Explain what you see:argumentation-based learning and robotic vision

In this thesis, we have introduced new techniques for the problems of open-ended learning, online incremental learning, and explainable learning. These methods have applications in the classification of tabular data, 3D object category recognition, and 3D object parts segmentation. We have utilized argumentation theory and probability theory to develop these methods. The first proposed open-ended online incremental learning approach is Argumentation-Based online incremental Learning (ABL). ABL works with tabular data and can learn with a small number of learning instances using an abstract argumentation framework and bipolar argumentation framework. It has a higher learning speed than state-of-the-art online incremental techniques. However, it has high computational complexity. We have addressed this problem by introducing Accelerated Argumentation-Based Learning (AABL). AABL uses only an abstract argumentation framework and uses two strategies to accelerate the learning process and reduce the complexity. The second proposed open-ended online incremental learning approach is the Local Hierarchical Dirichlet Process (Local-HDP). Local-HDP aims at addressing two problems of open-ended category recognition of 3D objects and segmenting 3D object parts. We have utilized Local-HDP for the task of object part segmentation in combination with AABL to achieve an interpretable model to explain why a certain 3D object belongs to a certain category. The explanations of this model tell a user that a certain object has specific object parts that look like a set of the typical parts of certain categories. Moreover, integrating AABL and Local-HDP leads to a model that can handle a high degree of occlusion

Argue to Learn:Accelerated Argumentation-Based Learning

Human agents can acquire knowledge and learn through argumentation. Inspired by this fact, we propose a novel argumentation-based machine learning technique that can be used for online incremental learning scenarios. Existing methods for online incremental learning problems typically do not generalize well from just a few learning instances. Our previous argumentation-based online incremental learning method outperformed state-of-the-art methods in terms of accuracy and learning speed. However, it was neither memory-efficient nor computationally efficient since the algorithm used the power set of the feature values for updating the model. In this paper, we propose an accelerated version of the algorithm, with polynomial instead of exponential complexity, while achieving higher learning accuracy. The proposed method is at least 200 times faster than the original argumentation-based learning method and is more memory-efficient

Deliberative Context-Aware Ambient Intelligence System for Assisted Living Homes

Monitoring wellbeing and stress is one of the problems covered by ambient intelligence, as stress is a significant cause of human illnesses directly affecting our emotional state. The primary aim was to propose a deliberation architecture for an ambient intelligence healthcare application. The architecture provides a plan for comforting stressed seniors suffering from negative emotions in an assisted living home and executes the plan considering the environment's dynamic nature. Literature was reviewed to identify the convergence between deliberation and ambient intelligence and the latter's latest healthcare trends. A deliberation function was designed to achieve context-aware dynamic human-robot interaction, perception, planning capabilities, reactivity, and context-awareness with regard to the environment. A number of experimental case studies in a simulated assisted living home scenario were conducted to demonstrate the approach's behavior and validity. The proposed methods were validated to show classification accuracy. The validation showed that the deliberation function has effectively achieved its deliberative objectives

Deliberative Context-Aware Ambient Intelligence System for Assisted Living Homes

[EN] Monitoring wellbeing and stress is one of the problems covered by ambient intelligence, as stress is a significant cause of human illnesses directly affecting our emotional state. The primary aim was to propose a deliberation architecture for an ambient intelligence healthcare application. The architecture provides a plan for comforting stressed seniors suffering from negative emotions in an assisted living home and executes the plan considering the environment¿s dynamic nature. Literature was reviewed to identify the convergence between deliberation and ambient intelligence and the latter¿s latest healthcare trends. A deliberation function was designed to achieve context-aware dynamic human-robot interaction, perception, planning capabilities, reactivity, and context-awareness with regard to the environment. A number of experimental case studies in a simulated assisted living home scenario were conducted to demonstrate the approach¿s behavior and validity. The proposed methods were validated to show classification accuracy. The validation showed that the deliberation function has effectively achieved its deliberative objectives.This work is supported by the Spanish MINECO Project (No. TIN2017-88476-C2-1-R) and the Universitat Politecnica de Valencia Research (Grant No. PAID-10-19).Babli, M.; Rincón-Arango, JA.; Onaindia De La Rivaherrera, E.; Carrascosa Casamayor, C.; Julian, V. (2021). Deliberative Context-Aware Ambient Intelligence System for Assisted Living Homes. Human-Centric Computing and Information Sciences. 11:1-18. https://doi.org/10.22967/HCIS.2021.11.0191181

A risk science perspective on the discussion concerning Safety I, Safety II and Safety III

Recently, there has been a discussion in the safety science community concerning the validity of basic approaches to safety, referred to as Safety I, Safety II and Safety III, with Erik Hollnagel and Nancy Leveson in leading roles. The present paper seeks to obtain new knowledge concerning the issues raised, by adding a contemporary risk science perspective to the discussion. Risk is, to a limited degree, addressed in the literature presenting and studying these three approaches; however, as argued in the paper, the concept of risk and risk analysis and management principles and methods are highly relevant and useful for understanding the three safety approaches, deciding on their suitability, and eventually applying them. The paper underlines the importance of an integration of the safety and risk sciences, to further enhance concepts, approaches, principles, methods and models for understanding, assessing, communicating and managing system performance.publishedVersio

Local-HDP:Interactive Open-Ended 3D Object Categorization

We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). This method allows an agent to learn independent topics for each category incrementally and to adapt to the environment in time. Hierarchical Bayesian approaches like Latent Dirichlet Allocation (LDA) can transform low-level features to high-level conceptual topics for 3D object categorization. However, the efficiency and accuracy of LDA-based approaches depend on the number of topics that is chosen manually. Moreover, fixing the number of topics for all categories can lead to overfitting or underfitting of the model. In contrast, the proposed Local-HDP can autonomously determine the number of topics for each category. Furthermore, an inference method is proposed that results in a fast posterior approximation. Experiments show that Local-HDP outperforms other state-of-the-art approaches in terms of accuracy, scalability, and memory efficiency with a large margin