1,720,957 research outputs found
Imitation learning in multi-sensor self-aware autonomous systems
No full textThe fusion of different sensory information in an integrated way for an Artificial Intelligence (AI) model is the most advancing research direction in building a self-aware agent. Single data modalities are short in representing and exploiting the representation in any real scenario. Information fusion plays a vital role in a holistic understanding of a situation in addition to sensor fail-safe applications. Nowadays, training an AI model involves experience modeling from expert-generated data. These datasets are vast, highly non-linear, and high dimensional. Integrating these highly complex and dynamic experiences robustly is challenging. Understanding different structures in higher dimensional sensory input, also fusing them with lower dimensional information, has been a primary objective in the progress of powerful generative models.
Recent advancements in sensory information integration for the development of explainable AI models are progressing through the combination of probabilistic self-expressive Bayesian models and Deep Learning approaches. Dynamic Bayesian Networks (DBNs) are unified general probabilistic representations and inference mechanisms for time-series domains that can be used to introduce explainability in an AI model through their causal-effect relations. Integrating DBNs with Variational Autoencoders (VAEs), to represent higher dimensional data, makes them powerful and scalable, and can be used for self-supervised learning of higher dimensional data distributions.
Autonomous driving is an area where the research direction is advancing to integrate explainable AI models to facilitate the realization of full autonomy. Increasing convenience to free up time, increasing the mobility of people with disabilities, improving environmental health, minimizing human driving errors, and minimizing the economic impact through car sharing and carpooling, make the current AI research trend focus more on generalized AI models for Self-Driving cars.
This thesis introduces a novel Multi-Agent Self-Awareness Architecture (MASAA) that integrates generative World Model (WM), Experience Models (Multi-Modal Perception, MMP), Active First-Person Model, Cost model (Multi-level Anomalies) through the Short-Term Memory Module (Multi-Agent Coupled Markov Jump Particle Filters, MAC-MJPFs). In the MASAA framework, a set of modules are introduced to enable an agent to localize itself while interacting with neighboring agents simultaneously.
One of the main goals of MASAA is to enhance the generalizability and interpretability of an AI model. To this end, this thesis introduces a novel methodology that integrates multi-sensorial data from proprioceptive and exteroceptive sensors of an agent, coupled in a Hierarchical DBN model in an Active Inference framework. A lower-dimensional unsupervised learning stage, considering both odometry and action modalities, is carried out by applying Null Force Filtering (NFF) and a modified Growing Neural Gas (GNG) clustering algorithm, thus producing lower-dimensional knowledge. A self-supervised higher-dimensional video modality learning stage using VAEs, guided by the learned lower-dimensional vocabularies, creates integrated multi-sensorial inference knowledge. An online model-based active learning in continuous and discrete state spaces and action spaces for decision-making in the Active Inference framework is developed. These representation and decision-making models are evaluated using localization and interaction benchmarks
A data-driven approach for the localization of interacting agents via a multi-modal Dynamic Bayesian Network framework
Get PDFProceedings of 2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 29 Nov. - 2 Dec. 2022, Madrid, SpainThis paper proposes a multi-modal situational inter-action model for collaborative agents by fusing multi-sensorial information in a Multi-Agent Hierarchical Dynamic Bayesian Network (MAH-DBN) framework. The proposed model is learned in a data-driven methodology to estimate the states of interacting agents only from video sequences. This can be regarded as a two-fold methodology for improving visual-based localization and interaction between autonomous agents. In the learning stage, the odometry model is used to drive the video learning model for a robust localization and interaction modeling. During the testing phase, the learned Multi-Agent Hierarchical DBN (MAH-DBN) model is used for the localization of collaborative agents only from video sequences by proposing an inference method called Multi-Agent Coupled Markov Jump Particle Filter (MAC-MJPF)
Modeling Interactions Between Autonomous Agents in a Multi-Agent Self-Awareness Architecture
No full textLearning from experience is a fundamental capability of intelligent agents. Autonomous systems rely on sensors that provide data about the environment and internal situations to their perception systems for learning and inference mechanisms. These systems can also learn Self-Aware and Situation-Aware generative modules from these data to localize themselves and interact with the environment. In this paper, we propose a self-aware cognitive architecture capable to perform tasks where the interactions between the self-state of an agent and the surrounding environment are explicitly and dynamically represented. We specifically develop a Deep Learning (DL) based Self-Aware interaction model, empowered by learning from Multi-Modal Perception (MMP) and World Models using multi-sensory data in a novel Multi-Agent Self-Awareness Architecture (MASAA). Two sub-modules are developed, the Situation Model (SM) and the First-Person model (FPM), that address different and interrelated aspects of the World Model (WM). The MMP model, instead, aims at learning the mapping of different sensory perceptions into Exteroceptive (EI) and Proprioceptive (PI) latent information. The WM then uses the learned MMP model as experience to predict dynamic self-behaviors and interaction patterns within the experienced environment. WM and MMP Models are learned in a data-driven way, starting from the lower-dimensional odometry data used to guide the learning of higher-dimensional video data, thus generating coupled Generalized State Hierarchical Dynamic Bayesian Networks (GS-HDBNs). We test our model on KITTI, CARLA, and iCab datasets, achieving high performance and a low average localization error (RMSE) of 2.897%, when considering two interacting agents
Integrated Learning and Decision Making for Autonomous Agents through Energy based Bayesian Models
No full textGeneralizability and interpretability are common terminologies that can be found in today’s machine learning algorithm design. Generalizability requires a clear understanding of one’s own action (self-awareness) and a robust interaction with the environment (situation awareness). Many current studies are devoted in developing an algorithm that is more robust in generalizing unseen situations while explaining self-action. However, such algorithms are complex and are not yet fully developed to be used in production. Intelligent transportation systems like self-driving cars are one of the emerging technologies that need generalizability and explainability in anomalous conditions. We propose to enhance generalizability and interpretability of a self-driving car model by introducing a novel methodology that fuses multi-sensorial data from proprioceptive and exteroceptive sensors of an agent, coupled in a Hierarchical Dynamic Bayesian Network model, in an Active Inference framework. The developed model has three stages: 1) a lower dimensional unsupervised learning stage, considering odometry and action modalities, carried out by first applying Null Force Filtering and then by applying modified GNG clustering algorithms; 2) a self-supervised higher-dimensional video modality learning stage assisted by the learned odometry vocabularies; and 3) an online model-based active learning in continuous and discrete state spaces, and action spaces, in the Active Inference framework. The developed system is tested using the CARLA simulator environment for localizing interacting agents, and exhibits low error compared to state-of-the-art methods.Generalizability and interpretability are common terminologies that can be found in today's machine learning algorithm design. Generalizability requires a clear understanding of one's own action (self-awareness) and a robust interaction with the environment (situation awareness). Many current studies are devoted in developing an algorithm that is more robust in generalizing unseen situations while explaining self-action. However, such algorithms are complex and are not yet fully developed to be used in production. Intelligent transportation systems like self-driving cars are one of the emerging technologies that need generalizability and explainability in anomalous conditions. We propose to enhance generalizability and interpretability of a self-driving car model by introducing a novel methodology that fuses multi-sensorial data from proprioceptive and exteroceptive sensors of an agent, coupled in a Hierarchical Dynamic Bayesian Network model, in an Active Inference framework. The developed model has three stages: 1) a lower dimensional unsupervised learning stage, considering odometry and action modalities, carried out by first applying Null Force Filtering and then by applying modified GNG clustering algorithms; 2) a self-supervised higher-dimensional video modality learning stage assisted by the learned odometry vocabularies; and 3) an online model-based active learning in continuous and discrete state spaces, and action spaces, in the Active Inference framework. The developed system is tested using the CARLA simulator environment for localizing interacting agents, and exhibits low error compared to state-of-the-art methods
A Kalman Variational Autoencoder Model Assisted by Odometric Clustering for Video Frame Prediction and Anomaly Detection
Get PDFThe combination of different sensory information to predict upcoming situations is an innate capability of intelligent beings. Consequently, various studies in the Artificial Intelligence field are currently being conducted to transfer this ability to artificial systems. Autonomous vehicles can particularly benefit from the combination of multi-modal information from the different sensors of the agent. This paper proposes a method for video-frame prediction that leverages odometric data. It can then serve as a basis for anomaly detection. A Dynamic Bayesian Network framework is adopted, combined with the use of Deep Learning methods to learn an appropriate latent space. First, a Markov Jump Particle Filter is built over the odometric data. This odometry model comprises a set of clusters. As a second step, the video model is learned. It is composed of a Kalman Variational Autoencoder modified to leverage the odometry clusters for focusing its learning attention on features related to the dynamic tasks that the vehicle is performing. We call the obtained overall model Cluster-Guided Kalman Variational Autoencoder. Evaluation is conducted using data from a car moving in a closed environment and leveraging a part of the University of Alcalá DriveSet dataset, where several drivers move in a normal and drowsy way along a secondary road.This work was supported in part by the Spanish Government under Grant PID2019-104793RB-C31, Grant PDC2021-121517-C31, and Grant PID2021 124335OB-C21; and in part by the Comunidad de Madrid under Grant SEGVAUTO-4.0-CM (P2018/EMT-4362)
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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