Using fractal interpolation for time series prediction

June 7, 2024 11:13 ,

Alexandra Băicoianu, Cristina Gabriela Gavrilă, Cristina Maria Păcurar, Victor Dan Păcurar, Fractal interpolation in the context of prediction accuracy optimization, Engineering Applications of Artificial Intelligence, Volume 133, Part D, 2024 DOI: 10.1016/j.engappai.2024.108380.

This paper focuses on the hypothesis of optimizing time series predictions using fractal interpolation techniques. In general, the accuracy of machine learning model predictions is closely related to the quality and quantitative aspects of the data used, following the principle of garbage-in, garbage-out. In order to quantitatively and qualitatively augment datasets, one of the most prevalent concerns of data scientists is to generate synthetic data, which should follow as closely as possible the actual pattern of the original data. This study proposes three different data augmentation strategies based on fractal interpolation, namely the Closest Hurst Strategy, Closest Values Strategy and Formula Strategy. To validate the strategies, we used four public datasets from the literature, as well as a private dataset obtained from meteorological records in the city of Braşov, Romania. The prediction results obtained with the LSTM model using the presented interpolation strategies showed a significant accuracy improvement compared to the raw datasets, thus providing a possible answer to practical problems in the field of remote sensing and sensor sensitivity. Moreover, our methodologies answer some optimization-related open questions for the fractal interpolation step using Optuna framework.

Posted in: Probability and statistics , Tagged: ,

Change point detection through self-supervised learning

June 7, 2024 10:53 ,

Xiangyu Bao, Liang Chen, Jingshu Zhong, Dianliang Wu, Yu Zheng, A self-supervised contrastive change point detection method for industrial time series, Engineering Applications of Artificial Intelligence, Volume 133, Part B, 2024, DOI: 10.1016/j.engappai.2024.108217.

Manufacturing process monitoring is crucial to ensure production quality. This paper formulates the detection problem of abnormal changes in the manufacturing process as the change point detection (CPD) problem for the industrial temporal data. The premise of known data property and sufficient data annotations in existing CPD methods limits their application in the complex manufacturing process. Therefore, a self-supervised and non-parametric CPD method based on temporal trend-seasonal feature decomposition and contrastive learning (CoCPD) is proposed. CoCPD aims to solve CPD problem in an online manner. By bringing the representations of time series segments with similar properties in the feature space closer, our model can sensitively distinguish the change points that do not conform to either historical data distribution or temporal continuity. The proposed CoCPD is validated by a real-world body-in-white production case and compared with 10 state-of-the-art CPD methods. Overall, CoCPD achieves promising results by Precision 70.6%, Recall 68.8%, and the mean absolute error (MAE) 8.27. With the ability to rival the best offline baselines, CoCPD outperforms online baseline methods with improvements in Precision, Recall and MAE by 14.90%, 11.93% and 43.93%, respectively. Experiment results demonstrate that CoCPD can detect abnormal changes timely and accurately.

See also: https://doi.org/10.1016/j.engappai.2024.108155

Posted in: Probability and statistics , Tagged: , , ,

Reducing discovered skills in DRL to the essential ones, modelling skills with SMDP Q-learning

June 7, 2024 10:38 ,

Shuai Qing, Fei Zhu, Refine to the essence: Less-redundant skill learning via diversity clustering, Engineering Applications of Artificial Intelligence, Volume 133, Part A, 2024 DOI: 10.1016/j.engappai.2024.107981.

In reinforcement learning, skill is a potentially conditional policy that solves tasks in a hierarchically controlled manner. Progress on skill discovery helps agents learn a set of diverse and useful skills without external supervision to tackle complex tasks with sparse rewards. Although most of the studies have aimed to maximize the diversity of skills discovered, the distinguishability between skills diminishes as the number of skills increases, leading to a subset of similar and redundant skills. To tackle this problem, a method called Refine to the Essence of Skills (RE-Skill) is proposed, which aims at learning skills with less redundancy. RE-Skill integrates the concepts of cluster analysis and policy distillation, clustering similar skills together based on their unique features, learning the most optimal performance within each cluster, and filtering out similar skills that involve excessive and intricate actions, thereby reducing redundancy among skills. By refining clusters of similar skills into less-redundant independent skills, RE-Skill demonstrates superior performance compared to other skill discovery algorithms and shows how these less-redundant skills effectively address downstream tasks, indicating that RE-Skill is able to extend its efficacy to engineering applications in robot control and obstacle training tasks within complex environments.

Posted in: Reinforcement learning in AI , Tagged: , , ,

A survey on neurosymbolic RL and planning

May 23, 2024 16:16 ,

K. Acharya, W. Raza, C. Dourado, A. Velasquez and H. H. Song, Neurosymbolic Reinforcement Learning and Planning: A Survey, IEEE Transactions on Artificial Intelligence, vol. 5, no. 5, pp. 1939-1953, May 2024 DOI: 10.1109/TAI.2023.3311428.

The area of neurosymbolic artificial intelligence (Neurosymbolic AI) is rapidly developing and has become a popular research topic, encompassing subfields, such as neurosymbolic deep learning and neurosymbolic reinforcement learning (Neurosymbolic RL). Compared with traditional learning methods, Neurosymbolic AI offers significant advantages by simplifying complexity and providing transparency and explainability. Reinforcement learning (RL), a long-standing artificial intelligence (AI) concept that mimics human behavior using rewards and punishment, is a fundamental component of Neurosymbolic RL, a recent integration of the two fields that has yielded promising results. The aim of this article is to contribute to the emerging field of Neurosymbolic RL by conducting a literature survey. Our evaluation focuses on the three components that constitute Neurosymbolic RL: neural, symbolic, and RL. We categorize works based on the role played by the neural and symbolic parts in RL, into three taxonomies: learning for reasoning, reasoning for learning, and learning–reasoning. These categories are further divided into subcategories based on their applications. Furthermore, we analyze the RL components of each research work, including the state space, action space, policy module, and RL algorithm. In addition, we identify research opportunities and challenges in various applications within this dynamic field.

Posted in: Reinforcement learning theory , Tagged: , ,

Ultrasounds as an interface to read neuro-muscular signals

May 23, 2024 16:13 ,

X. Yang, C. Castellini, D. Farina and H. Liu, Ultrasound as a Neurorobotic Interface: A Review, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 6, pp. 3534-3546, June 2024 DOI: 10.1109/TSMC.2024.3358960.

Neurorobotic devices, such as prostheses, exoskeletons, and muscle stimulators, can partly restore motor functions in individuals with disabilities, such as stroke, spinal cord injury (SCI), and amputations and musculoskeletal impairments. These devices require information transfer from and to the nervous system by neurorobotic interfaces. However, current interfacing systems have limitations of low-spatial and temporal resolution, and lack robustness, with sensitivity to, e.g., fatigue and sensor displacement. Muscle scanning and imaging by ultrasound technology has emerged as a neurorobotic interface alternative to more conventional electrophysiological recordings. While muscle ultrasound detects movement of muscle fibers, and therefore does not directly detect neural information, the muscle fibers are activated by neurons in the spinal cord and therefore their motions mirror the neural code sent from the spinal cord to muscles. In this view, muscle imaging by ultrasound provides information on the neural activation underlying movement intent and execution. Here, we critically review the literature on ultrasound applied as a neurorobotic interface, focusing on technological progresses and current achievements, machine learning algorithms, and applications in both upper- and lower- limb robotics. This critical review reveals that ultrasound in the human-machine interface field has evolved from bulky hardware to miniaturized systems, from multichannel imaging to sparse channel sensing, from simple muscle morphological analysis to input signal for musculoskeletal models and machine learning, from unimodal sensing to multimodal fusion, and from conventional statistical learning to deep learning. For future advances, we recommend exploring high-precision ultrasound imaging technology, improving the wearability and ergonomics of systems and transducers, and developing user-friendly real-time human-machine interaction models.

Posted in: Physiological engineering , Tagged:

An alternative conceptual basis for curiosity / motivation

May 9, 2024 08:21 ,

Francesco Poli, Jill X. O’Reilly, Rogier B. Mars, Sabine Hunnius, Curiosity and the dynamics of optimal exploration, Trends in Cognitive Sciences, Volume 28, Issue 5, 2024, DOI: 10.1016/j.tics.2024.02.001.

What drives our curiosity remains an elusive and hotly debated issue, with multiple hypotheses proposed but a cohesive account yet to be established. This review discusses traditional and emergent theories that frame curiosity as a desire to know and a drive to learn, respectively. We adopt a model-based approach that maps the temporal dynamics of various factors underlying curiosity-based exploration, such as uncertainty, information gain, and learning progress. In so doing, we identify the limitations of past theories and posit an integrated account that harnesses their strengths in describing curiosity as a tool for optimal environmental exploration. In our unified account, curiosity serves as a ‘common currency’ for exploration, which must be balanced with other drives such as safety and hunger to achieve efficient action.

Posted in: Psycho-physiological bases of engineering , Tagged:

A novel RL setting for non-Markovian systems

April 26, 2024 09:38 ,

Ronen I. Brafman, Giuseppe De Giacomo, Regular decision processes, Artificial Intelligence, Volume 331, 2024 DOI: 10.1016/j.artint.2024.104113.

We introduce and study Regular Decision Processes (RDPs), a new, compact model for domains with non-Markovian dynamics and rewards, in which the dependence on the past is regular, in the language theoretic sense. RDPs are an intermediate model between MDPs and POMDPs. They generalize k-order MDPs and can be viewed as a POMDP in which the hidden state is a regular function of the entire history. In factored RDPs, transition and reward functions are specified using formulas in linear temporal logics over finite traces, or using regular expressions. This allows specifying complex dependence on the past using intuitive and compact formulas, and building models of partially observable domains without specifying an underlying state space.

Posted in: Reinforcement learning in AI , Tagged: , ,

The problem of incorporating novelties into the knowledge of an AI agent

April 26, 2024 09:34 ,

Shivam Goel, Panagiotis Lymperopoulos, Ravenna Thielstrom, Evan Krause, Patrick Feeney, Pierrick Lorang, Sarah Schneider, Yichen Wei, Eric Kildebeck, Stephen Goss, Michael C. Hughes, Liping Liu, Jivko Sinapov, Matthias Scheutz, A neurosymbolic cognitive architecture framework for handling novelties in open worlds, Artificial Intelligence, Volume 331, 2024 DOI: 10.1016/j.artint.2024.104111.

“Open world” environments are those in which novel objects, agents, events, and more can appear and contradict previous understandings of the environment. This runs counter to the “closed world” assumption used in most AI research, where the environment is assumed to be fully understood and unchanging. The types of environments AI agents can be deployed in are limited by the inability to handle the novelties that occur in open world environments. This paper presents a novel cognitive architecture framework to handle open-world novelties. This framework combines symbolic planning, counterfactual reasoning, reinforcement learning, and deep computer vision to detect and accommodate novelties. We introduce general algorithms for exploring open worlds using inference and machine learning methodologies to facilitate novelty accommodation. The ability to detect and accommodate novelties allows agents built on this framework to successfully complete tasks despite a variety of novel changes to the world. Both the framework components and the entire system are evaluated in Minecraft-like simulated environments. Our results indicate that agents are able to efficiently complete tasks while accommodating “concealed novelties” not shared with the architecture development team.

Posted in: Artificial Intelligence , Tagged:

Imitating physiological processes for achieving robot-human social interaction

April 26, 2024 09:31 ,

Marcos Maroto-Gómez, Martín Bueno-Adrada, María Malfaz, Álvaro Castro-González, Miguel Ángel Salichs, Human–robot pair-bonding from a neuroendocrine perspective: Modeling the effect of oxytocin, arginine vasopressin, and dopamine on the social behavior of an autonomous robot, Robotics and Autonomous Systems, Volume 176, 2024 DOI: 10.1016/j.robot.2024.104687.

Robots and humans coexist in various social environments. In these contexts, robots predominantly serve as assistants, necessitating communication and understanding capabilities. This paper introduces a biologically inspired model grounded on neuroendocrine substances that facilitate the development of social bonds between robots and individuals. The model simulates the effects of oxytocin, arginine vasopressin, and dopamine on social behavior, acting as modulators for bonding in the interaction between the social robot Mini and its users. Neuroendocrine levels vary in response to circadian rhythms and social stimuli perceived by the robot. If users express care for the robot, a positive bond is established, enhancing human–robot interaction by prompting the robot to engage in cooperative actions such as playing or communicating more frequently. Conversely, mistreating the robot leads to a deterioration of the relationship, causing user rejection. An experimenter-robot interaction scenario illustrates the model’s adaptive mechanisms involving three types of profiles: Friendly, Aversive, and Naive. Besides, a user study with 22 participants was conducted to analyze the differences in Attachment, Social Presence, perceived Anthropomorphism, Likability, and User Experience between a robot randomly selecting its behavior and a robot behaving using the bioinspired pair-bonded method proposed in this contribution. The results show how the pair-bonding with the user regulates the robot’s social behavior in response to user actions. The user study reveals statistical differences favoring the robot using the pair-bonding regulation in Attachment and Social Presence. A qualitative study using an interview-like form suggests the positive effects of creating bonds with bioinspired robots.

Posted in: Psycho-physiological bases of engineering , Tagged:

Interesting testing (simulated) bed for quadrotors

April 26, 2024 09:26 ,

Júnio Santos Bulhões, Cristiane Lopes Martins, Cristian Hansen, Márcio Rodrigues da Cunha Reis, Alana da Silva Magalhães, Antonio Paulo Coimbra, Wesley Pacheco Calixto, Platform and simulator with three degrees of freedom for testing quadcopters, Robotics and Autonomous Systems, Volume 176, 2024 DOI: 10.1016/j.robot.2024.104682.

This study aims to design a test platform for quadcopters, which allows the execution of all rotational movements and prevents translational movements without affecting the dynamics of the system. The methodological approach involves both simulation and the construction of the test platform. Two simulators are developed: (i) a linear simulator, used to assist in determining control parameters, and (ii) a nonlinear simulator, used to model the nonlinearity inherent to the rotational behavior of aircraft. In addition, the control system for the quadcopter is implemented, utilizing proportional, integral, and derivative control principles. By conducting seven experiments on the test platform and in the nonlinear simulator, the obtained results are compared in order to validate the proposed methodology. The mean discrepancy observed between the mean absolute difference obtained by the test platform and by the nonlinear simulator for the angle ϕ was 0.85°, for the angle θ was 2.77°, and for the angle ψ was 4.66°. When analyzed separately, the mean absolute errors for the angles, using the nonlinear simulator and the test platform, showed differences below 2% in almost all evaluated experiments. The developed test platform preserves the rotational dynamics of the quadcopter as desired, closely approaching the results obtained by the nonlinear simulator. Consequently, this platform can be used to carry out practical tests in a controlled environment.

Posted in: Robotic software , Tagged: , ,