| 11. |
- Andersson, Olov, 1979-, et al.
(författare)
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Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization
- 2015
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Ingår i: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI). - : AAAI Press. - 9781577356981 ; , s. 2497-2503
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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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| 12. |
- Andersson, Olov, 1979-, et al.
(författare)
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Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance
- 2018
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Ingår i: 2018 IEEE Conference on Decision and Control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538613955 - 9781538613948 - 9781538613962 ; , s. 4467-4474
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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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| 13. |
- 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.
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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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| 14. |
- Bergström, David, 1994-, et al.
(författare)
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Bayesian optimization for selecting training and validation data for supervised machine learning
- 2019
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Ingår i: 31st annual workshop of the Swedish Artificial Intelligence Society (SAIS 2019), Umeå, Sweden, June 18-19, 2019..
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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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| 15. |
- Bonte, Pieter, et al.
(författare)
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Grounding Stream Reasoning Research
- 2024
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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
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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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| 16. |
- 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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| 17. |
- de Leng, Daniel, 1988-, et al.
(författare)
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Approximate Stream Reasoning with Metric Temporal Logic under Uncertainty
- 2019
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Ingår i: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI). - Palo Alto : AAAI Press. ; , s. 2760-2767
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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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| 18. |
- de Leng, Daniel, 1988-
(författare)
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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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| 19. |
- de Leng, Daniel, 1988-
(författare)
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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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| 20. |
- Doherty, Patrick, 1957-, et al.
(författare)
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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
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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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