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Wiman, E., Widén, L., Tiger, M. & Heintz, F. (2024). Autonomous 3D Exploration in Large-Scale Environments with Dynamic Obstacles. In: : . Paper presented at International Conference on Robotics and Automation, Yokohama, Japan, 13-17 Maj, 2024..
Öppna denna publikation i ny flik eller fönster >>Autonomous 3D Exploration in Large-Scale Environments with Dynamic Obstacles
2024 (Engelska)Konferensbidrag, Poster (med eller utan abstract) (Övrigt vetenskapligt)
Abstract [en]

Exploration in dynamic and uncertain real-world environments is an open problem in robotics and it constitutes a foundational capability of autonomous systems operating in most of the real-world. While 3D exploration planning has been extensively studied, the environments are assumed static or only reactive collision avoidance is carried out. We propose a novel approach to not only avoid dynamic obstacles but also include them in the plan itself, to deliberately exploit the dynamic environment in the agent's favor. The proposed planner, Dynamic AutonomousExploration Planner (DAEP), extends AEP [1] to explicitly plan with respect to dynamic obstacles. Furthermore, addressing prior errors within AEP in DAEP has resulted in enhanced exploration within static environments. To thoroughly evaluate exploration planners in dynamic settings, we propose a new enhanced benchmark suite with several dynamic environments, including large-scale outdoor environments. DAEP outperforms state-of-the-art planners in dynamic and large-scale environments and is shown to be more effective at both exploration and collision avoidance.

Nyckelord
3D-exploration, dynamic environments, planning under uncertainty, collision avoidance
Nationell ämneskategori
Datorgrafik och datorseende
Identifikatorer
urn:nbn:se:liu:diva-205049 (URN)
Konferens
International Conference on Robotics and Automation, Yokohama, Japan, 13-17 Maj, 2024.
Forskningsfinansiär
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Tillgänglig från: 2024-06-19 Skapad: 2024-06-19 Senast uppdaterad: 2025-02-07Bibliografiskt granskad
Wiman, E., Widén, L., Tiger, M. & Heintz, F. (2024). Autonomous 3D Exploration in Large-Scale Environments with Dynamic Obstacles. In: Zhidong Wang (Ed.), 2024 IEEE International Conference on Robotics and Automation (ICRA): . Paper presented at IEEE International Conference on Robotics and Automation (ICRA) 2024, 13-17 Maj 2024, Yokohama, Japan (pp. 2389-2395). IEEE
Öppna denna publikation i ny flik eller fönster >>Autonomous 3D Exploration in Large-Scale Environments with Dynamic Obstacles
2024 (Engelska)Ingår i: 2024 IEEE International Conference on Robotics and Automation (ICRA) / [ed] Zhidong Wang, IEEE, 2024, s. 2389-2395Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Exploration in dynamic and uncertain real-world environments is an open problem in robotics and it constitutes a foundational capability of autonomous systems operating in most of the real-world. While 3D exploration planning has been extensively studied, the environments are assumed static or only reactive collision avoidance is carried out. We propose a novel approach to not only avoid dynamic obstacles but also include them in the plan itself, to deliberately exploit the dynamic environment in the agent’s favor. The proposed planner, Dynamic Autonomous Exploration Planner (DAEP), extends AEP to explicitly plan with respect to dynamic obstacles. Furthermore, addressing prior errors within AEP in DAEP has resulted in enhanced exploration within static environments. To thoroughly evaluate exploration planners in such settings we propose a new enhanced benchmark suite with several dynamic environments, including large-scale outdoor environments. DAEP outperforms state-of-the-art planners in dynamic and large-scale environments and is shown to be more effective at both exploration and collision avoidance.

Ort, förlag, år, upplaga, sidor
IEEE, 2024
Nyckelord
3D-exploration, dynamic environments, planning under uncertainty, collision avoidance
Nationell ämneskategori
Datorgrafik och datorseende
Identifikatorer
urn:nbn:se:liu:diva-206793 (URN)10.1109/ICRA57147.2024.10610996 (DOI)001294576202005 ()2-s2.0-85202446874 (Scopus ID)9798350384574 (ISBN)9798350384581 (ISBN)
Konferens
IEEE International Conference on Robotics and Automation (ICRA) 2024, 13-17 Maj 2024, Yokohama, Japan
Forskningsfinansiär
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Anmärkning

Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation; Excellence Center at Linkoping-Lund in Information Technology (ELLIIT)

Tillgänglig från: 2024-08-22 Skapad: 2024-08-22 Senast uppdaterad: 2025-02-07Bibliografiskt granskad
Ramachandranpillai, R., Sikder, M. F., Bergström, D. & Heintz, F. (2024). Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks. The journal of artificial intelligence research, 79, 1313-1341
Öppna denna publikation i ny flik eller fönster >>Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks
2024 (Engelska)Ingår i: The journal of artificial intelligence research, ISSN 1076-9757, E-ISSN 1943-5037, Vol. 79, s. 1313-1341Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Synthetic data generation offers a promising solution to enhance the usefulness of Electronic Healthcare Records (EHR) by generating realistic de-identified data. However, the existing literature primarily focuses on the quality of synthetic health data, neglecting the crucial aspect of fairness in downstream predictions. Consequently, models trained on synthetic EHR have faced criticism for producing biased outcomes in target tasks. These biases can arise from either spurious correlations between features or the failure of models to accurately represent sub-groups. To address these concerns, we present Bias-transforming Generative Adversarial Networks (Bt-GAN), a GAN-based synthetic data generator specifically designed for the healthcare domain. In order to tackle spurious correlations (i), we propose an information-constrained Data Generation Process (DGP) that enables the generator to learn a fair deterministic transformation based on a well-defined notion of algorithmic fairness. To overcome the challenge of capturing exact sub-group representations (ii), we incentivize the generator to preserve sub-group densities through score-based weighted sampling. This approach compels the generator to learn from underrepresented regions of the data manifold. To evaluate the effectiveness of our proposed method, we conduct extensive experiments using the Medical Information Mart for Intensive Care (MIMIC-III) database. Our results demonstrate that Bt-GAN achieves state-of-the-art accuracy while significantly improving fairness and minimizing bias amplification. Furthermore, we perform an in-depth explainability analysis to provide additional evidence supporting the validity of our study. In conclusion, our research introduces a novel and professional approach to addressing the limitations of synthetic data generation in the healthcare domain. By incorporating fairness considerations and leveraging advanced techniques such as GANs, we pave the way for more reliable and unbiased predictions in healthcare applications.

Ort, förlag, år, upplaga, sidor
AAAI Press, 2024
Nyckelord
Fair data generation, Trustworthy AI, Synthetic data generation, MIMIC-III, EHR
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:liu:diva-203151 (URN)10.1613/jair.1.15317 (DOI)001218386100001 ()
Anmärkning

Funding Agencies|Knut and Alice Wallenberg Foundation; ELLIIT Excellence Center at Linkoeping-Lund for Information Technology; TAILOR-an EU project

Tillgänglig från: 2024-04-30 Skapad: 2024-04-30 Senast uppdaterad: 2024-05-22Bibliografiskt granskad
Carlsen, H., Nykvist, B., Joshi, S. & Heintz, F. (2024). Chasing artificial intelligence in shared socioeconomic pathways. One Earth, 7(1), 18-22
Öppna denna publikation i ny flik eller fönster >>Chasing artificial intelligence in shared socioeconomic pathways
2024 (Engelska)Ingår i: One Earth, ISSN 2590-3330, E-ISSN 2590-3322, Vol. 7, nr 1, s. 18-22Artikel i tidskrift, Editorial material (Övrigt vetenskapligt) Published
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
CELL PRESS, 2024
Nationell ämneskategori
Tvärvetenskapliga studier inom samhällsvetenskap
Identifikatorer
urn:nbn:se:liu:diva-201493 (URN)10.1016/j.oneear.2023.12.015 (DOI)001171139600001 ()
Anmärkning

Funding Agencies|Mistra Geopolitics research program [2016/11]

Tillgänglig från: 2024-03-12 Skapad: 2024-03-12 Senast uppdaterad: 2024-07-30
Sikder, M. F., Ramachandranpillai, R., de Leng, D. & Heintz, F. (2024). FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability. In: Roberta Calegari,Virginia Dignum, Barry O'Sullivan (Ed.), Proceedings of the 2nd Workshop on Fairness and Bias in AI, co-located with 27th European Conference on Artificial Intelligence (ECAI 2024): . Paper presented at 2nd Workshop on Fairness and Bias in AI (AEQUITAS), co-located with 27th European Conference on Artificial Intelligence (ECAI 2024). CEUR, 3808, Article ID 16.
Öppna denna publikation i ny flik eller fönster >>FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability
2024 (Engelska)Ingår i: Proceedings of the 2nd Workshop on Fairness and Bias in AI, co-located with 27th European Conference on Artificial Intelligence (ECAI 2024) / [ed] Roberta Calegari,Virginia Dignum, Barry O'Sullivan, CEUR , 2024, Vol. 3808, artikel-id 16Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

We present FairX, an open-source Python-based benchmarking tool designed for the comprehensive analysis of models under the umbrella of fairness, utility, and eXplainability (XAI). FairX enables users to train benchmarking bias-mitigation models and evaluate their fairness using a wide array of fairness metrics, data utility metrics, and generate explanations for model predictions, all within a unified framework. Existing benchmarking tools do not have the way to evaluate synthetic data generated from fair generative models, also they do not have the support for training fair generative models either. In FairX, we add fair generative models in the collection of our fair-model library (pre-processing, in-processing, post-processing) and evaluation metrics for evaluating the quality of synthetic fair data. This version of FairX supports both tabular and image datasets. It also allows users to provide their own custom datasets. The open-source FairX benchmarking package is publicly available at https://github.com/fahim-sikder/FairX.

Ort, förlag, år, upplaga, sidor
CEUR, 2024
Serie
CEUR Workshop Proceedings, ISSN 1613-0073
Nyckelord
Data Fairness, Benchmarking, Synthetic Data, Evaluation
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:liu:diva-209224 (URN)
Konferens
2nd Workshop on Fairness and Bias in AI (AEQUITAS), co-located with 27th European Conference on Artificial Intelligence (ECAI 2024)
Forskningsfinansiär
Knut och Alice Wallenbergs Stiftelse
Tillgänglig från: 2024-11-06 Skapad: 2024-11-06 Senast uppdaterad: 2024-11-15Bibliografiskt granskad
Bonte, P., Calbimonte, J.-P., de Leng, D., Dell'Aglio, D., Della Valle, E., Eiter, T., . . . Ziffer, G. (2024). Grounding Stream Reasoning Research. Transactions on Graph Data and Knowledge (TGDK), 2(1), 1-47, Article ID 2.
Öppna denna publikation i ny flik eller fönster >>Grounding Stream Reasoning Research
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2024 (Engelska)Ingår i: Transactions on Graph Data and Knowledge (TGDK), ISSN 2942-7517, Vol. 2, nr 1, s. 1-47, artikel-id 2Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Wadern, Germany: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, 2024
Nyckelord
Stream Reasoning, Stream Processing, RDF streams, Streaming Linked Data, Continuous query processing, Temporal Logics, High-performance computing, Databases
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:liu:diva-203211 (URN)10.4230/TGDK.2.1.2 (DOI)
Tillgänglig från: 2024-05-03 Skapad: 2024-05-03 Senast uppdaterad: 2024-05-08Bibliografiskt granskad
Sperling, K., Stenberg, C.-J., Mcgrath, C., Akerfeldt, A., Heintz, F. & Stenliden, L. (2024). In search of artificial intelligence (AI) literacy in teacher education: A scoping review. COMPUTERS AND EDUCATION OPEN, 6, Article ID 100169.
Öppna denna publikation i ny flik eller fönster >>In search of artificial intelligence (AI) literacy in teacher education: A scoping review
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2024 (Engelska)Ingår i: COMPUTERS AND EDUCATION OPEN, ISSN 2666-5573, Vol. 6, artikel-id 100169Artikel, forskningsöversikt (Refereegranskat) Published
Abstract [en]

Artificial intelligence (AI) literacy has recently emerged on the educational agenda raising expectations on teachers' and teacher educators' professional knowledge. This scoping review examines how the scientific literature conceptualises AI literacy in relation to teachers' different forms of professional knowledge relevant for Teacher Education (TE). The search strategy included papers and proceedings from 2000 to 2023 related to AI literacy and TE as well as the intersection of AI and teaching. Thirty-four papers were included in the analysis. The Aristotelian concepts episteme (theoretical-scientific knowledge), techne (practical-productive knowledge), and phronesis (professional judgement) were used as a lens to capture implicit and explicit dimensions of teachers' professional knowledge. Results indicate that AI literacy is a globally emerging research topic in education but almost absent in the context of TE. The literature covers many different topics and draws on different methodological approaches. Computer science and exploratory teaching approaches influence the type of epistemic, practical, and ethical knowledge. Currently, teachers' professional knowledge is not broadly addressed or captured in the research. Questions of ethics are predominantly addressed as a matter of understanding technical configurations of data-driven AI technologies. Teachers' practical knowledge tends to translate into the adoption of digital resources for teaching about AI or the integration of AI EdTech into teaching. By identifying several research gaps, particularly concerning teachers' practical and ethical knowledge, this paper adds to a more comprehensive understanding of AI literacy in teaching and can contribute to a more wellinformed AI literacy education in TE as well as laying the ground for future research related to teachers' professional knowledge.

Ort, förlag, år, upplaga, sidor
ELSEVIER, 2024
Nyckelord
AI education; Professional development; Teacher training; Aristoteles; AI readiness; Pre -service teachers
Nationell ämneskategori
Pedagogiskt arbete
Identifikatorer
urn:nbn:se:liu:diva-203734 (URN)10.1016/j.caeo.2024.100169 (DOI)001224342800001 ()
Tillgänglig från: 2024-05-27 Skapad: 2024-05-27 Senast uppdaterad: 2024-12-16
Hachem, H.-H. & Heintz, F. (2024). Where is the reflexive ‘I’ in the Elements of AI?. International Journal of Lifelong Education
Öppna denna publikation i ny flik eller fönster >>Where is the reflexive ‘I’ in the Elements of AI?
2024 (Engelska)Ingår i: International Journal of Lifelong Education, ISSN 0260-1370, E-ISSN 1464-519XArtikel i tidskrift (Refereegranskat) Epub ahead of print
Abstract [en]

AI's opportunities and potential high-risk consequences for individuals and societies render mass AI literacy imperative. MOOCs are one effective conduit for its provision. However, MOOCs remain epistemologically one-sided when lifelong learning steadily shifts towards a reflexive epistemology whereby subjectivities and expert knowledge intersect, problematising the latter's relevance to agents when disregarding the first. Addressing the underexplored epistemologies of AI literacy MOOCs and kindled by transformative learning in late modernity, this paper examines how the design of the MOOC Elements of AI prompts reflexivity over AI. A Bloom's taxonomy-based qualitative content analysis categorised 16 learning objectives and 25 assessments according to cognitive processes and knowledge dimensions they serve. Results showed adequate but delayed instruction for reflexivity and a benign constructive misalignment, with assessment hitting higher and wider processes and dimensions than the learning objectives. Following the fleshing out of results, their discussion leads to EAI-specific and general enhancements for identity-based transformative AI literacy MOOCs catering to scale and individuality.

Ort, förlag, år, upplaga, sidor
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD, 2024
Nyckelord
AI literacy; epistemologies of lifelong learning; MOOC
Nationell ämneskategori
Didaktik
Identifikatorer
urn:nbn:se:liu:diva-206787 (URN)10.1080/02601370.2024.2389107 (DOI)001289437600001 ()2-s2.0-85201060509 (Scopus ID)
Forskningsfinansiär
Vinnova, ADAPT
Anmärkning

Funding Agencies|VINNOVA, Sweden's innovation agency grant

Tillgänglig från: 2024-08-21 Skapad: 2024-08-21 Senast uppdaterad: 2025-02-07
Hayes, C. F., Rădulescu, R., Bargiacchi, E., Källström, J., Macfarlane, M., Reymond, M., . . . Roijers, D. M. (2023). A Brief Guide to Multi-Objective Reinforcement Learning and Planning. In: A. Ricci, W. Yeoh, N. Agmon, B. An (Ed.), Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS): . Paper presented at International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (pp. 1988-1990).
Öppna denna publikation i ny flik eller fönster >>A Brief Guide to Multi-Objective Reinforcement Learning and Planning
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2023 (Engelska)Ingår i: Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS) / [ed] A. Ricci, W. Yeoh, N. Agmon, B. An, 2023, s. 1988-1990Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Real-world sequential decision-making tasks are usually complex, and require trade-offs between multiple–often conflicting–objectives. However, the majority of research in reinforcement learning (RL) and decision-theoretic planning assumes a single objective, or that multiple objectives can be handled via a predefined weighted sum over the objectives. Such approaches may oversimplify the underlying problem, and produce suboptimal results. This extended abstract outlines the limitations of using a semi-blind iterative process to solve multi-objective decision making problems. Our extended paper [4], serves as a guide for the application of explicitly multi-objective methods to difficult problems.

Nyckelord
Multi-Objective, Reinforcement Learning, Planning
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:liu:diva-194556 (URN)978-1-4503-9432-1 (ISBN)
Konferens
International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
Forskningsfinansiär
Vinnova, NFFP7/2017-04885Wallenberg AI, Autonomous Systems and Software Program (WASP)
Tillgänglig från: 2023-06-08 Skapad: 2023-06-08 Senast uppdaterad: 2023-06-08
Sperling, K., Stenliden, L., Nissen, J. & Heintz, F. (2023). Behind the Scenes of Co-designing AI and LA in K-12 Education. Postdigital Science and Education, 6, 321-341
Öppna denna publikation i ny flik eller fönster >>Behind the Scenes of Co-designing AI and LA in K-12 Education
2023 (Engelska)Ingår i: Postdigital Science and Education, ISSN 2524-485X, Vol. 6, s. 321-341Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

This article explores the complex challenges of co-designing an AI- and learning analytics (LA)-integrated learning management system (LMS). While co-design has been proposed as a human-centred design approach for scaling AI and LA adoption, our understanding of how these design processes play out in real-life settings remains limited. This study is based on ethnographic fieldwork in primary and secondary schools and employs a relational materialist approach to trace, visualise, and analyse the increasingly complex and transformative relations between a growing number of actors. The findings shed light on the intricate ecosystem in which AI and LA are being introduced and on the marketisation of K-12 education. Instead of following a rational and sequential approach that can be easily executed, the co-design process emerged as a series of events, shifting from solely generating ideas with teachers to integrating and commercialising the LMS into a school market with an already high prevalence of educational technology (EdTech). AI and LA in education, co-design and data-driven schooling served as negotiating ideas, boundary objects, which maintained connectivity between actors, despite limited AI and LA implementation and the development of a stand-alone app. Even though teachers and students were actively involved in the design decisions, the co-design process did not lead to extensive adoption of the LMS nor did it sufficiently address the ethical issues related to the unrestricted collection of student data.

Nyckelord
Actor-network theory · AI in K-12 · Boundary objects · Co-design · Human-centred design · Learning analytics · Networks · Relationism
Nationell ämneskategori
Pedagogiskt arbete
Identifikatorer
urn:nbn:se:liu:diva-197678 (URN)10.1007/s42438-023-00417-5 (DOI)
Forskningsfinansiär
Linköpings universitet
Tillgänglig från: 2023-09-06 Skapad: 2023-09-06 Senast uppdaterad: 2024-12-17
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-9595-2471

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