.161.

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5

Barbosa, Raul, Douglas O. Cardoso, Diego Carvalho, and Felipe M. G. França. "Weightless neuro-symbolic GPS trajectory classification." Neurocomputing 298 (July 2018): 100–108. http://dx.doi.org/10.1016/j.neucom.2017.11.075.

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Prentzas, Jim, and Ioannis Hatzilygeroudis. "Neurules and connectionist expert systems: Unexplored neuro-symbolic reasoning aspects." Intelligent Decision Technologies 15, no. 4 (January 10, 2022): 761–77. http://dx.doi.org/10.3233/idt-210211.

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

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Morel, Gilles. "Neuro-symbolic A.I. for the smart city." Journal of Physics: Conference Series 2042, no. 1 (November 1, 2021): 012018. http://dx.doi.org/10.1088/1742-6596/2042/1/012018.

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Abstract Smart building and smart city specialists agree that complex, innovative use cases, especially those using cross-domain and multi-source data, need to make use of Artificial Intelligence (AI). However, today’s AI mainly concerns machine learning and artificial neural networks (deep learning), whereas the first forty years of the discipline (the last decades of the 20th century) were essentially focused on a knowledge-based approach, which is still relevant today for some tasks. In this article we advocate a merging of these two AI trends – an approach known as neuro-symbolic AI – for the smart city, and point the way towards a complete integration of the two technologies, compatible with standard software.

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Siyaev, Aziz, and Geun-Sik Jo. "Neuro-Symbolic Speech Understanding in Aircraft Maintenance Metaverse." IEEE Access 9 (2021): 154484–99. http://dx.doi.org/10.1109/access.2021.3128616.

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9

Sathasivam, Saratha. "Applying Different Learning Rules in Neuro-Symbolic Integration." Advanced Materials Research 433-440 (January 2012): 716–20. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.716.

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Pseudo inverse learning rule and new activation unction performance will be evaluated and compared with the primitive learning rule, Hebb rule. Comparisons are made between these three rules to see which rule is better or outperformed other rules in the aspects of computation time, memory and complexity. From the computer simulation that has been carried out, the new activation function performs better than the other two learning methods.

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10

Nauck, D. D. "Neuro-fuzzy learning with symbolic and numeric data." Soft Computing - A Fusion of Foundations, Methodologies and Applications -1, no. 1 (July 14, 2003): 1. http://dx.doi.org/10.1007/s00500-003-0294-7.

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Nauck, D. D. "Neuro-fuzzy learning with symbolic and numeric data." Soft Computing - A Fusion of Foundations, Methodologies and Applications 8, no. 6 (May 1, 2004): 383–96. http://dx.doi.org/10.1007/s00500-003-0294-y.

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12

Alshahrani, Mona, Mohammad Asif Khan, Omar Maddouri, Akira R. Kinjo, Núria Queralt-Rosinach, and Robert Hoehndorf. "Neuro-symbolic representation learning on biological knowledge graphs." Bioinformatics 33, no. 17 (April 25, 2017): 2723–30. http://dx.doi.org/10.1093/bioinformatics/btx275.

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13

Kishor, Rabinandan. "Neuro-Symbolic AI: Bringing a new era of Machine Learning." International Journal of Research Publication and Reviews 03, no. 12 (2022): 2326–36. http://dx.doi.org/10.55248/gengpi.2022.31271.

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Processing Natural Language using machines is not a new concept. Back in 1940 researchers estimated the importance of a machine that could translate one language to another. Further, during 1957-1970 researchers split into two divisions concerning NLP: symbolic and stochastic. This paper presents an extensive review of recent breakthroughs in Neuro Symbolic Artificial Intelligence (NSAI), an area of AI research which seeks to combine traditional rules-based AI approaches with modern deep learning techniques. Neuro Symbolic models have already demonstrated the capability to outperform state-of-the-art deep learning models in domains such as image and video reasoning. Such models not only performed better when trained on a fraction of dataset compared with traditional machine learning models, but also solved an underlined issue called generalization of deep neural network systems. We also find that symbolic models are good in visual question answering (VQA). In this paper, we also review research results related to Neuro Symbolic AI with the objective of exploring the importance of such AI systems and how it would shape the future of AI as a whole. We discuss different types of dataset of Visual Question Answering (VQA) tasks based on NSAI and extensive comparison of performance of different NSAI models. Later, the article focuses on the contemporary real time application of NSAI systems and how NSAI is shaping the world’s different sectors including finance, healthcare, and cyber security.

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van Bekkum, Michael, Maaike de Boer, Frank van Harmelen, André Meyer-Vitali, and Annette ten Teije. "Modular design patterns for hybrid learning and reasoning systems." Applied Intelligence 51, no. 9 (June 18, 2021): 6528–46. http://dx.doi.org/10.1007/s10489-021-02394-3.

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AbstractThe unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognized as one of the key challenges of modern AI. Recent years have seen a large number of publications on such hybrid neuro-symbolic AI systems. That rapidly growing literature is highly diverse, mostly empirical, and is lacking a unifying view of the large variety of these hybrid systems. In this paper, we analyze a large body of recent literature and we propose a set of modular design patterns for such hybrid, neuro-symbolic systems. We are able to describe the architecture of a very large number of hybrid systems by composing only a small set of elementary patterns as building blocks. The main contributions of this paper are: 1) a taxonomically organised vocabulary to describe both processes and data structures used in hybrid systems; 2) a set of 15+ design patterns for hybrid AI systems organized in a set of elementary patterns and a set of compositional patterns; 3) an application of these design patterns in two realistic use-cases for hybrid AI systems. Our patterns reveal similarities between systems that were not recognized until now. Finally, our design patterns extend and refine Kautz’s earlier attempt at categorizing neuro-symbolic architectures.

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Ebrahimi, Monireh, Aaron Eberhart, Federico Bianchi, and Pascal Hitzler. "Towards bridging the neuro-symbolic gap: deep deductive reasoners." Applied Intelligence 51, no. 9 (February 6, 2021): 6326–48. http://dx.doi.org/10.1007/s10489-020-02165-6.

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Hatzilygeroudis, Ioannis, and Jim Prentzas. "Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems." International Journal of Hybrid Intelligent Systems 1, no. 3-4 (January 19, 2005): 111–26. http://dx.doi.org/10.3233/his-2004-13-401.

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17

Feblowitz, Mark, Oktie Hassanzadeh, Michael Katz, Shirin Sohrabi, Kavitha Srinivas, and Octavian Udrea. "IBM Scenario Planning Advisor: A Neuro-Symbolic ERM Solution." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 16032–34. http://dx.doi.org/10.1609/aaai.v35i18.18003.

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Scenario Planning is a commonly used Enterprise Risk Management (ERM) technique to help decision makers with longterm plans by considering multiple alternative futures. It is typically a manual, highly labor intensive process involving dozens of experts and hundreds to thousands of person-hours. We previously introduced a Scenario Planning Advisor prototype (Sohrabi et al. 2018a,b) that focuses on generating scenarios quickly based on expert-developed models. We present the evolution of that prototype into a full-scale, cloud deployed ERM solution that: (i) can automatically (through NLP) create models from authoritative documents such as books, reports and articles, such that what typically took hundreds to thousands of person-hours can now be achieved in minutes to hours; (ii) can gather news and other feeds relevant to forces in the risk models and group them into storylines without any other user input; (iii) can generate scenarios at scale, starting with dozens of forces of interest from models with thousands of forces in seconds; (iv) provides interactive visualizations of scenario and force model graphs, including a full model editor in the browser. The SPA solution is deployed under a non-commercial use license at https://spa-service.draco.res.ibm.com and includes a user guide to help new users get started. A video demonstration is available at https://www.youtube.com/watch?v=IaX3d37NUl8.

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Singh, Gunjan, Sutapa Mondal, Sumit Bhatia, and Raghava Mutharaju. "Neuro-Symbolic Techniques for Description Logic Reasoning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15891–92. http://dx.doi.org/10.1609/aaai.v35i18.17942.

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

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19

Sen, Prithviraj, Breno W. S. R. de Carvalho, Ryan Riegel, and Alexander Gray. "Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8212–19. http://dx.doi.org/10.1609/aaai.v36i8.20795.

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Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data. While some proposals approximate logical operators with differentiable operators from fuzzy or real-valued logic that are parameter-free thus diminishing their capacity to fit the data, other approaches are only loosely based on logic making it difficult to interpret the learned ``rules". In this paper, we propose learning rules with the recently proposed logical neural networks (LNN). Compared to others, LNNs offer a strong connection to classical Boolean logic thus allowing for precise interpretation of learned rules while harboring parameters that can be trained with gradient-based optimization to effectively fit the data. We extend LNNs to induce rules in first-order logic. Our experiments on standard benchmarking tasks confirm that LNN rules are highly interpretable and can achieve comparable or higher accuracy due to their flexible parameterization.

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20

Shoureshi, Rahmat A., Tracy Schantz, and Sun W. Lim. "Bio-inspired neuro-symbolic approach to diagnostics of structures." Smart Structures and Systems 7, no. 3 (March 25, 2011): 229–40. http://dx.doi.org/10.12989/sss.2011.7.3.229.

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21

Oh, Dongsuk, Jungwoo Lim, and Heuiseok Lim. "Neuro-Symbolic Word Embedding Using Textual and Knowledge Graph Information." Applied Sciences 12, no. 19 (September 20, 2022): 9424. http://dx.doi.org/10.3390/app12199424.

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The construction of high-quality word embeddings is essential in natural language processing. In existing approaches using a large text corpus, the word embeddings learn only sequential patterns in the context; thus, accurate learning of the syntax and semantic relationships between words is limited. Several methods have been proposed for constructing word embeddings using syntactic information. However, these methods are not trained for the semantic relationships between words in sentences or external knowledge. In this paper, we present a method for improved word embeddings using symbolic graphs for external knowledge and the relationships of the syntax and semantic role between words in sentences. The proposed model sequentially learns two symbolic graphs with different properties through a graph convolutional network (GCN) model. A new symbolic graph representation is generated to understand sentences grammatically and semantically. This graph representation includes comprehensive information that combines dependency parsing and semantic role labeling. Subsequently, word embeddings are constructed through the GCN model. The same GCN model initializes the word representations that are created in the first step and trains the relationships of ConceptNet using the relationships between words. The proposed word embeddings outperform the baselines in benchmarks and extrinsic tasks.

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22

Kang, Tian, Ali Turfah, Jaehyun Kim, Adler Perotte, and Chunhua Weng. "A neuro-symbolic method for understanding free-text medical evidence." Journal of the American Medical Informatics Association 28, no. 8 (May 6, 2021): 1703–11. http://dx.doi.org/10.1093/jamia/ocab077.

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Abstract Objective We introduce Medical evidence Dependency (MD)–informed attention, a novel neuro-symbolic model for understanding free-text clinical trial publications with generalizability and interpretability. Materials and Methods We trained one head in the multi-head self-attention model to attend to the Medical evidence Ddependency (MD) and to pass linguistic and domain knowledge on to later layers (MD informed). This MD-informed attention model was integrated into BioBERT and tested on 2 public machine reading comprehension benchmarks for clinical trial publications: Evidence Inference 2.0 and PubMedQA. We also curated a small set of recently published articles reporting randomized controlled trials on COVID-19 (coronavirus disease 2019) following the Evidence Inference 2.0 guidelines to evaluate the model’s robustness to unseen data. Results The integration of MD-informed attention head improves BioBERT substantially in both benchmark tasks—as large as an increase of +30% in the F1 score—and achieves the new state-of-the-art performance on the Evidence Inference 2.0. It achieves 84% and 82% in overall accuracy and F1 score, respectively, on the unseen COVID-19 data. Conclusions MD-informed attention empowers neural reading comprehension models with interpretability and generalizability via reusable domain knowledge. Its compositionality can benefit any transformer-based architecture for machine reading comprehension of free-text medical evidence.

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23

Gaur, Manas, Kalpa Gunaratna, Shreyansh Bhatt, and Amit Sheth. "Knowledge-Infused Learning: A Sweet Spot in Neuro-Symbolic AI." IEEE Internet Computing 26, no. 4 (July 1, 2022): 5–11. http://dx.doi.org/10.1109/mic.2022.3179759.

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Sathasivam, Saratha, and Muraly Velavan. "AGENT BASED MODELLING FOR NEW TECHNIQUE IN NEURO SYMBOLIC INTEGRATION." MATTER: International Journal of Science and Technology 3, no. 2 (November 7, 2017): 445–54. http://dx.doi.org/10.20319/mijst.2017.32.445454.

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25

Franklin, Nicholas T., Kenneth A. Norman, Charan Ranganath, Jeffrey M. Zacks, and Samuel J. Gershman. "Structured Event Memory: A neuro-symbolic model of event cognition." Psychological Review 127, no. 3 (April 2020): 327–61. http://dx.doi.org/10.1037/rev0000177.

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26

Corchado, E., and M. Wozniak. "Editorial: Neuro-symbolic Algorithms and Models for Bio-inspired Systems." Logic Journal of IGPL 19, no. 2 (July 8, 2010): 289–92. http://dx.doi.org/10.1093/jigpal/jzq026.

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Souici-Meslati, Labiba, and Mokhtar Sellami. "A Hybrid Neuro-Symbolic Approach for Arabic Handwritten Word Recognition." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 1 (January 20, 2006): 17–25. http://dx.doi.org/10.20965/jaciii.2006.p0017.

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In this article, we suggest a system that automatically constructs knowledge based artificial neural networks (KBANN) for the holistic recognition of handwritten Arabic words in limited lexicons. To build a neuro-symbolic KBANN classifier for a given vocabulary, ideal samples of its words are first submitted to a structural feature extraction module. The analysis of the presence and possible occurrence numbers for these features in the considered lexicon enables to generate a symbolic knowledge base reflecting a hierarchical classification of the words. A rules-to-network translation algorithm uses this knowledge to build a multilayer neural network. It determines precisely its architecture and initializes its connections with specific values rather than random values, as is the case in classical neural networks. This construction approach provides the network with theoretical knowledge and reduces the training stage, which remains necessary because of styles and writing conditions variability. After this empirical training stage using real examples, the network acquires a final topology, which allows it to recognize new handwritten words. The proposed method has been tested on the automated construction of neuro-symbolic classifiers for two Arabic lexicons: literal amounts and city names. The application of this approach to the recognition of handwritten words or characters in different scripts and languages is also considered.

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Biswas, Saroj, Monali Bordoloi, and Biswajit Purkayastha. "Review on Feature Selection and Classification using Neuro-Fuzzy Approaches." International Journal of Applied Evolutionary Computation 7, no. 4 (October 2016): 28–44. http://dx.doi.org/10.4018/ijaec.2016100102.

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This research article attempts to provide a recent survey on neuro-fuzzy approaches for feature selection and classification. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy and curtailing irrelevant or redundant data. The neuro-fuzzy approach is used for feature selection and for providing some insight to the user about the symbolic knowledge embedded within the network. The neuro–fuzzy approach combines the merits of neural network and fuzzy logic to solve many complex machine learning problems. The objective of this article is to provide a generic introduction and a recent survey to neuro-fuzzy approaches for feature selection and classification in a wide area of machine learning problems. Some of the existing neuro-fuzzy models are also applied on standard datasets to demonstrate the applicability of neuro-fuzzy approaches.

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Feng, Yufei, Xiaoyu Yang, Xiaodan Zhu, and Michael Greenspan. "Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference." Transactions of the Association for Computational Linguistics 10 (2022): 240–56. http://dx.doi.org/10.1162/tacl_a_00458.

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Abstract We introduce a neuro-symbolic natural logic framework based on reinforcement learning with introspective revision. The model samples and rewards specific reasoning paths through policy gradient, in which the introspective revision algorithm modifies intermediate symbolic reasoning steps to discover reward-earning operations as well as leverages external knowledge to alleviate spurious reasoning and training inefficiency. The framework is supported by properly designed local relation models to avoid input entangling, which helps ensure the interpretability of the proof paths. The proposed model has built-in interpretability and shows superior capability in monotonicity inference, systematic generalization, and interpretability, compared with previous models on the existing datasets.

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Škrlj, Blaž, Matej Martinc, Nada Lavrač, and Senja Pollak. "autoBOT: evolving neuro-symbolic representations for explainable low resource text classification." Machine Learning 110, no. 5 (April 14, 2021): 989–1028. http://dx.doi.org/10.1007/s10994-021-05968-x.

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AbstractLearning from texts has been widely adopted throughout industry and science. While state-of-the-art neural language models have shown very promising results for text classification, they are expensive to (pre-)train, require large amounts of data and tuning of hundreds of millions or more parameters. This paper explores how automatically evolved text representations can serve as a basis for explainable, low-resource branch of models with competitive performance that are subject to automated hyperparameter tuning. We present autoBOT (automatic Bags-Of-Tokens), an autoML approach suitable for low resource learning scenarios, where both the hardware and the amount of data required for training are limited. The proposed approach consists of an evolutionary algorithm that jointly optimizes various sparse representations of a given text (including word, subword, POS tag, keyword-based, knowledge graph-based and relational features) and two types of document embeddings (non-sparse representations). The key idea of autoBOT is that, instead of evolving at the learner level, evolution is conducted at the representation level. The proposed method offers competitive classification performance on fourteen real-world classification tasks when compared against a competitive autoML approach that evolves ensemble models, as well as state-of-the-art neural language models such as BERT and RoBERTa. Moreover, the approach is explainable, as the importance of the parts of the input space is part of the final solution yielded by the proposed optimization procedure, offering potential for meta-transfer learning.

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Saha, Amrita, Shafiq Joty, and Steven C. H. Hoi. "Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning over Text." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11238–47. http://dx.doi.org/10.1609/aaai.v36i10.21374.

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Neural Module Networks (NMNs) have been quite successful in incorporating explicit reasoning as learnable modules in various question answering tasks, including the most generic form of numerical reasoning over text in Machine Reading Comprehension (MRC). However to achieve this, contemporary Neural Module Networks models obtain strong supervision in form of specialized program annotation from the QA pairs through various heuristic parsing and exhaustive computation of all possible discrete operations on discrete arguments. Consequently they fail to generalize to more open-ended settings without such supervision. Hence, we propose Weakly Supervised Neuro-Symbolic Module Network (WNSMN) trained with answers as the sole supervision for numerical reasoning based MRC. WNSMN learns to execute a noisy heuristic program obtained from the dependency parse of the query, as discrete actions over both neural and symbolic reasoning modules and trains it end-to-end in a reinforcement learning framework with discrete reward from answer matching. On the subset of DROP having numerical answers, WNSMN outperforms NMN by 32% and the reasoning-free generative language model GenBERT by 8% in exact match accuracy under comparable weakly supervised settings. This showcases the effectiveness of modular networks that can handle explicit discrete reasoning over noisy programs in an end-to-end manner.

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Skvorc, Tadej, Nada Lavrac, and Marko Robnik-Sikonja. "NeSyChair: Automatic Conference Scheduling Combining Neuro-Symbolic Representations and Constrained Clustering." IEEE Access 10 (2022): 10880–97. http://dx.doi.org/10.1109/access.2022.3144932.

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Tarau, Paul. "Natlog: a Lightweight Logic Programming Language with a Neuro-symbolic Touch." Electronic Proceedings in Theoretical Computer Science 345 (September 15, 2021): 141–54. http://dx.doi.org/10.4204/eptcs.345.27.

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Papadimitriou, Stergios, and Constantinos Terzidis. "Symbolic adaptive neuro-fuzzy inference for data mining of heterogenous data." Intelligent Data Analysis 7, no. 4 (August 27, 2003): 327–46. http://dx.doi.org/10.3233/ida-2003-7405.

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Lee, JuSang, and ChoelYoung Ock. "A Neuro Symbolic Ensemble Language Representation Using Syntactic and Semantic Information." Journal of KIISE 49, no. 12 (December 31, 2022): 1124–31. http://dx.doi.org/10.5626/jok.2022.49.12.1124.

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Moon, Jiyoun. "Plugin Framework-Based Neuro-Symbolic Grounded Task Planning for Multi-Agent System." Sensors 21, no. 23 (November 26, 2021): 7896. http://dx.doi.org/10.3390/s21237896.

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As the roles of robots continue to expand in general, there is an increasing demand for research on automated task planning for a multi-agent system that can independently execute tasks in a wide and dynamic environment. This study introduces a plugin framework in which multiple robots can be involved in task planning in a broad range of areas by combining symbolic and connectionist approaches. The symbolic approach for understanding and learning human knowledge is useful for task planning in a wide and static environment. The network-based connectionist approach has the advantage of being able to respond to an ever-changing dynamic environment. A planning domain definition language-based planning algorithm, which is a symbolic approach, and the cooperative–competitive reinforcement learning algorithm, which is a connectionist approach, were utilized in this study. The proposed architecture is verified through a simulation. It is also verified through an experiment using 10 unmanned surface vehicles that the given tasks were successfully executed in a wide and dynamic environment.

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Craandijk, Dennis, and Floris Bex. "Enforcement Heuristics for Argumentation with Deep Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5573–81. http://dx.doi.org/10.1609/aaai.v36i5.20497.

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In this paper, we present a learning-based approach to the symbolic reasoning problem of dynamic argumentation, where the knowledge about attacks between arguments is incomplete or evolving. Specifically, we employ deep reinforcement learning to learn which attack relations between arguments should be added or deleted in order to enforce the acceptability of (a set of) arguments. We show that our Graph Neural Network (GNN) architecture EGNN can learn a near optimal enforcement heuristic for all common argument-fixed enforcement problems, including problems for which no other (symbolic) solvers exist. We demonstrate that EGNN outperforms other GNN baselines and on enforcement problems with high computational complexity performs better than state-of-the-art symbolic solvers with respect to efficiency. Thus, we show our neuro-symbolic approach is able to learn heuristics without the expert knowledge of a human designer and offers a valid alternative to symbolic solvers. We publish our code at https://github.com/DennisCraandijk/DL-Abstract-Argumentation.

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Hodgson, Derek. "Decoding the Blombos Engravings, Shell Beads and Diepkloof Ostrich Eggshell Patterns." Cambridge Archaeological Journal 24, no. 1 (February 2014): 57–69. http://dx.doi.org/10.1017/s0959774313000450.

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The debate regarding the status of the Blombos ochre engravings and shell beads for gauging the timeline of when cognitive abilities and symbolic intent appeared has been controversial. This is mainly due to the fact that what is referred to as symbolic is often too loosely defined and is therefore attributed to artefacts in an indiscriminate way. Recent evidence from various concurrent sites in southern Africa, including Blombos, provide the opportunity for a more nuanced analysis of the probable level of symbolic intent and how this relates to neuro-cognitive precursors. In what follows, it will be shown that, although some of the southern African artefacts do indeed demonstrate particular kinds of ‘symbolic’ intent, others need to be approached with caution. Data from the visual brain is presented that provides crucial evidence as to the appropriate level of intent suggested by the engravings and shell beads from the southern Africa context.

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Onchis, Darian, Codruta Istin, and Eduard Hogea. "A Neuro-Symbolic Classifier with Optimized Satisfiability for Monitoring Security Alerts in Network Traffic." Applied Sciences 12, no. 22 (November 12, 2022): 11502. http://dx.doi.org/10.3390/app122211502.

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We introduce in this paper a neuro-symbolic predictive model based on Logic Tensor Networks, capable of discriminating and at the same time of explaining the bad connections, called alerts or attacks, and the normal connections. The proposed classifier incorporates both the ability of deep neural networks to improve on their own through learning from experience and the interpretability of the results provided by the symbolic artificial intelligence approach. Compared to other existing solutions, we advance in the discovery of potential security breaches from a cognitive perspective. By introducing the reasoning in the model, our aim is to further reduce the human staff needed to deal with the cyber-threat hunting problem. To justify the need for shifting towards hybrid systems for this task, the design, the implementation, and the comparison of the dense neural network and the neuro-symbolic model is performed in detail. While in terms of standard accuracy, both models demonstrated similar precision, we further introduced for our model the concept of interactive accuracy as a way of querying the model results at any time coupled with deductive reasoning over data. By applying our model on the CIC-IDS2017 dataset, we reached an accuracy of 0.95, with levels of satisfiability around 0.85. Other advantages such as overfitting mitigation and scalability issues are also presented.

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Bosselut, Antoine, Ronan Le Bras, and Yejin Choi. "Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 4923–31. http://dx.doi.org/10.1609/aaai.v35i6.16625.

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Understanding narratives requires reasoning about implicit world knowledge related to the causes, effects, and states of situations described in text. At the core of this challenge is how to access contextually relevant knowledge on demand and reason over it. In this paper, we present initial studies toward zero-shot commonsense question answering by formulating the task as inference over dynamically generated commonsense knowledge graphs. In contrast to previous studies for knowledge integration that rely on retrieval of existing knowledge from static knowledge graphs, our study requires commonsense knowledge integration where contextually relevant knowledge is often not present in existing knowledge bases. Therefore, we present a novel approach that generates contextually-relevant symbolic knowledge structures on demand using generative neural commonsense knowledge models. Empirical results on two datasets demonstrate the efficacy of our neuro-symbolic approach for dynamically constructing knowledge graphs for reasoning. Our approach achieves significant performance boosts over pretrained language models and vanilla knowledge models, all while providing interpretable reasoning paths for its predictions.

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Souici-Meslati, Labiba, and Mokhtar Sellami. "Toward a generalization of neuro-symbolic recognition: An application to arabic words." International Journal of Knowledge-based and Intelligent Engineering Systems 10, no. 5 (November 8, 2006): 347–61. http://dx.doi.org/10.3233/kes-2006-10503.

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Roh, Jea-Seung, Won-Chul Shin, Hyun-Kyu Park, and Young-Tack Park. "A Knowledge Completion Approach using Rule Generation based on Neuro-Symbolic Method." Journal of KIISE 48, no. 4 (April 30, 2021): 425–33. http://dx.doi.org/10.5626/jok.2021.48.4.425.

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Shin, Won-Chul, Hyun-Kyu Park, and Young-Tack Park. "Knowledge Completion System using Neuro-Symbolic-based Rule Induction and Inference Engine." Journal of KIISE 48, no. 11 (November 30, 2021): 1202–10. http://dx.doi.org/10.5626/jok.2021.48.11.1202.

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Wang, Jiacheng, Jingpu Zhang, Yideng Cai, and Lei Deng. "DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model." International Journal of Molecular Sciences 20, no. 23 (November 30, 2019): 6046. http://dx.doi.org/10.3390/ijms20236046.

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MicroRNAs (miRNAs) are a highly abundant collection of functional non-coding RNAs involved in cellular regulation and various complex human diseases. Although a large number of miRNAs have been identified, most of their physiological functions remain unknown. Computational methods play a vital role in exploring the potential functions of miRNAs. Here, we present DeepMiR2GO, a tool for integrating miRNAs, proteins and diseases, to predict the gene ontology (GO) functions based on multiple deep neuro-symbolic models. DeepMiR2GO starts by integrating the miRNA co-expression network, protein-protein interaction (PPI) network, disease phenotype similarity network, and interactions or associations among them into a global heterogeneous network. Then, it employs an efficient graph embedding strategy to learn potential network representations of the global heterogeneous network as the topological features. Finally, a deep multi-label classification network based on multiple neuro-symbolic models is built and used to annotate the GO terms of miRNAs. The predicted results demonstrate that DeepMiR2GO performs significantly better than other state-of-the-art approaches in terms of precision, recall, and maximum F-measure.

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Sun, Kexuan, Harsha Rayudu, and Jay Pujara. "A Hybrid Probabilistic Approach for Table Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4366–74. http://dx.doi.org/10.1609/aaai.v35i5.16562.

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Tables of data are used to record vast amounts of socioeconomic, scientific, and governmental information. Although humans create tables using underlying organizational principles, unfortunately AI systems struggle to understand the contents of these tables. This paper introduces an end-to-end system for table understanding, the process of capturing the relational structure of data in tables. We introduce models that identify cell types, group these cells into blocks of data that serve a similar functional role, and predict the relationships between these blocks. We introduce a hybrid, neuro-symbolic approach, combining embedded representations learned from thousands of tables with probabilistic constraints that capture regularities in how humans organize tables. Our neuro-symbolic model is better able to capture positional invariants of headers and enforce homogeneity of data types. One limitation in this research area is the lack of rich datasets for evaluating end-to-end table understanding, so we introduce a new benchmark dataset comprised of 431 diverse tables from data.gov. The evaluation results show that our system achieves the state-of-the-art performance on cell type classification, block identification, and relationship prediction, improving over prior efforts by up to 7% of macro F1 score.

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Alzaeemi, Shehab Abdulhabib. "Accelerating Hyperbolic Tangent Activation Function in Neuro Symbolic Integration using Agent Based Modelling." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 3 (June 25, 2020): 3366–75. http://dx.doi.org/10.30534/ijatcse/2020/136932020.

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Munawar, Asim. "How a shopping mall trip inspired me to work in neuro-symbolic AI." Communications of the ACM 65, no. 5 (April 2022): 11. http://dx.doi.org/10.1145/3528571.

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Palconit, Maria Gemel B., Ronnie S. Concepcion II, Jonnel D. Alejandrino, Michael E. Pareja, Vincent Jan D. Almero, Argel A. Bandala, Ryan Rhay P. Vicerra, Edwin Sybingco, Elmer P. Dadios, and Raouf N. G. Naguib. "Three-Dimensional Stereo Vision Tracking of Multiple Free-Swimming Fish for Low Frame Rate Video." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 5 (September 20, 2021): 639–46. http://dx.doi.org/10.20965/jaciii.2021.p0639.

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Three-dimensional multiple fish tracking has gained significant research interest in quantifying fish behavior. However, most tracking techniques use a high frame rate, which is currently not viable for real-time tracking applications. This study discusses multiple fish-tracking techniques using low-frame-rate sampling of stereo video clips. The fish were tagged and tracked based on the absolute error of the predicted indices using past and present fish centroid locations and a deterministic frame index. In the predictor sub-system, linear regression and machine learning algorithms intended for nonlinear systems, such as the adaptive neuro-fuzzy inference system (ANFIS), symbolic regression, and Gaussian process regression (GPR), were investigated. The results showed that, in the context of tagging and tracking accuracy, the symbolic regression attained the best performance, followed by the GPR, that is, 74% to 100% and 81% to 91%, respectively. Considering the computation time, symbolic regression resulted in the highest computing lag of approximately 946 ms per iteration, whereas GPR achieved the lowest computing time of 39 ms.

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Bonzon, Pierre. "Towards neuro-inspired symbolic models of cognition: linking neural dynamics to behaviors through asynchronous communications." Cognitive Neurodynamics 11, no. 4 (April 1, 2017): 327–53. http://dx.doi.org/10.1007/s11571-017-9435-3.

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Achler, Tsvi. "Neural networks that perform recognition using generative error may help fill the “Neuro-Symbolic Gap”." Biologically Inspired Cognitive Architectures 3 (January 2013): 6–12. http://dx.doi.org/10.1016/j.bica.2012.10.001.

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