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Proceedings of the 33rd Annual Workshop of the Psychology of Programming Interest Group
This is the Proceedings of the 33rd Annual Workshop of the Psychology of Programming Interest Group (PPIG). This was the first PPIG to be held physically since 2019, following the two online-only PPIGs in 2020 and 2021, both during the Covid pandemic. It was also the first PPIG conference to be designed specifically for hybrid attendance. Reflecting the theme, it was hosted by Music Computing Lab at the Open University in Milton Keynes
Interpretable and verifiable planning and prediction for autonomous vehicles
Autonomous driving (AD) has gained much attention in recent years due to its many potential benefits such as improving safety and increasing efficiency. However, AD is a difficult problem with challenges such as handling interactions with other vehicles and predicting the future behaviour of human drivers. This often takes place in complicated urban environments where information is missing due to occlusions. AD methods must also be accurate and effective while still being efficient enough to run in real time.
In this thesis, several novel AD methods are presented which contribute towards solving some of the problems of AD. In particular, the focus is on planning, prediction and goal recognition (GR) methods which are interpretable by humans and formally verifiable. Interpretability can increase user trust of AD systems and aid with debugging issues with such systems. Having the ability to formally verify propositions made about AD methods can help ensure safety and compliance with regulations.
The first novel method is Interpretable Goal-based Prediction and Planning (IGP2) which integrates GR through inverse planning with Monte Carlo tree search (MCTS) to achieve a full planning and prediction system. IGP2 is evaluated in several urban driving scenarios and is shown to successfully recognise other vehicle's goals and improve driving efficiency. The second method is Goal Recognition with Interpretable Trees (GRIT). GRIT makes use of learned decision trees trained to infer a probability distribution over the goals of other vehicles. An evaluation across two vehicle trajectory datasets shows that the inference process of GRIT is fast, accurate, interpretable and verifiable. The third method is Goal Recognition with Interpretable Trees under Occlusion (OGRIT). Similarly to GRIT, OGRIT makes use of learned decision trees for GR. Through an evaluation across two vehicle trajectory datasets with significant occlusions, OGRIT is also shown to handle information missing due to occlusions and can make inferences across multiple scenarios using the same learned models, while still remaining fast, accurate, interpretable and verifiable. This thesis contributes three novel methods which work towards allowing autonomous vehicles to accurately and efficiently infer the goals of other vehicles in complex, partially occluded urban environments, and then predict their future behaviour and plan accordingly
Towards Developing a Digital Twin Implementation Framework for Manufacturing Systems
This research studies the implementation of digital twins in manufacturing systems. Digital transformation is relevant due to changing manufacturing techniques and user demands. It brings new business opportunities, changes organizations, and allows factories to compete in the digital era. Nevertheless, digital transformation presents many uncertainties that could bring problems to a manufacturing system. Some potential problems are loss of data, cybersecurity threats, unpredictable behavior, and so on. For instance, there are doubts about how to integrate the physical and virtual spaces. Digital twin (DT) is a modern technology that can enable the digital transformation of manufacturing companies. DT works by collecting real-time data of machines, products, and processes. DT monitors and controls operations in real-time helping in the identification of problems. It performs simulations to improve manufacturing processes and end-products. DT presents several benefits for manufacturing systems. It gives feedback to the physical system, increases the system’s reliability and availability, reduces operational risks, helps to achieve organizational goals, reduces operations and maintenance costs, predicts machine failures, etc. DT presents all these benefits without affecting the system’s operation. xv This dissertation analyzes the implementation of digital twins in manufacturing systems. It uses systems thinking methods and tools to study the problem space and define the solution space. Some of these methods are the conceptagon, systemigram, and the theory of inventive problem solving (TRIZ in Russian acronym). It also uses systems thinking tools such as the CATWOE, the 9-windows tool, and the ideal final result (IFR). This analysis gives some insights into the digital twin implementation issues and potential solutions. One of these solutions is to build a digital twin implementation framework Next, this study proposes the development of a small-scale digital twin implementation framework. This framework could help users to create digital twins in manufacturing systems. The method to build this framework uses a Model-Based Systems Engineering approach and the systems engineering “Vee” model. This framework encompasses many concepts from the digital twin literature. The framework divides these concepts along three spaces: physical, virtual, and information. It also includes other concepts such as digital thread, data, ontology, and enabling technologies. Finally, this dissertation verifies the correctness of the proposed framework. The verification process shows that the proposed framework can develop digital twins for manufacturing systems. For that purpose, this study creates a process digital twin simulation using the proposed framework. This study presents a mapping and a workflow diagram to help users use the proposed framework. Then, it compares the digital twin simulation with the digital twin user and system requirements. The comparison finds that the proposed framework was built right
OpenCog Hyperon: A Framework for AGI at the Human Level and Beyond
An introduction to the OpenCog Hyperon framework for Artificiai General
Intelligence is presented. Hyperon is a new, mostly from-the-ground-up
rewrite/redesign of the OpenCog AGI framework, based on similar conceptual and
cognitive principles to the previous OpenCog version, but incorporating a
variety of new ideas at the mathematical, software architecture and
AI-algorithm level. This review lightly summarizes: 1) some of the history
behind OpenCog and Hyperon, 2) the core structures and processes underlying
Hyperon as a software system, 3) the integration of this software system with
the SingularityNET ecosystem's decentralized infrastructure, 4) the cognitive
model(s) being experimentally pursued within Hyperon on the hopeful path to
advanced AGI, 5) the prospects seen for advanced aspects like reflective
self-modification and self-improvement of the codebase, 6) the tentative
development roadmap and various challenges expected to be faced, 7) the
thinking of the Hyperon team regarding how to guide this sort of work in a
beneficial direction ... and gives links and references for readers who wish to
delve further into any of these aspects
A Design Science Research Approach to Architecting and Developing Information Systems for Collaborative Manufacturing : A Case for Human-Robot Collaboration
Konseptointi- ja suunnitteluvaiheessa sekä valmistuksen, käytön ja kehitysprosessin aikana syntyy tietoa, jonka hyödyntämisessä on valtavaa potentiaalia liike-elämän ja tuotantoprosessien muuttamiseen. Neljännen teollisen vallankumouksen ytimessä oleva digitaalinen muutos tunnistaa tämän painottaen erityisesti tämän tiedon yhdistämistä toimintojen ja järjestelmien tukemiseksi läpi tuotteen elinkaareen, mitä kutsutaan digitaaliseksi säikeen kehykseksi (digital thread framework). Tämän väitöskirjan tavoitteena on kehittää ja käyttää yhtä tällaista viitekehystä ihmisen ja robotin yhteistoiminnan asiayhteydessä. Tämä kehys pyrkii vastaamaan merkittävään ongelmaan, joka liittyy mukautuvuuden ja joustavuuden abstrakteihin ominaisuuksiin. Nykyiset ihmisen ja robotin yhteistyöjärjestelmät (human-robot collaboration (HRC)) on rakennettu pääasiassa pysyviksi järjestelmiksi, jotka sivuuttavat ihmisten intuitiivisen toiminnan asettamalla heidän roolinsa yhteistyötehtävissä etukäteen määritellyiksi. Lisäksi järjestelmien kyky vaihtaa tuotteesta toiseen on rajoittunutta. Tämä on erityisen ongelmallista nykyisellä laajan tuotevalikoiman aikakaudella, joka johtuu asiakkaiden räätälöidyistä vaatimuksista.
Tähän taustaan vastaten, tämä väitöskirja käyttää design science research methodology -menetelmää suunnitellakseen, kehittääkseen ja ottaakseen käyttöön kolme pääasiallista artefaktia ihmisen ja robotin yhteistyösolussa laboratorioympäristössä. Ensimmäinen on digitaalisen säikeen kehys (digital thread framework), joka integroi tuotesuunnitteluympäristön toimijaksi monitoimijajärjestelmään käyttäen uusimpia tietoon perustuvia suunnittelujärjestelmiä, mikä tarjoaa prosessin toimijoille pääsyn tuotesuunnittelumalleihin reaaliajassa. Toinen on lisätyn todellisuuden malli, joka tarjoaa rajapinnan kokoonpanotehtävässä yhteistyöhön osallistuvan ihmisoperaattorin ja edellä mainitun kehyksen välille. Kolmas on tukitietomalli, jota yhteistyötä tekevät toimijat käyttävät tietopohjanaan täyttääkseen yhteistyössä tapahtuvan kokoonpanon tavoitteet mukautuvasti.
Näitä kehitettyjä artefakteja käytettiin kokonaisuutena tapaustutkimuksissa, jotka liittyivät aidon dieselmoottorin kokoonpanoon, ja joissa todennettiin niiden hyödyllisyys ja että ne lisäävät joustavuutta, jota varten kehys (framework) suunniteltiin. Rajauslaatikoiden näyttäminen skaalautuvana informaationa, joka hahmottaa alikokoonpanon osien geometriaa, demostroi kehitettyjen artefaktien käytettävyyttä yhteistyötä tekevien toimijoiden aikomuksia heijastavien laajennetun todellisuuden projektioiden tuottamiseksi.
Yhteenvetona tämän väitöskirjan tuloksena syntyi lähestymistapa älykkään ja mukautuvan robotiikan toteuttamiseksi hyödyntäen tietovirtoja ja mallinnusta ihmisen ja robotin yhteistoiminnan kontekstissa. Teollisuuden raportoima älykkäästi mukautuvien HRC-järjestelmien puute taas toimi osaltaan motivaationa tähän väitöskirjassa tehtyyn työhön. Kun tulevaisuuden tuotteet ja tuotantojärjestelmät muuttuvat monimutkaisemmiksi, tietojärjestelmiltä odotetaan suurempaa vastuuta korvaamaan ihmisen työmuistin luontaiset rajat ja mahdollistamaan siirtyminen kohti ihmiskeskeistä valmistusta, joihin viitataan termeillä Operator 4.0 ja Industry 5.0. Näin ollen on odotettavissa, että tietojärjestelmien tutkimus, kuten tämä väitöskirja, voi auttaa ottamaan merkittäviä askeleita tähän suuntaan.Information generated from the conceptualization, design, manufacturing, and use of a product has immense potential in transforming both the business and manufacturing processes of the manufacturing enterprise. The digital transformation at the heart of the fourth industrial revolution has acknowledged this with a special emphasis on weaving a thread of this information to support functions and systems throughout the life cycle of the product with what is known as a digital thread framework. This dissertation aims to develop and use one such framework in the context of human-robot collaborative assembly. The overarching problem that the framework aims to solve can be attributed to the abstract qualities of adaptability and flexibility. The human-robot collaboration (HRC) systems of today are built predominantly as static systems and ignore the intuitive role of humans by having their roles in collaborative tasks pre-defined. Furthermore, their ability to switch between products during product changeovers is also limited. This is especially problematic in the current era of product variety, stemming from the customised requirements of customers.
To this end, this dissertation employs the design science research methodology to design, develop, and deploy predominantly three artefacts in a human-robot work cell in a laboratory setting. The first is the digital thread framework that integrates the product design environment using state-of-the-art knowledge-based engineering systems, as an agent of a multi-agent system, which provide the collaborative human-robot agents with access to product design models at run time. The second is a constituent mixed-reality model that provides an interface for the foregoing framework for the human operator engaged in collaborative assembly. The third is a supporting information model that the agents use as their knowledge base to fulfil adaptively the goals of collaborative assembly.
Together, these developed artefacts were employed in case studies involving a real diesel engine assembly during which they were observed to provide utility and support the cause of adaptability for which the framework was designed. The identification of bounding boxes as a scalable information construct, that approximates the part geometry of the sub-assembly components, demonstrates the utility of the developed artefacts for spatially augmenting them as projections as intentions of collaborating agents.
In summary, this dissertation contributes with an approach towards realising intelligent and adaptive robotics within the realms of information flows and modelling in the context of human-robot collaboration. The lack of intelligently adaptable HRC systems reported by the industry in part motivated the work undertaken in this dissertation. As future products and production systems become more complex, information systems are expected to assume greater responsibility to compensate for the inherent limits of the human working memory and enable transition towards a human-centred manufacturing, the current likes of which are labelled as Operator 4.0 and Industry 5.0. Thus, the expectation is that information systems research, such as this dissertation, can help take significant strides forward in this direction
Cybersecurity of Digital Service Chains
This open access book presents the main scientific results from the H2020 GUARD project. The GUARD project aims at filling the current technological gap between software management paradigms and cybersecurity models, the latter still lacking orchestration and agility to effectively address the dynamicity of the former. This book provides a comprehensive review of the main concepts, architectures, algorithms, and non-technical aspects developed during three years of investigation; the description of the Smart Mobility use case developed at the end of the project gives a practical example of how the GUARD platform and related technologies can be deployed in practical scenarios. We expect the book to be interesting for the broad group of researchers, engineers, and professionals daily experiencing the inadequacy of outdated cybersecurity models for modern computing environments and cyber-physical systems
Applied Methuerstic computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
The Road to General Intelligence
Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book
Proceedings of the 19th Sound and Music Computing Conference
Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France).
https://smc22.grame.f
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