Evaluación multicriterio Neutrosófica de arquitecturas de agentes de IA mediante AHP–TOPSIS: Desempeño, envejecimiento funcional y utilidad en ingeniería de software

Dalton Andrés Pazmiño Pérez; Christian Miguel Muñoz Carrasco; Manuel Reyes Wagnio; Dayron Rumbaut Rangel

Evaluación multicriterio Neutrosófica de arquitecturas de agentes de IA mediante AHP–TOPSIS: Desempeño, envejecimiento funcional y utilidad en ingeniería de software

Autores/as

Dalton Andrés Pazmiño Pérez Universidad Bolivariana del Ecuador
Christian Miguel Muñoz Carrasco Universidad Bolivariana del Ecuador
Manuel Reyes Wagnio Universidad Bolivariana del Ecuador
Dayron Rumbaut Rangel Universidad Bolivariana del Ecuador

Resumen

Este artículo presenta una evaluación multicriterio de tres arquitecturas de agentes de IA en ingeniería de software mediante AHP–TOPSIS con conjuntos Neutrosófica de valor único (SVNS). Se evalúan el Agente Reactivo Basado en Reglas (RBA), DLA-RAG (Deep Learning + Retrieval-Augmented Generation) y el Agente Híbrido Simbólico-Neuronal (HSN), bajo cinco criterios: degradación por envejecimiento funcional (Aging-Aware Score, AAS), gestión de la indeterminación, calidad de datos (Data-Centric AI), eficiencia operativa y estabilidad. Los pesos AHP, derivados del eigenvector principal (C1=36.52%, CR=0.0013), priorizan la degradación funcional. El ranking TOPSIS posiciona a DLA-RAG como arquitectura dominante (Ci=0.979), validado mediante dos escenarios de sensibilidad independientes. Los hallazgos sugieren que, conforme al juicio experto agregado, la utilidad real de los agentes de IA depende más de su sostenibilidad funcional que de su sofisticación algorítmica inicial.

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Citas

Abdel-Basset, M., Mohamed, M., & Smarandache, F. (2018). A hybrid neutrosophic group ANP-TOPSIS framework for supplier selection problems. Symmetry, 10(6), 226. https://doi.org/10.3390/sym10060226

Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3

Biswas, P., Pramanik, S., & Giri, B. C. (2016). TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing and Applications, 27(3), 727–737. https://doi.org/10.1007/s00521-015-1891-2

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., Bernstein, M. S., et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258. https://doi.org/10.48550/arXiv.2108.07258

Broumi, S., Nagarajan, D., Bakali, A., Talea, M., Smarandache, F., & Lathamaheswari, M. (2019). The shortest path problem in interval valued trapezoidal and triangular neutrosophic environment. Complex and Intelligent Systems, 5(4), 391–402. https://doi.org/10.1007/s40747-019-0092-5

Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. O., Kaplan, J., et al. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374. https://doi.org/10.48550/arXiv.2107.03374

Chi, P., & Liu, P. (2013). An extended TOPSIS method for the multiple attribute decision making problems based on interval neutrosophic set. Neutrosophic Sets and Systems, 1(1), 63–70. http://fs.unm.edu/NSS/AnExtendedTOPSIS.pdf

Edalatpanah, S. A., & Smarandache, F. (2019). Data envelopment analysis for simplified neutrosophic sets. Neutrosophic Sets and Systems, 29, 215–226. https://doi.org/10.5281/zenodo.3514433

Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4), Article 44. https://doi.org/10.1145/2523813

Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., & Wang, H. (2024). Large language models for software engineering: A systematic literature review. ACM Transactions on Software Engineering and Methodology, 33(8), 1–79. https://doi.org/10.1145/3695988

Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer. https://doi.org/10.1007/978-3-642-48318-9

Leyva Vázquez, M. Y., & Smarandache, F. (2018). Neutrosofía: Nuevos avances en el tratamiento de la incertidumbre. Pons Publishing House. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3729638

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1–35. https://doi.org/10.1145/3560815

Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229. https://doi.org/10.1145/3287560.3287596

Okoli, C., & Pawlowski, S. D. (2004). The Delphi method as a research tool: An example, design considerations and applications. Information & Management, 42(1), 15–29. https://doi.org/10.1016/j.im.2003.11.002

Peng, X., & Dai, J. (2020). A bibliometric analysis of neutrosophic set: Two decades review from 1998 to 2017. Artificial Intelligence Review, 53(1), 199–255. https://doi.org/10.1007/s10462-018-9652-0

Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4.ª ed.). Pearson. ISBN 978-0-13-461099-3.

Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98. https://doi.org/10.1504/IJSSci.2008.017590

Salmerón, J. L., & Smarandache, F. (2020). Redesigning decision matrix method with an indeterminacy-based inference process. Advances in Fuzzy Systems, 2020, Article 816038. https://doi.org/10.1155/2020/816038

Smarandache, F. (1998). Neutrosophy: A new branch of philosophy. Multiple-Valued Logic, 8(3), 297–384. American Research Press. http://fs.unm.edu/Neutrosophy.pdf

Wang, H., Smarandache, F., Zhang, Y. Q., & Sunderraman, R. (2010). Single valued neutrosophic sets. Multispace and Multistructure, 4, 410–413.

Wooldridge, M. (2020). An introduction to multiagent systems (2.ª ed.). Wiley. ISBN 978-0-470-51946-2.

Zha, D., Bhat, Z. P., Lai, K. H., Yang, F., Jiang, Z., Zhong, S., & Hu, X. (2023). Data-centric artificial intelligence: A survey. arXiv preprint arXiv:2303.10158. https://doi.org/10.48550/arXiv.2303.10158