Human-Friendly Explanation Model Based on the Aristotelian Practical Syllogism for Reinforcement Learning Agents in Urban Intelligent Design
Journal
Advances in Soft Computing. MICAI 2025 Posters Track : 24th Mexican International Conference on Artificial Intelligence, MICAI 2025, Guanajuato, Mexico, November 3-7, 2025, Proceedings
ISSN
1865-0929
1865-0937
Publisher
Springer Nature Switzerland
Date Issued
2025
Author(s)
Contreras Olivas, Daniel Adrian
Crespo Murillo, Alan
Type
text::book::book part
Abstract
This paper introduces a novel explanation model for reinforcement learning agents, grounded in Aristotle’s practical syllogism, with the goal of enhancing human-friendly explanations (HFEs) in urban intelligent design. By aligning the internal processes of a Q-learning agent with Aristotelian categories such as telos, phronēsis, and boulesis, the model enables the interpretation of agent behavior as a form of ethically structured practical reasoning. The selected case study focuses on an urban planning agent designed to optimize service accessibility and spatial coherence in real city environments. Through this alignment, the agent’s policy learning, state-action evaluation, and reward optimization are made intelligible to human users in terms of goals, normative principles, perception, deliberation, and action. Evaluated using the Human-Friendly Explanations (HFE) checklist, the model exhibits strengths in interpretability, comprehensibility, and ethical relevance, while identifying areas for improvement such as contrastive reasoning, personalization, and regulatory compliance. This work offers a conceptual and methodological foundation for integrating philosophical models into Explainable Reinforcement Learning (XRL), facilitating transparent, ethical, and user-aligned AI systems. Future directions include empirical validation, interactive implementation, and domain-specific adaptation across sociotechnical contexts. ©The authors ©Springer.
License
Acceso Restringido
How to cite
Olivas, D.A.C., Martinez-Villaseñor, L., Murillo, A.C. (2025). Human-Friendly Explanation Model Based on the Aristotelian Practical Syllogism for Reinforcement Learning Agents in Urban Intelligent Design. In: Martínez-Villaseñor, L., Vázquez, R.A., Ochoa-Ruiz, G. (eds) Advances in Soft Computing. MICAI 2025 Posters Track. MICAI 2025. Communications in Computer and Information Science, vol 2712. Springer, Cham. https://doi.org/10.1007/978-3-032-08704-1_4