| 1. |
- Heintz, Fredrik, et al.
(författare)
-
Linköping Humanoids : Application RoboCup 2016 Standard Platform League
- 2016
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This is the application for the RoboCup 2016 Standard Platform League from the Linköping Humanoids team.Linköping Humanoids participated in RoboCup 2015. We didn’t do very well, but we learned a lot. When we arrived nothing worked. However, we fixed more and more of the open issues and managed to play a draw in our final game. We also participated in some of the technical challenges and scored some points. At the end of the competition we had a working team. This was both frustrating and rewarding. Analyzing the competition we have identified both what we did well and the main issues that we need to fix. One important lesson is that it takes time to develop a competitive RoboCup SPL team. Weare dedicated to improving our performance over time in order to be competitive in 2017.
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| 2. |
- Heintz, Fredrik, et al.
(författare)
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Linköping Humanoids : Application RoboCup 2017 Standard Platform League
- 2017
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This is the application for the RoboCup 2017 Standard Platform League from the Link¨oping Humanoids teamLinköping Humanoids participated in both RoboCup 2015 and 2016 with the intention of incrementally developing a good team by learning as much as possible. We significantly improved from 2015 to 2016, even though we still didn’t perform very well. Our main challenge is that we are building our software from the ground up using the Robot Operating System (ROS) as the integration and development infrastructure. When the system became overloaded, the ROS infrastructure became very unpredictable. This made it very hard to debug during the contest, so we basically had to remove things until the load was constantly low. Our top priority has since been to make the system stable and more resource efficient. This will take us to the next level.From the start we have been clear that our goal is to have a competitive team by 2017 since we are developing our own software from scratch we are very well aware that we needed time to build up the competence and the software infrastructure. We believe we are making good progress towards this goal. The team of about 10 students has been very actively working during the fall with weekly workshops and bi-weekly one day hackathons.
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| 3. |
- Larsson, Stefan, et al.
(författare)
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HÅLLBAR AI : Inventering av kunskapsläget för etiska, sociala och rättsliga utmaningar med artificiell intelligens
- 2019
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Detta är en inventering av kunskapsläget för etiska, sociala, och rättsliga utmaningar med artificiell intelligens, utfört i ett Vinnovafinansierat projekt lett av Anna Felländer. Baserat på en kartläggning av rapporter och studier, en kvantitativ och bibliometrisk analys, och områdesfördjupningar inom vård och hälsa, telekom, och digitala plattformar ges tre rekommendationer: Hållbar AI kräver att vi 1. fokuserar regleringsfrågor i vid mening, 2. stimulerar mångvetenskap och samverkan, samt att 3. tillitsbyggande i användningen av samhällsapplicerad artificiell intelligens och maskininlärning är centralt och kräver mer kunskap i relationen mellan transparens och ansvar.
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| 4. |
- Löfgren, Fredrik, et al.
(författare)
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Qualification document : RoboCup 2015 Standard Platform League
- 2015
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This is the application for the RoboCup 2015 StandardPlatform League from the ”LiU Robotics” team. In thisdocument we present ourselves and what we want to achieve byour participation in the conference and competition
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| 5. |
- Präntare, Fredrik, 1990-, et al.
(författare)
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An Algorithm for Simultaneous Coalition Structure Generation and Task Assignment
- 2017
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Ingår i: PRIMA 2017: Principles and Practice of Multi-Agent Systems 20th International Conference, Nice, France, October 30 – November 3, 2017, Proceedings. - Cham : Springer. - 9783319691305 - 9783319691312 ; , s. 514-522
-
Konferensbidrag (refereegranskat)abstract
- Groups of agents in multi-agent systems may have to cooperate to solve tasks efficiently, and coordinating such groups is an important problem in the field of artificial intelligence. In this paper, we consider the problem of forming disjoint coalitions and assigning them to independent tasks simultaneously, and present an anytime algorithm that efficiently solves the simultaneous coalition structure generation and task assignment problem. This NP-complete combinatorial optimization problem has many real-world applications, including forming cross-functional teams aimed at solving tasks. To evaluate the algorithm's performance, we extend established methods for synthetic problem set generation, and benchmark the algorithm using randomized data sets of varying distribution and complexity. Our results show that the presented algorithm efficiently finds optimal solutions, and generates high quality solutions when interrupted prior to finishing an exhaustive search. Additionally, we apply the algorithm to solve the problem of assigning agents to regions in a commercial computer-based strategy game, and empirically show that our algorithm can significantly improve the coordination and computational efficiency of agents in a real-time multi-agent system.
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| 6. |
- Präntare, Fredrik, et al.
(författare)
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An anytime algorithm for optimal simultaneous coalition structure generation and assignment
- 2020
-
Ingår i: Autonomous Agents and Multi-Agent Systems. - : SPRINGER. - 1387-2532 .- 1573-7454. ; 34:1
-
Tidskriftsartikel (refereegranskat)abstract
- An important research problem in artificial intelligence is how to organize multiple agents, and coordinate them, so that they can work together to solve problems. Coordinating agents in a multi-agent system can significantly affect the systems performance-the agents can, in many instances, be organized so that they can solve tasks more efficiently, and consequently benefit collectively and individually. Central to this endeavor is coalition formation-the process by which heterogeneous agents organize and form disjoint groups (coalitions). Coalition formation often involves finding a coalition structure (an exhaustive set of disjoint coalitions) that maximizes the systems potential performance (e.g., social welfare) through coalition structure generation. However, coalition structure generation typically has no notion of goals. In cooperative settings, where coordination of multiple coalitions is important, this may generate suboptimal teams for achieving and accomplishing the tasks and goals at hand. With this in mind, we consider simultaneously generating coalitions of agents and assigning the coalitions to independent alternatives (e.g., tasks/goals), and present an anytime algorithm for the simultaneous coalition structure generation and assignment problem. This combinatorial optimization problem hasmany real-world applications, including forming goal-oriented teams. To evaluate the presented algorithms performance, we present five methods for synthetic problem set generation, and benchmark the algorithm against the industry-grade solver CPLEXusing randomized data sets of varying distribution and complexity. To test its anytime-performance, we compare the quality of its interim solutions against those generated by a greedy algorithm and pure random search. Finally, we also apply the algorithm to solve the problem of assigning agents to regions in a major commercial strategy game, and show that it can be used in game-playing to coordinate smaller sets of agents in real-time.
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| 7. |
- Präntare, Fredrik, 1990-, et al.
(författare)
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An Anytime Algorithm for Simultaneous Coalition Structure Generation and Assignment
- 2018
-
Ingår i: PRIMA 2018: Principles and Practice of Multi-Agent Systems. - Cham : Springer International Publishing. - 9783030030971 - 9783030030988 ; , s. 158-174
-
Konferensbidrag (refereegranskat)abstract
- A fundamental problem in artificial intelligence is how to organize and coordinate agents to improve their performance and skills. In this paper, we consider simultaneously generating coalitions of agents and assigning the coalitions to independent tasks, and present an anytime algorithm for the simultaneous coalition structure generation and assignment problem. This optimization problem has many real-world applications, including forming goal-oriented teams of agents. To evaluate the algorithm’s performance, we extend established methods for synthetic problem set generation, and benchmark the algorithm against CPLEX using randomized data sets of varying distribution and complexity. We also apply the algorithm to solve the problem of assigning agents to regions in a major commercial strategy game, and show that the algorithm can be utilized in game-playing to coordinate smaller sets of agents in real-time.
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| 8. |
- Präntare, Fredrik, et al.
(författare)
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Anytime Heuristic and Monte Carlo Methods for Large-Scale Simultaneous Coalition Structure Generation and Assignment
- 2021
-
Ingår i: THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE. - : ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. - 9781577358664 ; , s. 11317-11324
-
Konferensbidrag (refereegranskat)abstract
- Optimal simultaneous coalition structure generation and assignment is computationally hard. The state-of-the-art can only compute solutions to problems with severely limited input sizes, and no effective approximation algorithms that are guaranteed to yield high-quality solutions are expected to exist. Real-world optimization problems, however, are often characterized by large-scale inputs and the need for generating feasible solutions of high quality in limited time. In light of this, and to make it possible to generate better feasible solutions for difficult large-scale problems efficiently, we present and benchmark several different anytime algorithms that use general-purpose heuristics and Monte Carlo techniques to guide search. We evaluate our methods using synthetic problem sets of varying distribution and complexity. Our results show that the presented algorithms are superior to previous methods at quickly generating near-optimal solutions for small-scale problems, and greatly superior for efficiently finding high-quality solutions for large-scale problems. For example, for problems with a thousand agents and values generated with a uniform distribution, our best approach generates solutions 99.5% of the expected optimal within seconds. For these problems, the state-of-the-art solvers fail to find any feasible solutions at all.
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| 9. |
- Präntare, Fredrik, 1990-
(författare)
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Dividing the Indivisible : Algorithms, Empirical Advances, and Complexity Results for Value-Maximizing Combinatorial Assignment Problems
- 2024
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Allocating resources, goods, agents (e.g., humans), expertise, production, and assets is one of the most influential and enduring cornerstone challenges at the intersection of artificial intelligence, operations research, politics, and economics. At its core—as highlighted by a number of seminal works [181, 164, 125, 32, 128, 159, 109, 209, 129, 131]—is a timeless question: How can we best allocate indivisible entities—such as objects, items, commodities, jobs, or personnel—so that the outcome is as valuable as possible, be it in terms of expected utility, fairness, or overall societal welfare? This thesis confronts this inquiry from multiple algorithmic viewpoints, focusing on the value-maximizing combinatorial assignment problem: the optimization challenge of partitioning a set of indivisibles among alternatives to maximize a given notion of value. To exemplify, consider a scenario where an international aid organization is responsible for distributing medical resources, such as ventilators and vaccines, and allocating medical personnel, including doctors and nurses, to hospitals during a global health crisis. These resources and personnel—inherently indivisible and non-fragmentable—necessitate an allocation process designed to optimize utility and fairness. Rather than using manual interventions and ad-hoc methods, which often lack precision and scalability, a rigorously developed and demonstrably performant approach can often be more desirable. With this type of challenge in mind, our thesis begins through the lens of computational complexity theory, commencing with an initial insight: In general, under prevailing complexity-theoretic assumptions (P ≠ NP), it is impossible to develop an efficient method guaranteeing a value-maximizing allocation that is better than “arbitrarily bad”, even under severely constraining limitations and simplifications. This inapproximability result not only underscores the problem’s complexity but also sets the stage for our ensuing work, wherein we develop novel algorithms and concise representations for utilitarian, egalitarian, and Nash welfare maximization problems, aimed at maximizing average, equitable, and balanced utility, respectively. For example, we introduce the synergy hypergraph—a hypergraph-based characterization of utilitarian combinatorial assignment—which allows us to prove several new state-of-the-art complexity results to help us better understand how hard the problem is. We then provide efficient approximation algorithms and (non-trivial) exponential-time algorithms for many hard cases. In addition, we explore complexity bounds for generalizations with interdependent effects between allocations, known as externalities in economics. Natural applications in team formation, resource allocation, and combinatorial auctions are also discussed; and a novel “bootstrapped” dynamic-programming method is introduced. We then transition from theory to practice as we shift our focus to the utilitarian variant of the problem—an incarnation of the problem particularly applicable to many real-world scenarios. For this variation, we achieve substantial empirical algorithmic improvements over existing methods, including industry-grade solvers. This work culminates in the development of a new hybrid algorithm that combines dynamic programming with branch-and-bound techniques that is demonstrably faster than all competing methods in finding both optimal and near-optimal allocations across a wide range of experiments. For example, it solves one of our most challenging problem sets in just 0.25% of the time required by the previous best methods, representing an improvement of approximately 2.6 orders of magnitude in processing speed. Additionally, we successfully integrate and commercialize our algorithm into Europa Universalis IV—one of the world’s most popular strategy games, with a player base exceeding millions. In this dynamic and challenging setting, our algorithm efficiently manages complex strategic agent interactions, highlighting its potential to improve computational efficiency and decision-making in real-time, multi-agent scenarios. This also represents one of the first instances where a combinatorial assignment algorithm has been applied in a commercial context. We then introduce and evaluate several highly efficient heuristic algorithms. These algorithms—while lacking provable quality guarantees—employ general-purpose heuristic and random-sampling techniques to significantly outperform existing methods in both speed and quality in large-input scenarios. For instance, in one of our most challenging problem sets, involving a thousand indivisibles, our best algorithm generates outcomes that are 99.5% of the expected optimal in just seconds. This performance is particularly noteworthy when compared to state-of-the-art industry-grade solvers, which struggle to produce any outcomes under similar conditions. Further advancing our work, we employ novel machine learning techniques to generate new heuristics that outperform the best hand-crafted ones. This approach not only showcases the potential of machine learning in combinatorial optimization but also sets a new standard for combinatorial assignment heuristics to be used in real-world scenarios demanding rapid, high-quality decisions, such as in logistics, real-time tactics, and finance. In summary, this thesis bridges many gaps between the theoretical and practical aspects of combinatorial assignment problems such as those found in coalition formation, combinatorial auctions, welfare-maximizing resource allocation, and assignment problems. It deepens the understanding of the computational complexities involved and provides effective and improved solutions for longstanding real-world challenges across various sectors—providing new algorithms applicable in fields ranging from artificial intelligence to logistics, finance, and digital entertainment, while simultaneously paving the way for future work in computational problem-solving and optimization.
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| 10. |
- Präntare, Fredrik, 1990-, et al.
(författare)
-
Hybrid Dynamic Programming for Simultaneous Coalition Structure Generation and Assignment
- 2021
-
Ingår i: PRIMA 2020: Principles and Practice of Multi-Agent Systems. - Cham : Springer. - 9783030693220 - 9783030693213 ; , s. 19-33
-
Konferensbidrag (refereegranskat)abstract
- We present, analyze and benchmark two algorithms for simultaneous coalition structure generation and assignment: one based entirely on dynamic programming, and one anytime hybrid approach that uses branch-and-bound together with dynamic programming. To evaluate the algorithms’ performance, we benchmark them against both CPLEX (an industry-grade solver) and the state-of-the-art using difficult randomized data sets of varying distribution and complexity. Our results show that our hybrid algorithm greatly outperforms CPLEX, pure dynamic programming and the current state-of-the-art in all of our benchmarks. For example, when solving one of the most difficult problem sets, our hybrid approach finds optimum in roughly 0.1% of the time that the current best method needs, and it generates 98% efficient interim solutions in milliseconds in all of our anytime benchmarks; a considerable improvement over what previous methods can achieve.
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| 11. |
- Präntare, Fredrik, 1990-, et al.
(författare)
-
Towards Utilitarian Combinatorial Assignment with Deep Neural Networks and Heuristic Algorithms
- 2020
-
Konferensbidrag (refereegranskat)abstract
- This paper presents preliminary work on using deep neural networksto guide general-purpose heuristic algorithms for performing utilitarian combinatorial assignment. In more detail, we use deep learning in an attempt to produce heuristics that can be used together with e.g., search algorithms to generatefeasible solutions of higher quality more quickly. Our results indicate that ourapproach could be a promising future method for constructing such heuristics.
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| 12. |
- Åkerfeldt, Anna, et al.
(författare)
-
"Fridolin backar in i framtiden om digitala läromedel"
- 2021
-
Ingår i: Dagens Nyheter. - 1101-2447. ; :2021-12-16
-
Tidskriftsartikel (populärvet., debatt m.m.)abstract
- Ingress: 23 forskare inom it- och utbildningsområdet: Regeringens utredare borde inte lyfta fram läsning på skärm som något negativt.Forskning visar att både tryckta och digitala läromedel behövs i skolan.
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| 13. |
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| 14. |
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| 15. |
- Andersson, Olov, 1979-, et al.
(författare)
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Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization
- 2015
-
Ingår i: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI). - : AAAI Press. - 9781577356981 ; , s. 2497-2503
-
Konferensbidrag (refereegranskat)abstract
- Reinforcement learning for robot control tasks in continuous environments is a challenging problem due to the dimensionality of the state and action spaces, time and resource costs for learning with a real robot as well as constraints imposed for its safe operation. In this paper we propose a model-based reinforcement learning approach for continuous environments with constraints. The approach combines model-based reinforcement learning with recent advances in approximate optimal control. This results in a bounded-rationality agent that makes decisions in real-time by efficiently solving a sequence of constrained optimization problems on learned sparse Gaussian process models. Such a combination has several advantages. No high-dimensional policy needs to be computed or stored while the learning problem often reduces to a set of lower-dimensional models of the dynamics. In addition, hard constraints can easily be included and objectives can also be changed in real-time to allow for multiple or dynamic tasks. The efficacy of the approach is demonstrated on both an extended cart pole domain and a challenging quadcopter navigation task using real data.
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| 16. |
- Andersson, Olov, 1979-, et al.
(författare)
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Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance
- 2018
-
Ingår i: 2018 IEEE Conference on Decision and Control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538613955 - 9781538613948 - 9781538613962 ; , s. 4467-4474
-
Konferensbidrag (refereegranskat)abstract
- A key requirement of autonomous vehicles is the capability to safely navigate in their environment. However, outside of controlled environments, safe navigation is a very difficult problem. In particular, the real-world often contains both complex 3D structure, and dynamic obstacles such as people or other vehicles. Dynamic obstacles are particularly challenging, as a principled solution requires planning trajectories with regard to both vehicle dynamics, and the motion of the obstacles. Additionally, the real-time requirements imposed by obstacle motion, coupled with real-world computational limitations, make classical optimality and completeness guarantees difficult to satisfy. We present a unified optimization-based motion planning and control solution, that can navigate in the presence of both static and dynamic obstacles. By combining optimal and receding-horizon control, with temporal multi-resolution lattices, we can precompute optimal motion primitives, and allow real-time planning of physically-feasible trajectories in complex environments with dynamic obstacles. We demonstrate the framework by solving difficult indoor 3D quadcopter navigation scenarios, where it is necessary to plan in time. Including waiting on, and taking detours around, the motions of other people and quadcopters.
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| 17. |
- Berglund, Aseel, et al.
(författare)
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Integrating Soft Skills into Engineering Education for Increased Student Throughput and more Professional Engineers
- 2014
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Ingår i: Proceedings of LTHs 8:e Pedagogiska Inspirationskonferens (PIK). - Lund, Sweden : Lunds university.
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Soft skills are recognized as crucial for engineers as technical work is becoming more and more collaborative and interdisciplinary. Today many engineering educations fail to give appropriate training in soft skills. Linköping University has therefore developed a completely new course “Professionalism for Engineers” for two of its 5-year engineering programs in the area of computer science. The course stretches over the first 3 years with students from the three years taking it together. The purpose of the course is to give engineering students training in soft skills that are of importance during the engineering education as well as during their professional career. The examination is based on the Dialogue Seminar Method developed for learning from experience and through reflection. The organization of the course is innovative in many ways.
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| 18. |
- Bergström, David, 1994-, et al.
(författare)
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Bayesian optimization for selecting training and validation data for supervised machine learning
- 2019
-
Ingår i: 31st annual workshop of the Swedish Artificial Intelligence Society (SAIS 2019), Umeå, Sweden, June 18-19, 2019..
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Validation and verification of supervised machine learning models is becoming increasingly important as their complexity and range of applications grows. This paper describes an extension to Bayesian optimization which allows for selecting both training and validation data, in cases where data can be generated or calculated as a function of a spatial location.
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| 19. |
- Bhatt, Mehul, Professor, 1980-, et al.
(författare)
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Cognitive robotics
- 2016
-
Ingår i: Journal of experimental and theoretical artificial intelligence (Print). - : Taylor & Francis Group. - 0952-813X .- 1362-3079. ; 28:5, s. 779-780
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Tidskriftsartikel (refereegranskat)
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| 20. |
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| 21. |
- Bonte, Pieter, et al.
(författare)
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Grounding Stream Reasoning Research
- 2024
-
Ingår i: Transactions on Graph Data and Knowledge (TGDK). - Wadern, Germany : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH. - 2942-7517. ; 2:1, s. 1-47
-
Tidskriftsartikel (refereegranskat)abstract
- In the last decade, there has been a growing interest in applying AI technologies to implement complex data analytics over data streams. To this end, researchers in various fields have been organising a yearly event called the "Stream Reasoning Workshop" to share perspectives, challenges, and experiences around this topic.In this paper, the previous organisers of the workshops and other community members provide a summary of the main research results that have been discussed during the first six editions of the event. These results can be categorised into four main research areas: The first is concerned with the technological challenges related to handling large data streams. The second area aims at adapting and extending existing semantic technologies to data streams. The third and fourth areas focus on how to implement reasoning techniques, either considering deductive or inductive techniques, to extract new and valuable knowledge from the data in the stream.This summary is written not only to provide a crystallisation of the field, but also to point out distinctive traits of the stream reasoning community. Moreover, it also provides a foundation for future research by enumerating a list of use cases and open challenges, to stimulate others to join this exciting research area.
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| 22. |
- Carlsen, Henrik, et al.
(författare)
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Chasing artificial intelligence in shared socioeconomic pathways
- 2024
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Ingår i: One Earth. - : CELL PRESS. - 2590-3330 .- 2590-3322. ; 7:1, s. 18-22
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The development of artificial intelligence has likely reached an inflection point, with significant implications for how research needs to address emerging technologies and how they drive long-term socioeconomic development of importance for climate change scenarios.
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| 23. |
- Curry, Edward, et al.
(författare)
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Partnership on AI, Data, and Robotics
- 2022
-
Ingår i: Communications of the ACM. - : ASSOC COMPUTING MACHINERY. - 0001-0782 .- 1557-7317. ; 65:4, s. 54-55
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
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| 24. |
- Danelljan, Martin, et al.
(författare)
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A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems
- 2015
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Ingår i: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I. - Cham : Springer Publishing Company. - 9783319161778 - 9783319161785 ; , s. 223-237
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Konferensbidrag (refereegranskat)abstract
- Micro unmanned aerial vehicles are becoming increasingly interesting for aiding and collaborating with human agents in myriads of applications, but in particular they are useful for monitoring inaccessible or dangerous areas. In order to interact with and monitor humans, these systems need robust and real-time computer vision subsystems that allow to detect and follow persons.In this work, we propose a low-level active vision framework to accomplish these challenging tasks. Based on the LinkQuad platform, we present a system study that implements the detection and tracking of people under fully autonomous flight conditions, keeping the vehicle within a certain distance of a person. The framework integrates state-of-the-art methods from visual detection and tracking, Bayesian filtering, and AI-based control. The results from our experiments clearly suggest that the proposed framework performs real-time detection and tracking of persons in complex scenarios
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| 25. |
- de Leng, Daniel, 1988-, et al.
(författare)
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Approximate Stream Reasoning with Metric Temporal Logic under Uncertainty
- 2019
-
Ingår i: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI). - Palo Alto : AAAI Press. ; , s. 2760-2767
-
Konferensbidrag (refereegranskat)abstract
- Stream reasoning can be defined as incremental reasoning over incrementally-available information. The formula progression procedure for Metric Temporal Logic (MTL) makes use of syntactic formula rewritings to incrementally evaluate formulas against incrementally-available states. Progression however assumes complete state information, which can be problematic when not all state information is available or can be observed, such as in qualitative spatial reasoning tasks or in robotics applications. In those cases, there may be uncertainty as to which state out of a set of possible states represents the ‘true’ state. The main contribution of this paper is therefore an extension of the progression procedure that efficiently keeps track of all consistent hypotheses. The resulting procedure is flexible, allowing a trade-off between faster but approximate and slower but precise progression under uncertainty. The proposed approach is empirically evaluated by considering the time and space requirements, as well as the impact of permitting varying degrees of uncertainty.
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| 26. |
- de Leng, Daniel, 1988-, et al.
(författare)
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DyKnow: A Dynamically Reconfigurable Stream Reasoning Framework as an Extension to the Robot Operating System
- 2016
-
Ingår i: Proceedings of the Fifth IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR). - : IEEE conference proceedings. - 9781509046164 - 9781509046171 ; , s. 55-60
-
Konferensbidrag (refereegranskat)abstract
- DyKnow is a framework for stream reasoning aimed at robot applications that need to reason over a wide and varying array of sensor data for e.g. situation awareness. The framework extends the Robot Operating System (ROS). This paper presents the architecture and services behind DyKnow's run-time reconfiguration capabilities and offers an analysis of the quantitative and qualitative overhead. Run-time reconfiguration offers interesting advantages, such as fault recovery and the handling of changes to the set of computational and information resources that are available to a robot system. Reconfiguration capabilities are becoming increasingly important with the advances in areas such as the Internet of Things (IoT). We show the effectiveness of the suggested reconfiguration support by considering practical case studies alongside an empirical evaluation of the minimal overhead introduced when compared to standard ROS.
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| 27. |
- de Leng, Daniel, et al.
(författare)
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Ontology-Based Introspection in Support of Stream Reasoning
- 2015
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Ingår i: Proceedings of the Joint Ontology Workshops (JOWO 2015), Buenos Aires, Argentina, July 25-27, 2015. - : Rheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V. ; , s. 1-8, s. 78-87
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Building complex systems such as autonomous robots usually require the integration of a wide variety of components including high-level reasoning functionalities. One important challenge is integrating the information in a system by setting up the data flow between the components. This paper extends our earlier work on semantic matching with support for adaptive on-demand semantic information integration based on ontology-based introspection. We take two important stand-points. First, we consider streams of information, to handle the fact that information often becomes continually and incrementally available. Second, we explicitly represent the semantics of the components and the information that can be provided by them in an ontology. Based on the ontology our custom-made stream configuration planner automatically sets up the stream processing needed to generate the streams of information requested. Furthermore, subscribers are notified when properties of a stream changes, which allows them to adapt accordingly. Since the ontology represents both the system's information about the world and its internal stream processing many other powerful forms of introspection are also made possible. The proposed semantic matching functionality is part of the DyKnow stream reasoning framework and has been integrated in the Robot Operating System (ROS).
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| 28. |
- de Leng, Daniel, et al.
(författare)
-
Partial-State Progression for Stream Reasoning with Metric Temporal Logic
- 2018
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Ingår i: SIXTEENTH INTERNATIONAL CONFERENCE ON PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING. - : ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. ; , s. 633-634
-
Konferensbidrag (refereegranskat)abstract
- The formula progression procedure for Metric Temporal Logic (MTL), originally proposed by Bacchus and Kabanza, makes use of syntactic formula rewritings to incrementally evaluate MTL formulas against incrementally-available states. Progression however assumes complete state information, which can be problematic when not all state information is available or can be observed, such as in qualitative spatial reasoning tasks or in robot applications. Our main contribution is an extension of the progression procedure to handle partial state information. For each missing truth value, we efficiently consider all consistent hypotheses by branching progression for each such hypothesis. The resulting procedure is flexible, allowing a trade-off between faster but approximate and slower but precise partial-state progression.
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| 29. |
- de Leng, Daniel, 1988-, et al.
(författare)
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Qualitative Spatio-Temporal Stream Reasoning With Unobservable Intertemporal Spatial Relations Using Landmarks
- 2016
-
Ingår i: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI). - : AAAI Press. - 9781577357629 ; , s. 957-963
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Konferensbidrag (refereegranskat)abstract
- Qualitative spatio-temporal reasoning is an active research area in Artificial Intelligence. In many situations there is a need to reason about intertemporal qualitative spatial relations, i.e. qualitative relations between spatial regions at different time-points. However, these relations can never be explicitly observed since they are between regions at different time-points. In applications where the qualitative spatial relations are partly acquired by for example a robotic system it is therefore necessary to infer these relations. This problem has, to the best of our knowledge, not been explicitly studied before. The contribution presented in this paper is two-fold. First, we present a spatio-temporal logic MSTL, which allows for spatio-temporal stream reasoning. Second, we define the concept of a landmark as a region that does not change between time-points and use these landmarks to infer qualitative spatio-temporal relations between non-landmark regions at different time-points. The qualitative spatial reasoning is done in RCC-8, but the approach is general and can be applied to any similar qualitative spatial formalism.
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| 30. |
- de Leng, Daniel, 1988-
(författare)
-
Robust Stream Reasoning Under Uncertainty
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Vast amounts of data are continually being generated by a wide variety of data producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, the ability to make sense of these streams of data through reasoning is of great importance. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in physical environments. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and their refinement an important problem.Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this work, we integrate techniques for logic-based stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over uncertain streaming data and the problem of robustly managing streaming data and their refinement.The main contributions of this work are (1) a logic-based temporal reasoning technique based on path checking under uncertainty that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt to situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in a case study on run-time adaptive reconfiguration. The results show that the proposed system - by combining reasoning over and reasoning about streams - can robustly perform stream reasoning, even when the availability of streaming resources changes.
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| 31. |
- de Leng, Daniel, 1988-
(författare)
-
Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A lot of today's data is generated incrementally over time by a large variety of producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, making sense of these streams of data through reasoning is challenging. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in a physical environment. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and its refinement an important problem.Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this thesis, we integrate techniques for logic-based spatio-temporal stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over streaming data and the problem of robustly managing streaming data and its refinement.The main contributions of this thesis are (1) a logic-based spatio-temporal reasoning technique that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt in situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in the context of a case study on run-time adaptive reconfiguration. The results show that the proposed system – by combining reasoning over and reasoning about streams – can robustly perform spatio-temporal stream reasoning, even when the availability of streaming resources changes.
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| 32. |
- de Leng, Daniel, et al.
(författare)
-
Towards Adaptive Semantic Subscriptions for Stream Reasoning in the Robot Operating System
- 2017
-
Ingår i: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). - : IEEE. - 9781538626825 ; , s. 5445-5452
-
Konferensbidrag (refereegranskat)abstract
- Modern robotic systems often consist of a growing set of information-producing components that need to be appropriately connected for the system to function properly. This is commonly done manually or through relatively simple scripts by specifying explicitly which components to connect. However, this process is cumbersome and error-prone, does not scale well as more components are introduced, and lacks flexibility and robustness at run-time. This paper presents an algorithm for setting up and maintaining implicit subscriptions to information through its semantics rather than its source, which we call semantic subscriptions. The proposed algorithm automatically reconfigures the system when necessary in response to changes at run-time, making the semantic subscriptions adaptive to changing circumstances. To illustrate the effectiveness of adaptive semantic subscriptions, we present a case study with two SoftBank Robotics NAO robots for handling the cases when a component stops working and when new components, in this case a second robot, become available. The solution has been implemented as part of a stream reasoning framework integrated with the Robot Operating System (ROS).
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| 33. |
- de Leng, Daniel, et al.
(författare)
-
Towards On-Demand Semantic Event Processing for Stream Reasoning
- 2014
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Ingår i: 17th International Conference on Information Fusion. - 9788490123553
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The ability to automatically, on-demand, apply pattern matching over streams of information to infer the occurrence of events is an important fusion functionality. Existing event detection approaches require explicit configuration of what events to detect and what streams to use as input. This paper discusses on-demand semantic event processing, and extends the semantic information integration approach used in the stream processing middleware framework DyKnow to incorporate this new feature. By supporting on-demand semantic event processing, systems can automatically configure what events to detect and what streams to use as input for the event detection. This can also include the detection of lower-level events as well as processing of streams. The semantic stream query language C-SPARQL is used to specify events, which can be seen as transformations over streams. Since semantic streams consist of RDF triples, we suggest a method to convert between RDF streams and DyKnow streams. DyKnow is integrated in the Robot Operating System (ROS) and used for example in collaborative unmanned aircraft systems missions.
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| 34. |
- DellAglio, Daniele, et al.
(författare)
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Special issue on stream reasoning
- 2019
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Ingår i: Semantic Web. - : IOS PRESS. - 1570-0844 .- 2210-4968. ; 10:3, s. 453-455
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
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| 35. |
- Doherty, Patrick, 1957-, et al.
(författare)
-
A Delegation-Based Architecture for Collaborative Robotics
- 2011
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Ingår i: Agent-Oriented Software Engineering XI. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642226359 ; , s. 205-247
-
Bokkapitel (refereegranskat)abstract
- Collaborative robotic systems have much to gain by leveraging results from the area of multi-agent systems and in particular agent-oriented software engineering. Agent-oriented software engineering has much to gain by using collaborative robotic systems as a testbed. In this article, we propose and specify a formally grounded generic collaborative system shell for robotic systems and human operated ground control systems. Collaboration is formalized in terms of the concept of delegation and delegation is instantiated as a speech act. Task Specification Trees are introduced as both a formal and pragmatic characterization of tasks and tasks are recursively delegated through a delegation process implemented in the collaborative system shell. The delegation speech act is formally grounded in the implementation using Task Specification Trees, task allocation via auctions and distributed constraint problem solving. The system is implemented as a prototype on Unmanned Aerial Vehicle systems and a case study targeting emergency service applications is presented.
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| 36. |
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| 37. |
- Doherty, Patrick, 1957-, et al.
(författare)
-
A Delegation-Based Cooperative Robotic Framework
- 2011
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Ingår i: Proceedings of the IEEE International Conference on Robotics and Biomimetic. - : IEEE conference proceedings. - 9781457721366 ; , s. 2955-2962
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Konferensbidrag (refereegranskat)abstract
- Cooperative robotic systems, such as unmanned aircraft systems, are becoming technologically mature enough to be integrated into civil society. To gain practical use and acceptance, a verifiable, principled and well-defined foundation for interactions between human operators and autonomous systems is needed. In this paper, we propose and specify such a formally grounded collaboration framework. Collaboration is formalized in terms of the concept of delegation and delegation is instantiated as a speech act. Task Specification Trees are introduced as both a formal and pragmatic characterization of tasks and tasks are recursively delegated through a delegation process. The delegation speech act is formally grounded in the implementation using Task Specification Trees, task allocation via auctions and distributed constraint solving. The system is implemented as a prototype on unmanned aerial vehicle systems and a case study targeting emergency service applications is presented.
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| 38. |
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| 39. |
- Doherty, Patrick, et al.
(författare)
-
A Distributed Task Specification Language for Mixed-Initiative Delegation
- 2012
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Ingår i: Principles and Practice of Multi-Agent Systems. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642259197 - 9783642259203 ; , s. 42-57
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Konferensbidrag (refereegranskat)abstract
- In the next decades, practically viable robotic/agent systems are going to be mixed-initiative in nature. Humans will request help from such systems and such systems will request help from humans in achieving the complex mission tasks required. Pragmatically, one requires a distributed task specification language to define tasks and a suitable data structure which satisfies the specification and can be used flexibly by collaborative multi-agent/robotic systems. This paper defines such a task specification language and an abstract data structure called Task Specification Trees which has many of the requisite properties required for mixed-initiative problem solving and adjustable autonomy in a distributed context. A prototype system has been implemented for this delegation framework and has been used practically with collaborative unmanned aircraft systems.
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| 40. |
- Doherty, Patrick, et al.
(författare)
-
A Temporal Logic-based Planning and Execution Monitoring Framework for Unmanned Aircraft Systems
- 2009
-
Ingår i: Autonomous Agents and Multi-Agent Systems. - : Springer. - 1387-2532 .- 1573-7454. ; 19:3, s. 332-377
-
Tidskriftsartikel (refereegranskat)abstract
- Research with autonomous unmanned aircraft systems is reaching a new degree of sophistication where targeted missions require complex types of deliberative capability integrated in a practical manner in such systems. Due to these pragmatic constraints, integration is just as important as theoretical and applied work in developing the actual deliberative functionalities. In this article, we present a temporal logic-based task planning and execution monitoring framework and its integration into a fully deployed rotor-based unmanned aircraft system developed in our laboratory. We use a very challenging emergency services application involving body identification and supply delivery as a vehicle for showing the potential use of such a framework in real-world applications. TALplanner, a temporal logic-based task planner, is used to generate mission plans. Building further on the use of TAL (Temporal Action Logic), we show how knowledge gathered from the appropriate sensors during plan execution can be used to create state structures, incrementally building a partial logical model representing the actual development of the system and its environment over time. We then show how formulas in the same logic can be used to specify the desired behavior of the system and its environment and how violations of such formulas can be detected in a timely manner in an execution monitor subsystem. The pervasive use of logic throughout the higher level deliberative layers of the system architecture provides a solid shared declarative semantics that facilitates the transfer of knowledge between different modules.
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| 41. |
- Doherty, Patrick, 1957-, et al.
(författare)
-
Delegation-Based Collaboration
- 2012
-
Ingår i: Proceedings of the 5th International Conference on Cognitive Systems (CogSys).
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)
|
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| 42. |
- Doherty, Patrick, et al.
(författare)
-
HDRC3 - A Distributed Hybrid Deliberative/Reactive Architecture for Unmanned Aircraft Systems
- 2014
-
Ingår i: Handbook of Unmanned Aerial Vehicles. - Dordrecht : Springer Science+Business Media B.V.. - 9789048197064 - 9789048197071 ; , s. 849-952
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter presents a distributed architecture for unmanned aircraft systems that provides full integration of both low autonomy and high autonomy. The architecture has been instantiated and used in a rotorbased aerial vehicle, but is not limited to use in particular aircraft systems. Various generic functionalities essential to the integration of both low autonomy and high autonomy in a single system are isolated and described. The architecture has also been extended for use with multi-platform systems. The chapter covers the full spectrum of functionalities required for operation in missions requiring high autonomy. A control kernel is presented with diverse flight modes integrated with a navigation subsystem. Specific interfaces and languages are introduced which provide seamless transition between deliberative and reactive capability and reactive and control capability. Hierarchical Concurrent State Machines are introduced as a real-time mechanism for specifying and executing low-level reactive control. Task Specification Trees are introduced as both a declarative and procedural mechanism for specification of high-level tasks. Task planners and motion planners are described which are tightly integrated into the architecture. Generic middleware capability for specifying data and knowledge flow within the architecture based on a stream abstraction is also described. The use of temporal logic is prevalent and is used both as a specification language and as an integral part of an execution monitoring mechanism. Emphasis is placed on the robust integration and interaction between these diverse functionalities using a principled architectural framework. The architecture has been empirically tested in several complex missions, some of which are described in the chapter.
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| 43. |
- Doherty, Patrick, et al.
(författare)
-
High-level Mission Specification and Planning for Collaborative Unmanned Aircraft Systems using Delegation
- 2013
-
Ingår i: Unmanned Systems. - : World Scientific. - 2301-3850 .- 2301-3869. ; 1:1, s. 75-119
-
Tidskriftsartikel (refereegranskat)abstract
- Automated specification, generation and execution of high level missions involving one or more heterogeneous unmanned aircraft systems is in its infancy. Much previous effort has been focused on the development of air vehicle platforms themselves together with the avionics and sensor subsystems that implement basic navigational skills. In order to increase the degree of autonomy in such systems so they can successfully participate in more complex mission scenarios such as those considered in emergency rescue that also include ongoing interactions with human operators, new architectural components and functionalities will be required to aid not only human operators in mission planning, but also the unmanned aircraft systems themselves in the automatic generation, execution and partial verification of mission plans to achieve mission goals. This article proposes a formal framework and architecture based on the unifying concept of delegation that can be used for the automated specification, generation and execution of high-level collaborative missions involving one or more air vehicles platforms and human operators. We describe an agent-based software architecture, a temporal logic based mission specification language, a distributed temporal planner and a task specification language that when integrated provide a basis for the generation, instantiation and execution of complex collaborative missions on heterogeneous air vehicle systems. A prototype of the framework is operational in a number of autonomous unmanned aircraft systems developed in our research lab.
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| 44. |
- Doherty, Patrick, et al.
(författare)
-
Research with Collaborative Unmanned Aircraft Systems
- 2010
-
Ingår i: Proceedings of the Dagstuhl Workshop on Cognitive Robotics. - : Leibniz-Zentrum für Informatik.
-
Konferensbidrag (refereegranskat)abstract
- We provide an overview of ongoing research which targets development of a principled framework for mixed-initiative interaction with unmanned aircraft systems (UAS). UASs are now becoming technologically mature enough to be integrated into civil society. Principled interaction between UASs and human resources is an essential component in their future uses in complex emergency services or bluelight scenarios. In our current research, we have targeted a triad of fundamental, interdependent conceptual issues: delegation, mixed- initiative interaction and adjustable autonomy, that is being used as a basis for developing a principled and well-defined framework for interaction. This can be used to clarify, validate and verify different types of interaction between human operators and UAS systems both theoretically and practically in UAS experimentation with our deployed platforms.
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| 45. |
- Doherty, Patrick, 1957-, et al.
(författare)
-
Robotics, Temporal Logic and Stream Reasoning
- 2013
-
Ingår i: Proceedings of Logic for Programming Artificial Intelligence and Reasoning (LPAR), 2013.
-
Konferensbidrag (refereegranskat)
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| 46. |
|
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| 47. |
- Engelsons, Daniel, et al.
(författare)
-
Coverage Path Planning in Large-scale Multi-floor Urban Environments with Applications to Autonomous Road Sweeping
- 2022
-
Ingår i: 2022 International Conference on Robotics and Automation (ICRA). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728196817 - 9781728196824 ; , s. 3328-3334
-
Konferensbidrag (refereegranskat)abstract
- Coverage Path Planning is the work horse of contemporary service task automation, powering autonomous floor cleaning robots and lawn mowers in households and office sites. While steady progress has been made on indoor cleaning and outdoor mowing, these environments are small and with simple geometry compared to general urban environments such as city parking garages, highway bridges or city crossings. To pave the way for autonomous road sweeping robots to operate in such difficult and complex environments, a benchmark suite with three large-scale 3D environments representative of this task is presented. On this benchmark we evaluate a new Coverage Path Planning method in comparison with previous well performing algorithms, and demonstrate state-of-the-art performance of the proposed method. Part of the success, for all evaluated algorithms, is the usage of automated domain adaptation by in-the-loop parameter optimization using Bayesian Optimization. Apart from improving the performance, tedious and bias-prone manual tuning is made obsolete, which makes the evaluation more robust and the results even stronger.
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| 48. |
- Felländer, Anna, et al.
(författare)
-
Achieving a Data-driven Risk Assessment Methodology for Ethical AI
- 2021
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The AI landscape demands a broad set of legal, ethical, and societal considerations to be accounted for in order to develop ethical AI (eAI) solutions which sustain human values and rights. Currently, a variety of guidelines and a handful of niche tools exist to account for and tackle individual challenges. However, it is also well established that many organizations face practical challenges in navigating these considerations from a risk management perspective. Therefore, new methodologies are needed to provide a well-vetted and real-world applicable structure and path through the checks and balances needed for ethically assessing and guiding the development of AI. In this paper we show that a multidisciplinary research approach, spanning cross-sectional viewpoints, is the foundation of a pragmatic definition of ethical and societal risks faced by organizations using AI. Equally important is the findings of cross-structural governance for implementing eAI successfully. Based on evidence acquired from our multidisciplinary research investigation, we propose a novel data-driven risk assessment methodology, entitled DRESS-eAI. In addition, through the evaluation of our methodological implementation, we demonstrate its state-of-the-art relevance as a tool for sustaining human values in the data-driven AI era.
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| 49. |
- Felländer, Anna, et al.
(författare)
-
Achieving a Data‐Driven Risk Assessment Methodology for Ethical AI
- 2022
-
Ingår i: Digital Society. - : Springer Science and Business Media LLC. - 2731-4669 .- 2731-4650. ; 1:2, s. 1-27
-
Tidskriftsartikel (refereegranskat)abstract
- The AI landscape demands a broad set of legal, ethical, and societal considerations to be accounted for in order to develop ethical AI (eAI) solutions which sustain human values and rights. Currently, a variety of guidelines and a handful of niche tools exist to account for and tackle individual challenges. However, it is also well established that many organizations face practical challenges in navigating these considerations from a risk management perspective within AI governance. Therefore, new methodologies are needed to provide a well-vetted and real-world applicable structure and path through the checks and balances needed for ethically assessing and guiding the development of AI. In this paper, we show that a multidisciplinary research approach, spanning cross-sectional viewpoints, is the foundation of a pragmatic definition of ethical and societal risks faced by organizations using AI. Equally important are the findings of cross-structural governance for implementing eAI successfully. Based on evidence acquired from our multidisciplinary research investigation, we propose a novel data-driven risk assessment methodology, entitled DRESS-eAI. In addition, through the evaluation of our methodological implementation, we demonstrate its state-of-the-art relevance as a tool for sustaining human values in the data-driven AI era.
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| 50. |
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