Multimodal explanations for explainable AI: an empirical study of text-video combinations on end-user understandability
Explainable Artificial Intelligence (XAI) has become increasingly important as deep reinforcement learning (RL) systems are deployed in safety-critical and human-facing applications. While recent methods provide post-hoc attributions for single decisions, they often fail to capture the sequential an...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/450577 |
| Acceso en línea: | https://hdl.handle.net/2117/450577 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial intelligence Reinforcement learning Computer vision Intel·ligència artificial Aprenentatge per reforç Visió per ordinador Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | Explainable Artificial Intelligence (XAI) has become increasingly important as deep reinforcement learning (RL) systems are deployed in safety-critical and human-facing applications. While recent methods provide post-hoc attributions for single decisions, they often fail to capture the sequential and model-based nature of RL agents. In particular, model-based RL agents such as Dreamer rely on imagined rollouts to plan their actions, yet these internal simulations remain invisible to external observers. This creates a gap between what the agent predicts and what users can understand. This thesis investigates whether multimodal explanations, combining visual rollouts with natural language, can bridge this gap. A framework that integrates observed and imagined trajectories from DreamerV3 with instruction-tuned vision–language models (VLMs) is proposed. The pipeline extracts rollouts from MiniGrid environments, preprocesses them into single-frame, sequence, and comparative conditions, and generates explanations under a set of prompting strategies. After reviewing candidate models, Qwen-VL 2.5 was selected for its balance of accessibility, grounding quality, and feasibility. The resulting explanations were evaluated using a combination of automatic metrics and qualitative analysis focusing on factual consistency, temporal grounding, and divergence handling. The results show that VLMs can produce coherent descriptions of agent behaviour and partially capture temporal dependencies, though challenges remain with hallucinations and alignment between observed and imagined rollouts. Comparative explanations, in particular, highlighted divergences that revealed the agent’s planning process. This work contributes a reproducible methodology for generating and assessing multimodal explanations of model-based RL agents and suggests directions for future research, including user studies and integration with interactive explanation systems. |
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