1,721,258 research outputs found
Flexible Service Provisioning in Multi-Agent Systems
No full textService-oriented computing is an increasingly popular approach for providing applications, computational resources and business services over highly distributed and open systems (such as the Web, computational Grids and peer-to-peer systems). In this approach, service providers advertise their offerings by means of standardised computer-readable descriptions, which can then be used by software applications to discover and consume appropriate services without human intervention. However, despite active research in service infrastructures, and in service discovery and composition mechanisms, little work has recognised that services are offered by inherently autonomous and self-interested entities. This autonomy implies that providers may choose not to honour every service request, demand remuneration for their efforts, and, in general, exhibit uncertain behaviour. This uncertainty is especially problematic for the service consumers when services are part of complex workflows, as is common in many application domains, such as bioinformatics, large-scale data analysis and processing, and commercial supply-chain management. In order to address this uncertainty, we propose a novel algorithm for provisioning services for complex workflows (i.e., for selecting suitable services for the constituent tasks of a workflow). This algorithm uses probabilistic performance information about providers to reason about service uncertainty and its impact on the overall workflow. Furthermore, our approach actively mitigates this uncertainty by employing two key techniques. First, it proactively provisions redundant services for particularly critical or failure-prone tasks (thus increasing the probability of success). Second, it recovers dynamically from service failures by re-provisioning services at run-time (without necessarily receiving explicit failure messages). Unlike existing work in this area, our algorithm employs principled decision-theoretic techniques to determine which services to provision, whether to introduce redundant services and when to re-provision failed services. In doing so, it explicitly balances the cost of provisioning with the expected value of the workflow. To show how our algorithm applies to a range of common service-oriented systems, we consider a variety of different scenarios in this thesis. More specifically, we first examine environments where the consumer lacks specific knowledge to differentiate between distinct service providers, as is common in highly dynamic and open systems. Despite this lack of detailed knowledge, we demonstrate how the consumer can use redundancy and dynamic re-provisioning to influence the outcome of a workflow and to deal with uncertainty. Then, we look into systems where the consumer has more specific knowledge about highly heterogeneous providers. While existing work has concentrated on selecting the single best provider for each workflow task, we show that a consumer can often improve its performance by provisioning multiple providers with different qualities for a single task. Finally, we discuss how our algorithm can be adapted for systems where consumers and providers reach explicit service contracts in advance. In this context, we are the first to propose a gradual provisioning approach, whereby the consumer negotiates contracts for some tasks in advance, but leaves the negotiation of others to a later time. This approach allows the consumer to better react to uncertain service outcomes and to avoid paying reservation fees that are later lost when services fail. Throughout this thesis, we compare our approach empirically to current provisioning algorithms. In doing so, we demonstrate that our approach typically achieves a significantly higher utility for the service consumer than approaches that do not reason about uncertainty, that rely on fixed levels of redundancy or service time-outs, and approaches that select single services to achieve the optimal balance of various performance characteristics. Furthermore, we show that these results hold over a large range of environments and workflow types and that our algorithm copes well even in highly uncertain environments where most services fail. As our approach relies on fast heuristics to solve a problem that is known to be intractable, it scales well to larger workflows with hundreds of tasks and thousands of providers. Finally, where it is tractable to compute an optimal solution, we show empirically that our algorithm achieves a high utility that is within 87% or more of the optimal
Citizen-centric multiagent systems
No full textAdvances in multiagent systems (MAS) have the potential to solve critical societal challenges. For example, MAS techniques for efficient resource allocation can help us implement cleaner and more efficient forms of on-demand mobility; social choice methods can support us in deciding how to trade off energy use and comfort in smart buildings; and task coordination methods can be used to respond to disasters in an effective and resilient manner. However, the benefits of these approaches can only be realised if citizen end users are able to trust these emerging multiagent systems. To achieve this, a citizen-centric approach needs to be taken. This places citizens at the heart of the design, development and deployment of trustworthy multiagent systems. We present open research challenges in this area, put forward key application domains for citizen-centric MAS (C-MAS) and discuss collaborative research opportunities
Electric Vehicle Charging on Long Journeys: Current Challenges and Future Opportunities
Full text linkRecommended from our members
[Book Review] Boldyrev, Ivan and Stein, Sebastian (eds.). Interpreting Hegel’s Phenomenology of Spirit: Expositions and Critique of Contemporary Readings. New York / Abingdon: Routledge 2022, ix + 277 pp.
No full textBoldyrev Ivan Stein Sebastian (eds.) Interpreting Hegel’s Phenomenology of Spirit: Expositions and Critique of Contemporary Readings New York / Abingdon Routledge 2022 1 277</p
Competitive influence maximisation with nonlinear cost of allocations
No full textWe explore the competitive influence maximisation problem in the voter model. We extend past work by modelling real-world settings where the strength of influence changes nonlinearly with external allocations to the network. We use this approach to identify two distinct regimes — one where optimal intervention strategies offer significant gain in outcomes, and the other where they yield no gains. The two regimes also vary in their sensitivity to budget availability, and we find that in some cases, even a tenfold increase in the budget only marginally improves the outcome of an intervention in a population
Real-time opinion aggregation methods for crowd robotics
No full textUnmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenario
Client-master multiagent deep reinforcement learning for task offloading in mobile edge computing
No full textAs mobile applications grow in complexity, there is an increasing need to perform computationally intensive tasks. However, user devices (UDs), such as tablets and smartphones, have limited capacity to carry out the required computations. Task offloading in mobile edge computing (MEC) is a strategy that meets this demand by distributing tasks between UDs and servers. Deep reinforcement learning (DRL) is a promising solution for this strategy because it can adapt to dynamic changes and minimize online computational complexity. However, various types of continuous and discrete resource constraints on UDs and MEC servers pose challenges to the design of an efficient DRL algorithm. Existing DRL-based task-offloading algorithms focus on the constraints of the UDs, assuming the availability of enough resources on the server. Moreover, existing Multiagent DRL (MADRL)-based task-offloading algorithms are homogeneous agents and consider homogeneous constraints as a penalty in their reward function. We propose a novel Client-Master MADRL (CMMADRL) algorithm for task offloading in MEC that uses client agents at the UDs to decide on their resource requirements and a master agent at the server to make a combinatorial action selection based on the decision of the UDs. CMMADRL is shown to achieve up to 59% improvement in performance over existing benchmark and heuristic algorithms
Adaptive microtolling in competitive online congestion games via multiagent reinforcement learning
No full textEfficient urban traffic management remains a critical challenge, yet traditional congestion games fail to capture the dynamic and competitive nature of real-world transportation systems. We introduce the Multi-Market Routing Problem (MMRP), an online and oligopolistic extension that models competition amongst route providers utilising adaptive microtolling strategies to influence driver behaviour and mitigate congestion. We formally define the MMRP, highlighting the computational complexity of solving the MMRP, and use an adapted version of Proximal Policy Optimisation (PPO) to improve update stability in multiagent environments to address this problem in online settings. Our empirical analysis demonstrates that our PPO-based approach not only matches the performance of existing benchmarks but also significantly enhances equity, reduces travel times for users, and increases profitability for providers
A personalised thermal comfort model using a Bayesian network
No full textIn this paper, we address the challenge of predicting optimal comfort temperatures of individual users of a smart heating system. At present, such systems use simple models of user comfort when deciding on a set point temperature. These models generally fail to adapt to an individual user’s preferences, resulting in poor estimates of a user’s preferred temperature. To address this issue, we propose a personalised thermal comfort model that uses a Bayesian network to learn and adapt to a user’s individual preferences. Through an empirical evaluation based on the ASHRAE RP-884 data-set, we show that our model is consistently 17.5-23.5% more accurate than current models, regardless of environmental conditions and the type of heating system used. Our model is not limited to a single metric but can also infer information about expected user feedback, optimal comfort temperature and thermal sensitivity at the same time, which can be used to reduce energy used for heating with minimal comfort loss
CrowdAR: augmenting live video with a real-time crowd
No full textFinding and tracking targets and events in a live video feed is important formany commercial applications, from CCTV surveillance used by police and securityfirms, to the rapid mapping of events from aerial imagery.However, descriptions of targets are typically provided in natural language bythe end users, and interpreting these in the context of a live video stream is acomplex task. Due to current limitations in artificial intelligence, especiallyvision, this task cannot be automated and instead requires human supervision.Hence, in this paper, we consider the use of real-time crowdsourcing to identifyand track targets given by a natural language description. In particular wepresent a novel method for augmenting live video with a real-time crowd
- …
