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Vellenga, K., Steinhauer, H. J., Falkman, G. & Björklund, T. (2024). Evaluation of Video Masked Autoencoders' Performance and Uncertainty Estimations for Driver Action and Intention Recognition. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV): . Paper presented at IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), January 4-8, 2024, Waikoloha, Hawaii, USA (pp. 7429-7437). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Evaluation of Video Masked Autoencoders' Performance and Uncertainty Estimations for Driver Action and Intention Recognition
2024 (engelsk)Inngår i: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, 2024, s. 7429-7437Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Traffic fatalities remain among the leading death causes worldwide. To reduce this figure, car safety is listed as one of the most important factors. To actively support human drivers, it is essential for advanced driving assistance systems to be able to recognize the driver's actions and intentions. Prior studies have demonstrated various approaches to recognize driving actions and intentions based on in-cabin and external video footage. Given the performance of self-supervised video pre-trained (SSVP) Video Masked Autoencoders (VMAEs) on multiple action recognition datasets, we evaluate the performance of SSVP VMAEs on the Honda Research Institute Driving Dataset for driver action recognition (DAR) and on the Brain4Cars dataset for driver intention recognition (DIR). Besides the performance, the application of an artificial intelligence system in a safety-critical environment must be capable to express when it is uncertain about the produced results. Therefore, we also analyze uncertainty estimations produced by a Bayes-by-Backprop last-layer (BBB-LL) and Monte-Carlo (MC) dropout variants of an VMAE. Our experiments show that an VMAE achieves a higher overall performance for both offline DAR and end-to-end DIR compared to the state-of-the-art. The analysis of the BBB-LL and MC dropout models show higher uncertainty estimates for incorrectly classified test instances compared to correctly predicted test instances.

sted, utgiver, år, opplag, sider
IEEE, 2024
Serie
Proceedings IEEE Workshop on Applications of Computer Vision, ISSN 2472-6737, E-ISSN 2642-9381
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-23540 (URN)10.1109/WACV57701.2024.00726 (DOI)979-8-3503-1893-7 (ISBN)979-8-3503-1892-0 (ISBN)
Konferanse
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), January 4-8, 2024, Waikoloha, Hawaii, USA
Forskningsfinansiär
Vinnova, 2018-05012
Tilgjengelig fra: 2024-01-16 Laget: 2024-01-16 Sist oppdatert: 2024-04-11bibliografisk kontrollert
Vellenga, K., Karlsson, A., Steinhauer, H. J., Falkman, G. & Sjögren, A. (2023). Surrogate Deep Learning to Estimate Uncertainties for Driver Intention Recognition. In: ICMLC 2023: Proceedings of 2023 15th International Conference on Machine Learning and Computing, Zhuhai, China, February 17-20, 2023. Paper presented at 15th International Conference on Machine Learning and Computing, Zhuhai, China, February 17-20, 2023 (pp. 252-258). New York, NY, USA: Association for Computing Machinery (ACM)
Åpne denne publikasjonen i ny fane eller vindu >>Surrogate Deep Learning to Estimate Uncertainties for Driver Intention Recognition
Vise andre…
2023 (engelsk)Inngår i: ICMLC 2023: Proceedings of 2023 15th International Conference on Machine Learning and Computing, Zhuhai, China, February 17-20, 2023, New York, NY, USA: Association for Computing Machinery (ACM), 2023, s. 252-258Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Real-world applications of artificial intelligence that can potentially harm human beings should be able to express uncertainty about the made predictions. Probabilistic deep learning (DL) methods (e.g., variational inference [VI], VI last layer [VI-LL], Monte-Carlo [MC] dropout, stochastic weight averaging - Gaussian [SWA-G], and deep ensembles) can produce a predictive uncertainty but require expensive MC sampling techniques. Therefore, we evaluated if the probabilistic DL methods are uncertain when making incorrect predictions for an open-source driver intention recognition dataset and if a surrogate DL model can reproduce the uncertainty estimates. We found that all probabilistic DL methods are significantly more uncertain when making incorrect predictions at test time, but there are still instances where the models are very certain but completely incorrect. The surrogate DL models trained on the MC dropout and VI uncertainty estimates were capable of reproducing a significantly higher uncertainty estimate when making incorrect predictions.

sted, utgiver, år, opplag, sider
New York, NY, USA: Association for Computing Machinery (ACM), 2023
Emneord
Driver intention recognition, probabilistic deep learning, surrogate modeling, uncertainty quantification
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-22851 (URN)10.1145/3587716.3587758 (DOI)2-s2.0-85173817744 (Scopus ID)978-1-4503-9841-1 (ISBN)
Konferanse
15th International Conference on Machine Learning and Computing, Zhuhai, China, February 17-20, 2023
Prosjekter
Intention recognition for real-time automotive 3D situation awareness
Forskningsfinansiär
Vinnova, 2018-05012
Merknad

CC BY-NC-SA 4.0

CORRESPONDING AUTHOR: K. VELLENGA (e-mail: koen.vellenga@volvocars.com)

This work was supported by the Intention Recognition in Real Time for Automotive 3D Situation Awareness (IRRA) Project (https://www.vinnova.se/p/intention-recognition-i-realtid-for-automotive-3d-situation-awareness-irra/).

Tilgjengelig fra: 2023-06-27 Laget: 2023-06-27 Sist oppdatert: 2023-10-26bibliografisk kontrollert
Steinhauer, H. J., Helldin, T., Mathiason, G. & Karlsson, A. (2023). Topic modeling for anomaly detection in telecommunication networks. Journal of Ambient Intelligence and Humanized Computing, 14(11), 15085-15096
Åpne denne publikasjonen i ny fane eller vindu >>Topic modeling for anomaly detection in telecommunication networks
2023 (engelsk)Inngår i: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 14, nr 11, s. 15085-15096Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

To ensure reliable network performance, anomaly detection is an important part of the telecommunication operators’ work. This includes that operators need to timely intervene with the network, should they encounter indications of network performance degradation. In this paper, we describe the results of an initial experiment for anomaly detection with regard to network performance, using topic modeling on base station run-time variable data collected from live Radio Access Networks (RANs). The results show that topic modeling clusters semantically related data in the same way as human experts would and that the anomalies in our test cases could be identified in latent Dirichlet allocation (LDA) topic models. Our experiment further reveals which information provided by the topic model is particularly usable to support human anomaly detection in this application domain.

sted, utgiver, år, opplag, sider
Springer, 2023
Emneord
Telecommunication anomaly detection, Topic modeling, Decision-making
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-17527 (URN)10.1007/s12652-019-01372-5 (DOI)2-s2.0-85182305640 (Scopus ID)
Prosjekter
bison
Forskningsfinansiär
University of SkövdeKnowledge Foundation
Merknad

CC BY 4.0

Received: 31 January 2019 / Accepted: 18 June 2019 / Published online: 2 August 2019

H. Joe Steinhauer joe.steinhauer@his.se

Open access funding provided by University of Skövde. This work was supported by the Swedish Knowledge Foundation under grant BISON—Big Data Fusion—in cooperation with Huawei Technologies Sweden AB. We would like to thank Anders Åhlén for sharing his knowledge throughout our work. The topic modeling was performed using the package topicmodels (Grün and Hornik 2011) in R (R Core Team 2017), and the LDAvis visualization was enabled by Sievert and Shirley (2014).

Tilgjengelig fra: 2019-08-13 Laget: 2019-08-13 Sist oppdatert: 2024-01-26bibliografisk kontrollert
Vellenga, K., Steinhauer, H. J., Karlsson, A., Falkman, G., Rhodin, A. & Koppisetty, A. C. (2022). Driver intention recognition: state-of-the-art review. IEEE Open Journal of Intelligent Transportation Systems, 3, 602-616
Åpne denne publikasjonen i ny fane eller vindu >>Driver intention recognition: state-of-the-art review
Vise andre…
2022 (engelsk)Inngår i: IEEE Open Journal of Intelligent Transportation Systems, E-ISSN 2687-7813, Vol. 3, s. 602-616Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

Every year worldwide more than one million people die and a further 50 million people are injured in traffic accidents. Therefore, the development of car safety features that actively support the driver in preventing accidents, is of utmost importance to reduce the number of injuries and fatalities. However, to establish this support it is necessary that the advanced driver assistance system (ADAS) understands the driver’s intended behavior in advance. The growing variety of sensors available for vehicles together with improved computer vision techniques, hence led to increased research directed towards inferring the driver’s intentions. This article reviews 64 driver intention recognition studies with regard to the maneuvers considered, the driving features included, the AI methods utilized, the achieved performance within the presented experiments, and the open challenges identified by the respected researchers. The article provides a high level analysis of the current technology readiness level of driver intention recognition technology to address the challenges to enable reliable driver intention recognition, such as the system architecture, implementation, and the purpose of the technology.

sted, utgiver, år, opplag, sider
IEEE, 2022
Emneord
Driver intentions, intention recognition, driver behavior, driving maneuvers
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-21812 (URN)10.1109/ojits.2022.3197296 (DOI)000853832800001 ()2-s2.0-85147393634 (Scopus ID)
Prosjekter
Intention recognition for real-time automotive 3D situation awareness
Forskningsfinansiär
Vinnova, 2018-05012
Merknad

CC BY-NC-ND 4.0

CORRESPONDING AUTHOR: K. VELLENGA (e-mail: koen.vellenga@volvocars.com)

This work was supported by the Intention Recognition in Real Time for Automotive 3D Situation Awareness (IRRA) Project (https://www.vinnova.se/p/intention-recognition-i-realtid-for-automotive-3d-situation-awareness-irra/).

Tilgjengelig fra: 2022-09-12 Laget: 2022-09-12 Sist oppdatert: 2023-02-16bibliografisk kontrollert
Hamed, O. & Steinhauer, H. J. (2021). Pedestrian Intention Recognition and Action Prediction Using a Feature Fusion Deep Learning Approach. In: Vicenç Torra; Yasuo Narukawa (Ed.), USB Proceedings The 18th International Conference on Modeling Decisions for Artificial Intelligence: MDAI 2021, Umeå. Paper presented at The 18th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2021, Umeå, Sweden, September 27–30, 2021 (pp. 89-100).
Åpne denne publikasjonen i ny fane eller vindu >>Pedestrian Intention Recognition and Action Prediction Using a Feature Fusion Deep Learning Approach
2021 (engelsk)Inngår i: USB Proceedings The 18th International Conference on Modeling Decisions for Artificial Intelligence: MDAI 2021, Umeå / [ed] Vicenç Torra; Yasuo Narukawa, 2021, s. 89-100Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Recognizing Pedestrians intention to cross a street and predicting their imminent crossing action are major challenges for advanced driver assistance systems (ADAS) and Autonomous Vehicles (AV). In this paper we address these problems by proposing a new neural network architecture that uses feature fusion. The approach is used to recog[1]nise/predict the pedestrians intention/action 1.5 sec (45 frames) ahead. We evaluate our approach on the recently suggested benchmark by Rasouli et al. and show that our approach outperforms current state of the art models. We observe further improved results when the model is trained and tested on a stronger balanced subset of the PIE dataset.

Emneord
Intention Recognition, ADAS, Deep Learning, Feature Fusion
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-20644 (URN)978-91-527-1027-2 (ISBN)
Konferanse
The 18th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2021, Umeå, Sweden, September 27–30, 2021
Merknad

Supporting Institutions:

Department of Computing Sciences, Umeå University

The European Society for Fuzzy Logic and Technology (EUSFLAT)

The Catalan Association for Artificial Intelligence (ACIA)

The Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)

The UNESCO Chair in Data Privacy 

Tilgjengelig fra: 2021-10-13 Laget: 2021-10-13 Sist oppdatert: 2022-04-11bibliografisk kontrollert
Hamed, O. & Steinhauer, H. J. (2021). Pedestrian’s Intention Recognition, Fusion of Handcrafted Features in a Deep Learning Approach. In: AAAI-21 / IAAI-21 / EAAI-21 Proceedings: A virtual conference February 2-9, 2021: Thirty-Fifth AAAI Conference on Artificial Intelligence, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, The Eleventh Symposium on Educational Advances in Artificial Intelligence. Paper presented at AAAI-21 Student Abstract and Poster Program, [track/issue 18 of the] Thirty-Fifth AAAI Conference on Artificial Intelligence, held virtually February 2-9, 2021 (pp. 15795-15796). Palo Alto: AAAI Press
Åpne denne publikasjonen i ny fane eller vindu >>Pedestrian’s Intention Recognition, Fusion of Handcrafted Features in a Deep Learning Approach
2021 (engelsk)Inngår i: AAAI-21 / IAAI-21 / EAAI-21 Proceedings: A virtual conference February 2-9, 2021: Thirty-Fifth AAAI Conference on Artificial Intelligence, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, The Eleventh Symposium on Educational Advances in Artificial Intelligence, Palo Alto: AAAI Press, 2021, s. 15795-15796Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The safety of vulnerable road users (VRU) is a major concernfor both advanced driver assistance systems (ADAS) and autonomousvehicle manufacturers. To guarantee people safetyon roads, autonomous vehicles must be able to detect thepresence of pedestrians, track them, and predict their intentionto cross the road. Most of the earlier work on pedestrianintention recognition focused on using either handcraftedfeatures or an end-to-end deep learning approach. In thisproject, we investigate the impact of fusing handcrafted featureswith auto learned features by using a two-stream neuralnetwork architecture. Our results show that the combined approachimproves the performance. Furthermore, the proposedmethod achieved very good results on the JAAD dataset. Dependingon whether we considered the immediate frames beforethe crossing or only half a second before that point, wereceived prediction accuracy of 91%, and 84%, respectively.

sted, utgiver, år, opplag, sider
Palo Alto: AAAI Press, 2021
Serie
Proceedings of the AAAI Conference on Artificial Intelligence, ISSN 2159-5399, E-ISSN 2374-3468 ; 35(18)
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-19533 (URN)000681269807095 ()2-s2.0-85130070574 (Scopus ID)978-1-57735-866-4 (ISBN)
Konferanse
AAAI-21 Student Abstract and Poster Program, [track/issue 18 of the] Thirty-Fifth AAAI Conference on Artificial Intelligence, held virtually February 2-9, 2021
Forskningsfinansiär
Vinnova, 2018-05012
Merknad

Association for the Advancement of Artificial Intelligence

VINNOVA, the Swedish Government Agency for Innovation Systems, proj. "Intention Recognition for Real-time Automotive 3D situation awareness (IRRA)", in the funding program FFI: Strategic Vehicle Research and Innovation DNR 2018-05012

[även poster]

Tilgjengelig fra: 2021-03-12 Laget: 2021-03-12 Sist oppdatert: 2022-07-12bibliografisk kontrollert
Steinhauer, H. J., Åhlén, A., Helldin, T., Karlsson, A. & Mathiason, G. (2020). Increased Network Monitoring Support through Topic Modeling. International Journal of Information, Communication Technology and Applications, 6(1)
Åpne denne publikasjonen i ny fane eller vindu >>Increased Network Monitoring Support through Topic Modeling
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2020 (engelsk)Inngår i: International Journal of Information, Communication Technology and Applications, E-ISSN 2205-0930, Vol. 6, nr 1Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

To ensure that a wireless telecommunication system is reliably functioning at all times, root-causes of potential network failures need to be identified and remedied, ideally before a noticeable network performance degradation occurs. Network operators are today observing a multitude of key performance indicators (KPIs) and are notified of possible network problems through alarms issued by different parts of the network. However, the number of cascading alarms together with the number of observable KPIs are easily overwhelming the operator’s cognitive capacity. In this paper we show how exploratory data analysis and machine learning, in particular topic modelling, can assist the operator when monitoring network performance and identifying anomalous network behaviour as well as supporting the operator’s analysis of the anomaly and identification of its root-cause. 

sted, utgiver, år, opplag, sider
Australasian Association for Information and Communication Technology, 2020
Emneord
topic modelling, exploratory data analysis, anomaly detection, root cause identification, telecommunication networks, network performance monitoring
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-19532 (URN)
Forskningsfinansiär
Knowledge Foundation
Merknad

CC BY-NC-ND 4.0

Copyright © Australasian Association for Information and Communication Technology General permission to republish, but not for profit, all or part of this material is granted, under the Creative Commons Australian Attribution-NonCommercial-NoDerivs 4.0 Licence, provided that the copyright notice is given and that reference is made to the publication, to its date of issue, and to the fact that reprinting privileges were granted by permission of the Copyright holder.

Tilgjengelig fra: 2021-03-12 Laget: 2021-03-12 Sist oppdatert: 2021-04-26bibliografisk kontrollert
Darwish, A. & Steinhauer, H. J. (2020). Learning Individual Driver’s Mental Models Using POMDPs and BToM. In: Lars Hanson, Dan Högberg, Erik Brolin (Ed.), DHM2020: Proceedings of the 6th International Digital Human Modeling Symposium, August 31 – September 2, 2020. Paper presented at 6th International Digital Human Modeling Symposium, August 31 – September 2, 2020, Skövde, Sweden (pp. 51-60). Amsterdam: IOS Press
Åpne denne publikasjonen i ny fane eller vindu >>Learning Individual Driver’s Mental Models Using POMDPs and BToM
2020 (engelsk)Inngår i: DHM2020: Proceedings of the 6th International Digital Human Modeling Symposium, August 31 – September 2, 2020 / [ed] Lars Hanson, Dan Högberg, Erik Brolin, Amsterdam: IOS Press, 2020, s. 51-60Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Advanced driver assistant systems are supposed to assist the driver and ensure their safety while at the same time providing a fulfilling driving experience that suits their individual driving styles. What a driver will do in any given traffic situation depends on the driver’s mental model which describes how the driver perceives the observable aspects of the environment, interprets these aspects, and on the driver’s goals and beliefs of applicable actions for the current situation. Understanding the driver’s mental model has hence received great attention from researchers, where defining the driver’s beliefs and goals is one of the greatest challenges. In this paper we present an approach to establish individual drivers’ temporal-spatial mental models by considering driving to be a continuous Partially Observable Markov Decision Process (POMDP) wherein the driver’s mental model can be represented as a graph structure following the Bayesian Theory of Mind (BToM). The individual’s mental model can then be automatically obtained through deep reinforcement learning. Using the driving simulator CARLA and deep Q-learning, we demonstrate our approach through the scenario of keeping the optimal time gap between the own vehicle and the vehicle in front.

sted, utgiver, år, opplag, sider
Amsterdam: IOS Press, 2020
Serie
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 11
Emneord
Driver Mental Model, Partially Observable Markov Decision Processes (POMDPs), Bayesian Theory of Mind (BToM), Reinforcement Learning
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-19122 (URN)10.3233/ATDE200009 (DOI)000680825700006 ()2-s2.0-85091228001 (Scopus ID)978-1-64368-104-7 (ISBN)978-1-64368-105-4 (ISBN)
Konferanse
6th International Digital Human Modeling Symposium, August 31 – September 2, 2020, Skövde, Sweden
Prosjekter
Intention Recognition for Real-time Automotive 3D situation awareness (IRRA)
Forskningsfinansiär
Vinnova, 2018-05012
Merknad

CC BY-NC 4.0 This work has been supported by VINNOVA, the Swedish Government Agency for Innovation Systems, proj. “Intention Recognition for Real-time Automotive 3D situation awareness (IRRA)”, in the funding program FFI: Strategic Vehicle Research and Innovation (DNR 2018-05012)

Tilgjengelig fra: 2020-09-29 Laget: 2020-09-29 Sist oppdatert: 2023-10-04bibliografisk kontrollert
Huhnstock, N. A., Karlsson, A., Riveiro, M. & Steinhauer, H. J. (2019). An Infinite Replicated Softmax Model for Topic Modeling. In: Vicenç Torra, Yasuo Narukawa, Gabriella Pasi, Marco Viviani (Ed.), Modeling Decisions for Artificial Intelligence: 16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019, Proceedings. Paper presented at 16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019 (pp. 307-318). Springer
Åpne denne publikasjonen i ny fane eller vindu >>An Infinite Replicated Softmax Model for Topic Modeling
2019 (engelsk)Inngår i: Modeling Decisions for Artificial Intelligence: 16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019, Proceedings / [ed] Vicenç Torra, Yasuo Narukawa, Gabriella Pasi, Marco Viviani, Springer, 2019, s. 307-318Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In this paper, we describe the infinite replicated Softmax model (iRSM) as an adaptive topic model, utilizing the combination of the infinite restricted Boltzmann machine (iRBM) and the replicated Softmax model (RSM). In our approach, the iRBM extends the RBM by enabling its hidden layer to adapt to the data at hand, while the RSM allows for modeling low-dimensional latent semantic representation from a corpus. The combination of the two results is a method that is able to self-adapt to the number of topics within the document corpus and hence, renders manual identification of the correct number of topics superfluous. We propose a hybrid training approach to effectively improve the performance of the iRSM. An empirical evaluation is performed on a standard data set and the results are compared to the results of a baseline topic model. The results show that the iRSM adapts its hidden layer size to the data and when trained in the proposed hybrid manner outperforms the base RSM model.

sted, utgiver, år, opplag, sider
Springer, 2019
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11676
Emneord
Restricted Boltzmann machine, Unsupervised learning, Topic modeling, Adaptive Neural Network
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-17664 (URN)10.1007/978-3-030-26773-5_27 (DOI)2-s2.0-85072832710 (Scopus ID)978-3-030-26772-8 (ISBN)978-3-030-26773-5 (ISBN)
Konferanse
16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019
Tilgjengelig fra: 2019-09-09 Laget: 2019-09-10 Sist oppdatert: 2020-07-03bibliografisk kontrollert
Torra, V., Karlsson, A., Steinhauer, H. J. & Berglund, S. (2019). Artificial Intelligence. In: Alan Said, Vicenç Torra (Ed.), Data Science in Practice: (pp. 9-26). Springer
Åpne denne publikasjonen i ny fane eller vindu >>Artificial Intelligence
2019 (engelsk)Inngår i: Data Science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, s. 9-26Kapittel i bok, del av antologi (Fagfellevurdert)
Abstract [en]

This chapter gives a brief introduction to what artificial intelligence is. We begin discussing some of the alternative definitions for artificial intelligence and introduce the four major areas of the field. Then, in subsequent sections we present these areas. They are problem solving and search, knowledge representation and knowledge-based systems, machine learning, and distributed artificial intelligence. The chapter follows with a discussion on some ethical dilemma we find in relation to artificial intelligence. A summary closes this chapter.

sted, utgiver, år, opplag, sider
Springer, 2019
Serie
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL); Kognitiv neurovetenskap och filosofi
Identifikatorer
urn:nbn:se:his:diva-16784 (URN)10.1007/978-3-319-97556-6_2 (DOI)000464719500003 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Tilgjengelig fra: 2019-04-17 Laget: 2019-04-17 Sist oppdatert: 2019-09-30bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0003-2949-4123