12,159 research outputs found

    Optimal Cost-Preference Trade-off Planning with Multiple Temporal Tasks

    Full text link
    Autonomous robots are increasingly utilized in realistic scenarios with multiple complex tasks. In these scenarios, there may be a preferred way of completing all of the given tasks, but it is often in conflict with optimal execution. Recent work studies preference-based planning, however, they have yet to extend the notion of preference to the behavior of the robot with respect to each task. In this work, we introduce a novel notion of preference that provides a generalized framework to express preferences over individual tasks as well as their relations. Then, we perform an optimal trade-off (Pareto) analysis between behaviors that adhere to the user's preference and the ones that are resource optimal. We introduce an efficient planning framework that generates Pareto-optimal plans given user's preference by extending A* search. Further, we show a method of computing the entire Pareto front (the set of all optimal trade-offs) via an adaptation of a multi-objective A* algorithm. We also present a problem-agnostic search heuristic to enable scalability. We illustrate the power of the framework on both mobile robots and manipulators. Our benchmarks show the effectiveness of the heuristic with up to 2-orders of magnitude speedup.Comment: 8 pages, 4 figures, to appear in International Conference on Intelligent Robots and Systems (IROS) 202

    The Globalization of Artificial Intelligence: African Imaginaries of Technoscientific Futures

    Get PDF
    Imaginaries of artificial intelligence (AI) have transcended geographies of the Global North and become increasingly entangled with narratives of economic growth, progress, and modernity in Africa. This raises several issues such as the entanglement of AI with global technoscientific capitalism and its impact on the dissemination of AI in Africa. The lack of African perspectives on the development of AI exacerbates concerns of raciality and inclusion in the scientific research, circulation, and adoption of AI. My argument in this dissertation is that innovation in AI, in both its sociotechnical imaginaries and political economies, excludes marginalized countries, nations and communities in ways that not only bar their participation in the reception of AI, but also as being part and parcel of its creation. Underpinned by decolonial thinking, and perspectives from science and technology studies and African studies, this dissertation looks at how AI is reconfiguring the debate about development and modernization in Africa and the implications for local sociotechnical practices of AI innovation and governance. I examined AI in international development and industry across Kenya, Ghana, and Nigeria, by tracing Canada’s AI4D Africa program and following AI start-ups at AfriLabs. I used multi-sited case studies and discourse analysis to examine the data collected from interviews, participant observations, and documents. In the empirical chapters, I first examine how local actors understand the notion of decolonizing AI and show that it has become a sociotechnical imaginary. I then investigate the political economy of AI in Africa and argue that despite Western efforts to integrate the African AI ecosystem globally, the AI epistemic communities in the continent continue to be excluded from dominant AI innovation spaces. Finally, I examine the emergence of a Pan-African AI imaginary and argue that AI governance can be understood as a state-building experiment in post-colonial Africa. The main issue at stake is that the lack of African perspectives in AI leads to negative impacts on innovation and limits the fair distribution of the benefits of AI across nations, countries, and communities, while at the same time excludes globally marginalized epistemic communities from the imagination and creation of AI

    A Low-Delay MAC for IoT Applications: Decentralized Optimal Scheduling of Queues without Explicit State Information Sharing

    Full text link
    We consider a system of several collocated nodes sharing a time slotted wireless channel, and seek a MAC (medium access control) that (i) provides low mean delay, (ii) has distributed control (i.e., there is no central scheduler), and (iii) does not require explicit exchange of state information or control signals. The design of such MAC protocols must keep in mind the need for contention access at light traffic, and scheduled access in heavy traffic, leading to the long-standing interest in hybrid, adaptive MACs. Working in the discrete time setting, for the distributed MAC design, we consider a practical information structure where each node has local information and some common information obtained from overhearing. In this setting, "ZMAC" is an existing protocol that is hybrid and adaptive. We approach the problem via two steps (1) We show that it is sufficient for the policy to be "greedy" and "exhaustive". Limiting the policy to this class reduces the problem to obtaining a queue switching policy at queue emptiness instants. (2) Formulating the delay optimal scheduling as a POMDP (partially observed Markov decision process), we show that the optimal switching rule is Stochastic Largest Queue (SLQ). Using this theory as the basis, we then develop a practical distributed scheduler, QZMAC, which is also tunable. We implement QZMAC on standard off-the-shelf TelosB motes and also use simulations to compare QZMAC with the full-knowledge centralized scheduler, and with ZMAC. We use our implementation to study the impact of false detection while overhearing the common information, and the efficiency of QZMAC. Our simulation results show that the mean delay with QZMAC is close that of the full-knowledge centralized scheduler.Comment: 28 pages, 19 figure

    Learning and decentralized control in linear switched systems

    No full text
    Switched systems are an important and widely studied area of control theory since their applications can be encountered in many different circumstances. Examples of such systems include robotics, power grids, networked systems, and macroeconomic models. In this dissertation, we focus on the controller synthesis problem for interconnected systems and the application to physical systems. While there are many existing control theory tools to optimize the performance of systems with a centralized structure, we are interested in developing tools that take into consideration the topology of interconnected systems to generate decentralized controllers. In addition, most of the existing literature results consider the controller synthesis problem when the user has full knowledge of the system. With this in mind, we also look into machine learning tools as a way to simplify the synthesis problem. In the first part of the thesis, we develop synthesis methods for decentralized control of switched systems with mode-dependent (more generally, path-dependent) performance specifications. This specification flexibility is important when achievable system performance varies greatly between modes, as a mode-independent specification will lead to designs that do not extract all the system performance available in each mode. More specifically, under these specifications, we derive exact conditions for existence of block lower triangular path-dependent controllers with â„“2\ell_2-induced norm performance. The synthesis conditions are given in the form of a semidefinite program (SDP) for both uniform and path-dependent performance bounds. Since the given synthesis conditions might become computationally intractable for large-scale systems, we also introduce a basis-based approach that allows computational complexity to be more carefully controlled. We also applied the resulting design methods to a group of miniature quadcopters, illustrating different features of the decentralized control approach, along with some practical engineering considerations. In the second part of this dissertation, we turn to policy gradient approaches with the goal of starting from some sub-optimal controller and using measurement data to optimize the control gains. Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning. More specifically, there have been studies on the convergence properties of policy gradient methods to learn the optimal quadratic control of linear time-invariant systems. With our overall goal of controlling switched systems, we take the first step of investigating the global convergence of gradient-based policy optimization methods for quadratic optimal control of discrete-time Markovian jump linear systems (MJLS), which are a special case of switched systems. Despite the non-convexity of the resultant problem, we are still able to identify several useful properties such as coercivity, gradient dominance, and almost smoothness. Based on these properties, we show global convergence guarantees of three types of policy optimization methods: the gradient descent method; the Gauss-Newton method; and the natural policy gradient method. We prove that all three methods converge to the optimal state feedback controller for MJLS at a linear rate if initialized at a controller which is mean-square stabilizing. Lastly, we study model-free (data-driven) approaches to the MJLS quadratic control problem. Our simulation results suggest that the data-driven versions of the three studied iterative methods can efficiently learn the optimal controller for MJLS with unknown dynamics. This work brings new insights for understanding the performance of policy gradient methods on the Markovian jump linear quadratic control problem.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

    Identity, Power, and Prestige in Switzerland's Multilingual Education

    Get PDF
    Switzerland is known for its multilingualism, yet not all languages are represented equally in society. The situation is exacerbated by the influx of heritage languages and English through migration and globalization processes which challenge the traditional education system. This study is the first to investigate how schools in Grisons, Fribourg, and Zurich negotiate neoliberal forces leading to a growing necessity of English, a romanticized view on national languages, and the social justice perspective of institutionalizing heritage languages. It uncovers power and legitimacy issues and showcases students' and teachers' complex identities to advocate equitable multilingual education

    Wastewater and sludge valorisation: a novel approach for treatment and resource recovery to achieve circular economy concept

    Get PDF
    Global demand for freshwater is rapidly escalating. It is highly essential to keep pace with the necessities of the increasing population. The effluents of wastewater are gradually identified as a reservoir of resources for energy generation and economic boom. Henceforth, most wastewater and sludge have great potential for reuse and recycling. The re-utilization and valorization of wastewater and sludge contribute to accomplishing sustainable development goals, combating water scarcity, and alleviating adverse environmental impacts of wastewater on the environmental components. The present article highlights the most novel approaches for wastewater treatment for the waste valorization of different industrial origins and the generation of value-added products and recovery of biopolymers, vitamins, enzymes, dyes, pigments, and phenolic compounds. We highlighted the life cycle assessment and techno-economic analysis. In addition, we have addressed a critical overview of the barriers to the large-scale application of resource recovery strategies and economic, environmental, and social concerns associated with using waste-derived products

    Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks

    Full text link
    We develop a novel graph-based trainable framework to maximize the weighted sum energy efficiency (WSEE) for power allocation in wireless communication networks. To address the non-convex nature of the problem, the proposed method consists of modular structures inspired by a classical iterative suboptimal approach and enhanced with learnable components. More precisely, we propose a deep unfolding of the successive concave approximation (SCA) method. In our unfolded SCA (USCA) framework, the originally preset parameters are now learnable via graph convolutional neural networks (GCNs) that directly exploit multi-user channel state information as the underlying graph adjacency matrix. We show the permutation equivariance of the proposed architecture, which is a desirable property for models applied to wireless network data. The USCA framework is trained through a stochastic gradient descent approach using a progressive training strategy. The unsupervised loss is carefully devised to feature the monotonic property of the objective under maximum power constraints. Comprehensive numerical results demonstrate its generalizability across different network topologies of varying size, density, and channel distribution. Thorough comparisons illustrate the improved performance and robustness of USCA over state-of-the-art benchmarks.Comment: Published in IEEE Transactions on Wireless Communication

    Circular supply chain management: a bibliometric analysis-based literature review

    Get PDF
    Purpose Supply chain management (SCM) research has contributed to the transition to a circular economy (CE). Still, confusions exist on the related terms, and no review has mapped out the development trends in the domain. This research clarifies the boundaries of the relevant concepts. Then, it conducts a comprehensive review of the circular SCM (CSCM) literature and identifies opportunities for future research. Design/methodology/approach Using relevant keywords, 1,130 journal articles published in December 31, 2021 were identified. Unlike the published reviews, which mainly relied on content analysis, this review uses bibliometric analysis tools, including citation analysis, co-citation analysis and cluster analysis. The review identifies general trends, influential researchers, high-impact publications, citation patterns and established and emergent research themes. Findings The extant CSCM literature includes five prominent clusters: (1) reverse channel optimization; (2) CSCM review and empirical studies; (3) closed-loop supply chain (CLSC) and consumers; (4) CLSC and inventory management and (5) CLSC and reverse logistics (RL). Significant research gaps exist in the use of secondary and longitudinal data, a wider range of theories, mixed-methods, multi-method, action research and behavioral experiment. The least researched topics include zero waste, industrial symbiosis, circular product design, sourcing and supply management and reuse. Originality/value This is the first bibliometric analysis-based literature review on CSCM. It clarifies the interrelated supply chain sustainability terms and thus reduces related confusion. It offers insights into the patterns in the CSCM literature and suggests important research directions
    • …
    corecore