281 research outputs found
Safe Multi-Agent Reinforcement Learning with Quantitatively Verified Constraints
Multi-agent reinforcement learning is a machine learning technique that involves
multiple agents attempting to solve sequential decision-making problems. This learn-
ing is driven by objectives and failures modelled as positive numerical rewards and
negative numerical punishments, respectively. These multi-agent systems explore
shared environments in order to find the highest cumulative reward for the sequential
decision-making problem. Multi-agent reinforcement learning within autonomous
systems has become a prominent research area with many examples of success and
potential applications. However, the safety-critical nature of many of these potential
applications is currently underexplored—and under-supported. Reinforcement learn-
ing, being a stochastic process, is unpredictable, meaning there are no assurances that
these systems will not harm themselves, other expensive equipment, or humans. This
thesis introduces Assured Multi-Agent Reinforcement Learning (AMARL) to mitigate
these issues. This approach constrains the actions of learning systems during and
after a learning process. Unlike previous multi-agent reinforcement learning methods,
AMARL synthesises constraints through the formal verification of abstracted multi-
agent Markov decision processes that model the environment’s functional and safety
aspects. Learned policies guided by these constraints are guaranteed to satisfy strict
functional and safety requirements and are Pareto-optimal with respect to a set of op-
timisation objectives. Two AMARL extensions are also introduced in the thesis. Firstly,
the thesis presents a Partial Policy Reuse method that allows the use of previously
learned knowledge to reduce AMARL learning time significantly when initial models
are inaccurate. Secondly, an Adaptive Constraints method is introduced to enable
agents to adapt to environmental changes by constraining their learning through a
procedure that follows the styling of monitoring, analysis, planning, and execution
during runtime. AMARL and its extensions are evaluated within three case studies
from different navigation-based domains and shown to produce policies that meet
strict safety and functional requirements
Undergraduate and Graduate Course Descriptions, 2023 Spring
Wright State University undergraduate and graduate course descriptions from Spring 2023
Catalog | 2022-2023
(2022-2023). In its early years as the State Normal School, JSU produced a variety of publications (announcements, bulletins, and catalogs) that contain course information combined with the types of information that would later be found in yearbooks. Examples include historical information about the school, lists of enrolled students and club officers, photographs of athletic teams and literary clubs, notes on alumni, faculty and campus facilities, and more.https://digitalcommons.jsu.edu/lib_ac_bul_bulletin/1221/thumbnail.jp
Multi-Robot Coverage Path Planning for Inspection of Offshore Wind Farms: A Review
Offshore wind turbine (OWT) inspection research is receiving increasing interest as the sector grows worldwide. Wind farms are far from emergency services and experience extreme weather and winds. This hazardous environment lends itself to unmanned approaches, reducing human exposure to risk. Increasing automation in inspections can reduce human effort and financial costs. Despite the benefits, research on automating inspection is sparse. This work proposes that OWT inspection can be described as a multi-robot coverage path planning problem. Reviews of multi-robot coverage exist, but to the best of our knowledge, none captures the domain-specific aspects of an OWT inspection. In this paper, we present a review on the current state of the art of multi-robot coverage to identify gaps in research relating to coverage for OWT inspection. To perform a qualitative study, the PICo (population, intervention, and context) framework was used. The retrieved works are analysed according to three aspects of coverage approaches: environmental modelling, decision making, and coordination. Based on the reviewed studies and the conducted analysis, candidate approaches are proposed for the structural coverage of an OWT. Future research should involve the adaptation of voxel-based ray-tracing pose generation to UAVs and exploration, applying semantic labels to tasks to facilitate heterogeneous coverage and semantic online task decomposition to identify the coverage target during the run time.</jats:p
Advances in Artificial Intelligence: Models, Optimization, and Machine Learning
The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications
Collected Papers (on Neutrosophics, Plithogenics, Hypersoft Set, Hypergraphs, and other topics), Volume X
This tenth volume of Collected Papers includes 86 papers in English and Spanish languages comprising 972 pages, written between 2014-2022 by the author alone or in collaboration with the following 105 co-authors (alphabetically ordered) from 26 countries: Abu Sufian, Ali Hassan, Ali Safaa Sadiq, Anirudha Ghosh, Assia Bakali, Atiqe Ur Rahman, Laura Bogdan, Willem K.M. Brauers, Erick González Caballero, Fausto Cavallaro, Gavrilă Calefariu, T. Chalapathi, Victor Christianto, Mihaela Colhon, Sergiu Boris Cononovici, Mamoni Dhar, Irfan Deli, Rebeca Escobar-Jara, Alexandru Gal, N. Gandotra, Sudipta Gayen, Vassilis C. Gerogiannis, Noel Batista Hernández, Hongnian Yu, Hongbo Wang, Mihaiela Iliescu, F. Nirmala Irudayam, Sripati Jha, Darjan Karabašević, T. Katican, Bakhtawar Ali Khan, Hina Khan, Volodymyr Krasnoholovets, R. Kiran Kumar, Manoranjan Kumar Singh, Ranjan Kumar, M. Lathamaheswari, Yasar Mahmood, Nivetha Martin, Adrian Mărgean, Octavian Melinte, Mingcong Deng, Marcel Migdalovici, Monika Moga, Sana Moin, Mohamed Abdel-Basset, Mohamed Elhoseny, Rehab Mohamed, Mohamed Talea, Kalyan Mondal, Muhammad Aslam, Muhammad Aslam Malik, Muhammad Ihsan, Muhammad Naveed Jafar, Muhammad Rayees Ahmad, Muhammad Saeed, Muhammad Saqlain, Muhammad Shabir, Mujahid Abbas, Mumtaz Ali, Radu I. Munteanu, Ghulam Murtaza, Munazza Naz, Tahsin Oner, Gabrijela Popović, Surapati Pramanik, R. Priya, S.P. Priyadharshini, Midha Qayyum, Quang-Thinh Bui, Shazia Rana, Akbara Rezaei, Jesús Estupiñán Ricardo, Rıdvan Sahin, Saeeda Mirvakili, Said Broumi, A. A. Salama, Flavius Aurelian Sârbu, Ganeshsree Selvachandran, Javid Shabbir, Shio Gai Quek, Son Hoang Le, Florentin Smarandache, Dragiša Stanujkić, S. Sudha, Taha Yasin Ozturk, Zaigham Tahir, The Houw Iong, Ayse Topal, Alptekin Ulutaș, Maikel Yelandi Leyva Vázquez, Rizha Vitania, Luige Vlădăreanu, Victor Vlădăreanu, Ștefan Vlăduțescu, J. Vimala, Dan Valeriu Voinea, Adem Yolcu, Yongfei Feng, Abd El-Nasser H. Zaied, Edmundas Kazimieras Zavadskas.
Great team play: A study of computational trust in a team of agents
Teams have arguably been the most essential organisational form in human society. During the pursuit of better team performance, several factors have attracted consistent attentions; among which, trust has been widely recognised as especially important. Trust directly impacts team performance as it plays a crucial and pivotal role in the decisions that each team member (each agent) makes with regard to its own actions and its interactions with fellow team members (other agents). However, there is no systematic and generic modelling of trust aiming at improving team performance by enabling each agent in the team to conduct trust-based interactions through accurate trust evaluation and prescribing the appropriate actions in response to that trust evaluation.
This thesis addresses this absence of such a framework in three steps. Firstly, objective interaction records (past one-to-one interactions between pairs of agents) are considered the most reliable source of information pertaining to trustworthiness. Thus, to obtain accurate trust evaluations in a team of agents, a Determination of Trust Model (DoTM) is proposed. This DoTM models trust by establishing the relationship between interaction data and the trust values through a machine learner. Secondly, a Novel Trust Architecture (NoTA) is proposed to address the appropriate transition from trust to practical actions for an agent conducting an interactive team task. A team task may involve multiple sub-tasks and different types of interactions, so determining the appropriate interactions according to trust is challenging as it requires the differentiation of the trust (of the agent being interacted with's capacity) in performing different sub-tasks and the association between trust and designated interactions. The NoTA is a nuanced framework determining an agent's appropriate actions during the task through the application of differentiated trust and matched response strategies. Finally, an integrated trust model is proposed on the basis of the DoTM and the NoTA, which enables an agent the full autonomy of trust-based interactions.
Experimental demonstration is conducted in two selected domains, i.e. a food foraging task (FFT) and a coverage task. In each domain, agents are differentiated as reliable agents and flawed agents in terms of their trustworthiness; with the assumption that each scenario only involve one type of flawed agent, experiments are conducted in scenarios possessing agents with different types of flaws and ratios between flawed and reliable agents within a team. The DoTM shows an average evaluation accuracy (ACC) of at least 94% in the FFT domain, and 80% for 14 types of flawed agents (out of 15) in the coverage task. It is also shown that with a priori knowledge about trust, by applying the NoTA, average team performance is improved by at least 5% (and up to 331%) for 7 types of flawed agents (out of 12) in the FFT domain compared with the baseline team performance. In the coverage task, the improvement is at least 17% (with a maximum of 431%) for 7 types of flawed agents (out of 15). By applying the integrated trust model, when agents possess no a priori knowledge about each other's trustworthiness, it is shown that in the FFT domain, with regard to 5 of the types of flaws (out of 12), average team performance is improved by at least 6%. In the coverage domain, in presence of 7 types of flaws (out of 15), team performance is improved by at least 13%.
Based on experimental investigations, it can be concluded that (1) the proposed DoTM enables high trust evaluation accuracy in a team with agents possessing different trustworthiness; (2) the proposed NoTA is capable of obtaining the optimal strategy which determines the appropriate actions during interactive teamwork according to trust for optimised team performance; (3) without a priori knowledge about trust, the proposed integrated trust model facilitates improvement to team performance in various scenarios with agents having different trustworthiness. The proposed computational trust models can be applied to or used as an important tool to improve the performance of a team of intelligent agents conducting a variety of cooperative/collaborative tasks
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Reliable Decision-Making with Imprecise Models
The rapid growth in the deployment of autonomous systems across various sectors has generated considerable interest in how these systems can operate reliably in large, stochastic, and unstructured environments. Despite recent advances in artificial intelligence and machine learning, it is challenging to assure that autonomous systems will operate reliably in the open world. One of the causes of unreliable behavior is the impreciseness of the model used for decision-making. Due to the practical challenges in data collection and precise model specification, autonomous systems often operate based on models that do not represent all the details in the environment. Even if the system has access to a comprehensive decision-making model that accounts for all the details in the environment and all possible scenarios the agent may encounter, it may be intractable to solve this complex model optimally. Consequently, this complex, high fidelity model may be simplified to accelerate planning, introducing imprecision. Reasoning with such imprecise models affects the reliability of autonomous systems. A system\u27s actions may sometimes produce unexpected, undesirable consequences, which are often identified after deployment. How can we design autonomous systems that can operate reliably in the presence of uncertainty and model imprecision?
This dissertation presents solutions to address three classes of model imprecision in a Markov decision process, along with an analysis of the conditions under which bounded-performance can be guaranteed. First, an adaptive outcome selection approach is introduced to devise risk-aware reduced models of the environment that efficiently balance the trade-off between model simplicity and fidelity, to accelerate planning in resource-constrained settings. Second, a framework that extends stochastic shortest path framework to problems with imperfect information about the goal state during planning is introduced, along with two solution approaches to solve this problem. Finally, two complementary solution approaches are presented to minimize the negative side effects of agent actions. The techniques presented in this dissertation enable an autonomous system to detect and mitigate undesirable behavior, without redesigning the model entirely
Undergraduate and Graduate Course Descriptions, 2022 Fall
Wright State University undergraduate and graduate course descriptions from Fall 2022
Floating (on) platforms? European Left parties and the digital revolution. A Gramscian analysis
This thesis examines how European Left Parties reacted to the digital revolution in the 2010s, a decade that recent literature theorised as one of ‘crisis’ for the Left. Meanwhile, critical theorists claimed that platform societies provide new routes for transformative Left-wing politics. However, there is a lack of research on how Left Parties tapped into these dynamics. This gap set the rationale for developing a Gramscian framework through which I explored the core attributes of the hegemony in platform societies. On this ground, I conducted empirical research on how six left-wing parties in Italy, France and Spain sought to navigate or transform ‘digital’ hegemony. By looking at how parties conceived platform capitalism and platform politics, I theorised the emergence of three left-wing ‘digital’ ideologies: the neoliberal Techno-Third Way, Post-Social Democracy, and Platform Socialism. I analysed parties’ official discourses and original evidence from 37 elites’ interviews to advance understandings of how Left parties ‘fit’ into the confrontations for hegemony in platform societies. The empirical findings develop the thesis’s central argument, namely that the politics of the digital revolution provided Left parties with potential essential resources to exit their ideological crises, but in opposite directions that (re-)polarised the Left. Indeed, while parties embracing Techno-Third Way could elaborate ideas and strategies to organically represent the ruling classes of platform capitalism, Platform Socialists found new ways to empower resistance around the field of the ‘digital commons’. Conversely, the thesis argues that Post-Social Democracy demonstrates the ongoing crisis of the ‘arbitrary’ attempts to revive Social Democracy under impossible structural conditions
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