5,320 research outputs found

    Exploring the Synergy between two Modular Learning Techniques for Automated Planning

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    In the last decade the emphasis on improving the operational performance of domain independent automated planners has been in developing complex techniques which merge a range of different strategies. This quest for operational advantage, driven by the regular international planning competitions, has not made it easy to study, understand and predict what combinations of techniques will have what effect on a planner’s behaviour in a particular application domain. In this paper, we consider two machine learning techniques for planner performance improvement, and exploit a modular approach to their combination in order to facilitate the analysis of the impact of each individual component. We believe this can contribute to the development of more transparent planning engines, which are designed using modular, interchangeable, and well-founded components. Specifically, we combined two previously unrelated learning techniques, entanglements and relational decision trees, to guide a “vanilla” search algorithm. We report on a large experimental analysis which demonstrates the effectiveness of the approach in terms of performance improvements, resulting in a very competitive planning configuration despite the use of a more modular and transparent architecture. This gives insights on the strengths and weaknesses of the considered approaches, that will help their future exploitation

    Optimal dynamic operations scheduling for small-scale satellites

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    A satellite's operations schedule is crafted based on each subsystem/payload operational needs, while taking into account the available resources on-board. A number of operating modes are carefully designed, each one with a different operations plan that can serve emergency cases, reduced functionality cases, the nominal case, the end of mission case and so on. During the mission span, should any operations planning amendments arise, a new schedule needs to be manually developed and uplinked to the satellite during a communications' window. The current operations planning techniques over a reduced number of solutions while approaching operations scheduling in a rigid manner. Given the complexity of a satellite as a system as well as the numerous restrictions and uncertainties imposed by both environmental and technical parameters, optimising the operations scheduling in an automated fashion can over a flexible approach while enhancing the mission robustness. In this paper we present Opt-OS (Optimised Operations Scheduler), a tool loosely based on the Ant Colony System algorithm, which can solve the Dynamic Operations Scheduling Problem (DOSP). The DOSP is treated as a single-objective multiple constraint discrete optimisation problem, where the objective is to maximise the useful operation time per subsystem on-board while respecting a set of constraints such as the feasible operation timeslot per payload or maintaining the power consumption below a specific threshold. Given basic mission inputs such as the Keplerian elements of the satellite's orbit, its launch date as well as the individual subsystems' power consumption and useful operation periods, Opt-OS outputs the optimal ON/OFF state per subsystem per orbital time step, keeping each subsystem's useful operation time to a maximum while ensuring that constraints such as the power availability threshold are never violated. Opt-OS can provide the flexibility needed for designing an optimal operations schedule on the spot throughout any mission phase as well as the ability to automatically schedule operations in case of emergency. Furthermore, Opt-OS can be used in conjunction with multi-objective optimisation tools for performing full system optimisation. Based on the optimal operations schedule, subsystem design parameters are being optimised in order to achieve the maximal usage of the satellite while keeping its mass minimal

    Construction IT in 2030: a scenario planning approach

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    Summary: This paper presents a scenario planning effort carried out in order to identify the possible futures that construction industry and construction IT might face. The paper provides a review of previous research in the area and introduces the scenario planning approach. It then describes the adopted research methodology. The driving forces of change and main trends, issues and factors determined by focusing on factors related to society, technology, environment, economy and politics are discussed. Four future scenarios developed for the year 2030 are described. These scenarios start from the global view and present the images of the future world. They then focus on the construction industry and the ICT implications. Finally, the preferred scenario determined by the participants of a prospective workshop is presented

    On the Integration of Adaptive and Interactive Robotic Smart Spaces

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    © 2015 Mauro Dragone et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the user’s acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree – to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving users’ needs,but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.Peer reviewe

    Science and innovation for catchment management: report of scientific workshop May 2019

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    The India-UK Water Centre (IUKWC) promotes cooperation and collaboration between the complementary priorities of NERC-MoES water security research. This report represents an overview of the participation, activities and conclusions at a Scientific Workshop, held at Warwick Conferences at the University of Warwick, UK from 8th to 10th May, 2019. The workshop was convened by the India-UK Water Centre and led by Mr Ant Parsons (ALP Synergy Ltd) and Dr Kapil Gupta (IIT Bombay). The three-day workshop aimed to explore and build on existing knowledge and research to enhance collaboration and identify pathways to impact (including relevant NERC-MoES Science), identify gaps in research and innovation that are constraining sustainable catchment management, explore innovative approaches to monitoring and management, and consider the potential for SMART Rivers as part of integrated catchment management. The aims of the workshop were met by bringing together early career researchers, seasoned professors and experienced professionals from India and the UK, who covered a wide range of topics across the themes of climate, water quality, water quantity, and land and catchment management. The following report outlines some common challenges, cross-cutting themes and activities required for improving catchment management, potential solutions to catchment management and shares some of the ideas for new collaborative projects that were developed. The report is intended for the workshop participants, India-UK Water Centre members and stakeholders

    Anticipating plausible futures for innovative experimental ecosystems using foresight approach. Case: Design Factory

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    Change-makers are visionaries who wish to bring change in their respective fields. Design Factory at Aalto University, as an innovative experimental ecosystem with inter-disciplinary principles and new teaching methodologies has been successful and at the forefront in educating the students to be change-makers. The students learn skills, knowledge and are provided with a safe environment that guides them to become a change-maker in their respective fields such as social organizations, entrepreneurship, and careers in start-up or industry. Educating the students to be change-makers will evolve with future; the aim of the study is to holistically anticipate plausible futures for innovative experimental ecosystems utilizing foresight approach. The focus of the study is on Design Factory ways of working, spaces, and teaching methods which will support students in learning by year 20x6{x = 2, 3}. This study is about drawing virtual lines that connect the trends, future drivers, visions, and scenarios, using a contemporary approach that fuses qualitative and quantitative methods. The research on future trends and drivers were performed through semi structured interviews and environmental scanning. The drivers are evaluated through an online survey based on principles of the Delphi method. Further, the drivers are used to build mini scenarios which are further evaluated with the Design Factory stakeholders through a workshop. The results from the study are six future scenarios for the Aalto Design Factory. These results are expected to further foster or trigger new research and development experiments, directions on building radical environments, new teaching methods and ways of working

    Do muscle synergies reduce the dimensionality of behavior?

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    The muscle synergy hypothesis is an archetype of the notion of Dimensionality Reduction (DR) occurring in the central nervous system due to modular organisation. Towards validating this hypothesis, it is however important to understand if muscle synergies can reduce the state-space dimensionality while suitably achieving task control. In this paper we present a scheme for investigating this reduction, utilising the temporal muscle synergy formulation. Our approach is based on the observation that constraining the control input to a weighted combination of temporal muscle synergies instead constrains the dynamic behaviour of a system in trajectory-specific manner. We compute this constrained reformulation of system dynamics and then use the method of system balancing for quantifying the DR; we term this approach as Trajectory Specific Dimensionality Analysis (TSDA). We then use this method to investigate the consequence of minimisation of this dimensionality for a given task. These methods are tested in simulation on a linear (tethered mass) and a nonlinear (compliant kinematic chain) system; dimensionality of various reaching trajectories is compared when using idealised temporal synergies. We show that as a consequence of this Minimum Dimensional Control (MDC) model, smooth straight-line Cartesian trajectories with bell-shaped velocity profiles are obtained as the solution to reaching tasks in both of the test systems. We also investigate the effect on dimensionality due to adding via-points to a trajectory. The results indicate that a synergy basis and trajectory-specific DR of motor behaviours results from usage of muscle synergy control. The implications of these results for the synergy hypothesis, optimal motor control, developmental skill acquisition and robotics are then discussed

    A quantitative taxonomy of human hand grasps

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    Background: A proper modeling of human grasping and of hand movements is fundamental for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively justified. Methods: This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40 healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic) taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific taxonomies provide similar results despite describing different parameters of hand movements, one being muscular and the other kinematic. Results: The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements can be divided into five movement categories defined based on the overall grasp shape, finger positioning and muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic hand grasping synergies. Conclusions: The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields

    Futures of shipbuilding in the 22nd century : Explorative industry foresight research of the long-range futures for commercial ship-building, using elements of OpenAI.

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    The shipbuilding industry has historically shaped global trade, logistics, research, and cultural globalization. It was instrumental in exploring and colonizing new continents, thereby significantly shaping our society. Today, it's essential to consider the industry's current transformations and speculate on what shipbuilding might look like in the 22nd century. This study is dedicated to exploring the possible futures of shipbuilding over a long-range time horizon of 70 -100 years. This thesis applied futures research methods to data collected using OpenAI tools and explored possible transformative pathways within the industry. The research offers potential future scenarios and delineates change pathways from external pressures and internal shifts within the shipbuilding system. Additionally, the study highlights the possible applications and implications of utilizing OpenAI technology in a research context. The analysis of shipbuilding incorporates the Multi-Level Perspective (MLP) concept, viewing the industry as a system involving ten groups of key actors. This structure guided the data collection process for the input of the research. The primary research process adheres to traditional futures research methods, which include horizon scanning, systems thinking, scenario building, and causal layered analysis (CLA). Furthermore, the methodology was expanded to incorporate AI-assisted techniques. This includes using AI technology for automated data collection and a separate pathway using ChatGPT-4 for computer-generated scenarios and CLA narratives development. The outcomes from both methodologies are compared, and additional literature research about the applicability and implications of using AI in futures studies. The research has identified critical external drivers of change, originating from fields such as technology, energy, and social development, as well as internal drivers, including biotechnology and diversifying floating structures. The external drivers could influence the future direction of shipbuilding, while the internal factors represent potential changes originating from within the industry. The constructed scenarios are designed to stimulate discussion and provide context for future developmental trajectories of shipbuilding
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