Integrating neural networks into the agent’s decision-making: A Systematic Literature Mapping
Resumo
AI systems have been playing a crucial role in many different fields of study. Even though connectionist methods, more precisely deep neural networks, are more prevalent nowadays, many of their limitations have delayed the deployment of AI systems in relevant areas, such as healthcare, financial, and legal. One of its main criticisms relies on the fact that deep neural networks require large data sets, poor generalization, and lack of interpretability. Researchers believe that the next level of AI will require integrating these connectionist methods with different AI’s fields. Although many different studies explore this research topic, many of them are surveys or do not cover AI’s new advances. A Systematic Literature Mapping is performed to fill this gap, which aims to explore the integration of neural networks into the intelligent agent’s decision making. In this study, we analyzed over 1000 papers, and the main findings are: (i) 64% of studies use neural networks to define the learning agent’s reward policies; (ii) 5% of studies explore the integration of neural networks as part of the agent’s reasoning cycle; and (iii) although 55% of studies main contributions are related to neural networks and agents design, we find that the remaining 45% of the studies use both agents and neural networks to solve or contribute to a particular field of study or application.Referências
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Amrani, N., Abra, O., Youssfi, M., and Bouattane, O. (2019). A new interpretation technique of traffic signs, based on deep learning and semantic web. cited By 0.
Anjomshoae, S., Najjar, A., Calvaresi, D., and Främling, K. (2019). Explainable agents and robots: Results from a systematic literature review. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, pages 1078–1088. International Foundation for Autonomous Agents and Multiagent Systems.
Arrieta, A. B., Díaz-Rodríguez, N., Ser, J. D., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., and Herrera, F. (2019). Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai.
Bennetot, A., Laurent, J.-L., Chatila, R., and Díaz-Rodríguez, N. (2019). Towards explainable neural-symbolic visual reasoning.
Chen, I.-M., Zhao, C., and Chan, C.-Y. (2019). A deep reinforcement learning-based approach to intelligent powertrain control for automated vehicles. cited By 0.
Dixon-Woods, M., Cavers, D., Agarwal, S., Annandale, E., Arthur, A., Harvey, J., Hsu, R., Katbamna, S., Olsen, R., Smith, L., et al. (2006). Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups. BMC medical research methodology, 6(1):1–13.
Garcez, A., Besold, T. R., Raedt, L., Foldiak, P., Hitzler, P., Icard, T., Kuhnberger, K.-U., Lamb, L. C., Miikkulainen, R., and Silver, D. L. (2015). Neural-symbolic learning and reasoning: contributions and challenges.
Garcez, A. d., Gori, M., Lamb, L. C., Serafini, L., Spranger, M., and Tran, S. N. (2019). Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning. arXiv preprint arXiv:1905.06088.
Garg, D., Chli, M., and Vogiatzis, G. (2019). A deep reinforcement learning agent for traffic intersection control optimization. cited By 0.
Garnelo, M., Arulkumaran, K., and Shanahan, M. (2016). Towards deep symbolic reinforcement learning. arXiv preprint arXiv:1609.05518.
Garnelo, M. and Shanahan, M. (2019a). Reconciling deep learning with symbolic artificial intelligence: representing objects and relations. Current Opinion in Behavioral Sciences, 29:17–23.
Garnelo, M. and Shanahan, M. (2019b). Reconciling deep learning with symbolic artificial intelligence: representing objects and relations. Current Opinion in Behavioral Sciences, 29:17 – 23. SI: 29: Artificial Intelligence (2019).
Goodman, B. and Flaxman, S. (2017). European union regulations on algorithmic decision-making and a “right to explanation”. AI Magazine, 38(3):50–57.
Jedrzejowicz, P. (2011). Machine learning and agents. In KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications, pages 2–15. Springer.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.
Klose, P. and Mester, R. (2019). Simulated autonomous driving in a realistic driving environment using deep reinforcement learning and a deterministic finite state machine. In Proceedings of the 2nd International Conference on Applications of Intelligent Systems, pages 1–6.
Kotyan, S., Vargas, D., and Venkanna, U. (2019). Self training autonomous driving agent. cited By 0.
Kriesel, D. (2007). A brief introduction on neural networks.
Lamouik, I., Yahyaouy, A., and Sabri, M. (2017). Smart multi-agent traffic coordinator for autonomous vehicles at intersections. cited By 6.
Loumiotis, I., Demestichas, K., Adamopoulou, E., Kosmides, P., Asthenopoulos, V., and Sykas, E. (2018). Road traffic prediction using artificial neural networks. cited By 3.
Marra, G., Giannini, F., Diligenti, M., and Gori, M. (2019). Integrating learning and reasoning with deep logic models. arXiv preprint arXiv:1901.04195.
McCulloch, W. S. and Pitts, W. (1990). A logical calculus of the ideas immanent in nervous activity. Bulletin of mathematical biology, 52(1-2):99–115.
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013). Playing atari with deep reinforcement learning.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015). Human-level control through deep reinforcement learning. nature, 518(7540):529–533.
Ozaki, A. (2020). Learning description logic ontologies: Five approaches. where do they stand? KI-Künstliche Intelligenz, 34(3):317–327.
Parisotto, E., Mohamed, A.-r., Singh, R., Li, L., Zhou, D., and Kohli, P. (2016). Neuro-symbolic program synthesis. arXiv preprint arXiv:1611.01855.
Petticrew, M. and Roberts, H. (2008). Systematic reviews in the social sciences: A practical guide. John Wiley & Sons.
Rizk, Y., Awad, M., and Tunstel, E. (2018). Decision making in multiagent systems: A survey. IEEE Transactions on Cognitive and Developmental Systems, 10(3):514–529. cited By 10.
Russell, S. and Norvig, P. (2002). Artificial intelligence: a modern approach.
Wang, S.-C. (2003). Artificial Neural Network, pages 81–100. Springer US, Boston, MA.
Wooldridge, M. (2009). An introduction to multiagent systems. John Wiley & Sons.
Wooldridge, M., Jennings, N. R., et al. (1995). Intelligent agents: Theory and practice. Knowledge engineering review, 10(2):115–152.
Publicado
10/08/2021
Como Citar
RODRIGUES, Rodrigo; SILVEIRA, Ricardo Azambuja; SANTIAGO, Rafael de.
Integrating neural networks into the agent’s decision-making: A Systematic Literature Mapping. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 15. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 107-118.
ISSN 2326-5434.
