34 research outputs found

    Current and Future Challenges in Knowledge Representation and Reasoning

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    Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

    Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives

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    Collectiveness is an important property of many systems--both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals, or even to produce intelligent collective behaviour out of not-so-intelligent individuals. Indeed, collective intelligence, namely the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems--motivated by recent techno-scientific trends like the Internet of Things, swarm robotics, and crowd computing, just to name a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognised research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this paper considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for publication in the Artificial Life journal. Data: 34 pages, 2 figure

    Sensing the Cultural Significance with AI for Social Inclusion

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    Social Inclusion has been growing as a goal in heritage management. Whereas the 2011 UNESCO Recommendation on the Historic Urban Landscape (HUL) called for tools of knowledge documentation, social media already functions as a platform for online communities to actively involve themselves in heritage-related discussions. Such discussions happen both in “baseline scenarios” when people calmly share their experiences about the cities they live in or travel to, and in “activated scenarios” when radical events trigger their emotions. To organize, process, and analyse the massive unstructured multi-modal (mainly images and texts) user-generated data from social media efficiently and systematically, Artificial Intelligence (AI) is shown to be indispensable. This thesis explores the use of AI in a methodological framework to include the contribution of a larger and more diverse group of participants with user-generated data. It is an interdisciplinary study integrating methods and knowledge from heritage studies, computer science, social sciences, network science, and spatial analysis. AI models were applied, nurtured, and tested, helping to analyse the massive information content to derive the knowledge of cultural significance perceived by online communities. The framework was tested in case study cities including Venice, Paris, Suzhou, Amsterdam, and Rome for the baseline and/or activated scenarios. The AI-based methodological framework proposed in this thesis is shown to be able to collect information in cities and map the knowledge of the communities about cultural significance, fulfilling the expectation and requirement of HUL, useful and informative for future socially inclusive heritage management processes

    Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

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    The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

    Towards Flexible and Cognitive Production—Addressing the Production Challenges

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    Globalization in the field of industry is fostering the need for cognitive production systems. To implement modern concepts that enable tools and systems for such a cognitive production system, several challenges on the shop floor level must first be resolved. This paper discusses the implementation of selected cognitive technologies on a real industrial case-study of a construction machine manufacturer. The partner company works on the concept of mass customization but utilizes manual labour for the high-variety assembly stations or lines. Sensing and guidance devices are used to provide information to the worker and also retrieve and monitor the working, with respecting data privacy policies. Next, a specified process of data contextualization, visual analytics, and causal discovery is used to extract useful information from the retrieved data via sensors. Communications and safety systems are explained further to complete the loop of implementation of cognitive entities on a manual assembly line. This deepened involvement of cognitive technologies are human-centered, rather than automated systems. The explained cognitive technologies enhance human interaction with the processes and ease the production methods. These concepts form a quintessential vision for an effective assembly line. This paper revolutionizes the existing industry 4.0 with an even-intensified human–machine interaction and moving towards cognitivity

    A 3D Digital Approach to the Stylistic and Typo-Technological Study of Small Figurines from Ayia Irini, Cyprus

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    The thesis aims to develop a 3D digital approach to the stylistic and typo-technological study of coroplastic, focusing on small figurines. The case study to test the method is a sample of terracotta statuettes from an assemblage of approximately 2000 statues and figurines found at the beginning of the 20th century in a rural open-air sanctuary at Ayia Irini (Cyprus) by the archaeologists of the Swedish Cyprus Expedition. The excavators identified continuity of worship at the sanctuary from the Late Cypriot III (circa 1200 BC) to the end of the Cypro-Archaic II period (ca. 475 BC). They attributed the small figurines to the Cypro-Archaic I-II. Although the excavation was one of the first performed through the newly established stratigraphic method, the archaeologists studied the site and its material following a traditional, merely qualitative approach. Theanalysis of the published results identified a classification of the material with no-clear-cut criteria, and their overlap between types highlights ambiguities in creating groups and classes. Similarly, stratigraphic arguments and different opinions among archaeologists highlight the need for revising. Moreover, pastlegislation allowed the excavators to export half of the excavated antiquities, creating a dispersion of the assemblage. Today, the assemblage is still partly exhibited at the Cyprus Museum in Nicosia and in four different museums in Sweden. Such a setting prevents to study, analyse and interpret the assemblageholistically. This research proposes a 3D chaîne opératoire methodology to study the collection’s small terracotta figurines, aiming to understand the context’s function and social role as reflected by the classification obtained with the 3D digital approach. The integration proposed in this research of traditional archaeological studies, and computer-assisted investigation based on quantitative criteria, identified and defined with 3D measurements and analytical investigations, is adopted as a solution to the biases of a solely qualitative approach. The 3D geometric analysis of the figurines focuses on the objects’ shape and components, mode of manufacture, level of expertise, specialisation or skills of the craftsman and production techniques. The analysis leads to the creation of classes of artefacts which allow archaeologists to formulate hypotheses on the production process, identify a common production (e.g., same hand, same workshop) and establish a relative chronological sequence. 3D reconstruction of the excavation’s area contributes to the virtual re-unification of the assemblage for its holistic study, the relative chronological dating of the figurines and the interpretation of their social and ritual purposes. The results obtained from the selected sample prove the efficacy of the proposed 3D approach and support the expansion of the analysis to the whole assemblage, and possibly initiate quantitative and systematic studies on Cypriot coroplastic production

    Accelerating Deep Reinforcement Learning via Action Advising

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    Deep Reinforcement Learning (RL) algorithms can solve complex sequential decision-making tasks successfully. However, they suffer from the major drawbacks of having poor sample efficiency and long training times, which can often be tackled by knowledge reuse. Action advising is a promising knowledge exchange mechanism that adopts the teacher-student paradigm to leverage some legacy knowledge through a budget-limited number of interactions in the form of action advice between peers. In this thesis, we studied action advising techniques, particularly in Deep RL domain, both in single-agent and multi-agent scenarios. We proposed a heuristic-based jointly-initiated action advising method that is suitable for multi-agent Deep RL setting, for the first time in literature. By adopting Random Network Distillation (RND), we devised a measurement for agents to assess their confidence in any given state to initiate the teacher-student dynamics with no prior role assumptions. We also used RND as an advice novelty metric to construct more robust student-initiated advice query strategies in single-agent Deep RL. Moreover, we addressed the absence of advice utilisation mechanisms beyond collection by employing a behavioural cloning module to imitate the teacher's advice. We also proposed a method to automatically tune the relevant hyperparameters of these components on the fly to make our action advising algorithms capable of adapting to any domain with minimal human intervention. Finally, we extended our advice reuse via imitation technique to construct a unified student-initiated approach that addresses both advice collection and advice utilisation problems. The experiments we conducted in a range of Deep RL domains showed that our proposal provides significant contributions. Our Deep RL-compatible action advising techniques managed to achieve a state-of-the-art level of performance. Furthermore, we demonstrated that their practical attributes render domain adaptation and implementation processes straightforward, which is an important progression towards being able to apply action advising in real-world problems

    An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

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    The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so
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