Per-Arne Andersen

• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2024). Towards safe and sustainable reinforcement learning for real-time strategy games. Information Sciences. ISSN 0020-0255. 679. doi: 10.1016/j.ins.2024.120980.
• Bhattarai, Bimal; Granmo, Ole-Christoffer; Lei, Jiao; Andersen, Per-Arne; Tunheim, Svein Anders & Shafik, Rishad Ahmed [Show all 7 contributors for this article] (2023). Contracting Tsetlin Machine with Absorbing Automata, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10455040.
• Drøsdal, Didrik Kallhovd; Grimsmo, Andreas; Andersen, Per-Arne; Granmo, Ole-Christoffer & Goodwin, Morten (2023). Exploring the Potential of Model-Free Reinforcement Learning using Tsetlin Machines, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10455080.
• Gunvaldsen, Ole; Thorsen, Henning Blomfeld; Andersen, Per-Arne; Granmo, Ole-Christoffer & Goodwin, Morten (2023). Towards IoT Anomaly Detection with Tsetlin Machines, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10455063.
• Granmo, Ole-Christoffer; Andersen, Per-Arne; Lei, Jiao; Zhang, Xuan; Blakely, Christian Dallas & Berge, Geir Thore [Show all 7 contributors for this article] (2023). Learning Minimalistic Tsetlin Machine Clauses with Markov Boundary-Guided Pruning, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10454914.
• Jyhne, Sander; Andersen, Per-Arne; Goodwin, Morten & Oveland, Ivar (2023). A Contrastive Learning Scheme with Transformer Innate Patches. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14381, p. 103–114. doi: 10.1007/978-3-031-47994-6_8. Show summary

  This paper presents Contrastive Transformer (CT), a contrastive learning scheme using the innate transformer patches. CT enables existing contrastive learning techniques, often used for image classification, to benefit dense downstream prediction tasks such as semantic segmentation. The scheme performs supervised patch-level contrastive learning, selecting the patches based on the ground truth mask, subsequently used for hard-negative and hard-positive sampling. The scheme applies to all patch-based vision-transformer architectures, is easy to implement, and introduces minimal additional memory footprint. Additionally, the scheme removes the need for huge batch sizes, as each patch is treated as an image. We apply and test CT for the case of aerial image segmentation, known for low-resolution data, large class imbalance, and similar semantic classes. We perform extensive experiments to show the efficacy of the CT scheme on the ISPRS Potsdam aerial image segmentation dataset. Additionally, we show the generalizability of our scheme by applying it to multiple inherently different transformer architectures. Ultimately, the results show a consistent increase in mean Intersection-over-Union (IoU) across all classes.

• Laursen, Rune Alexander; Alo, Peshang; Goodwin, Morten & Andersen, Per-Arne (2023). Distinct Sequential Models for Inference Boosting. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14381, p. 198–203. doi: 10.1007/978-3-031-47994-6_15. Show summary

  Type 1 Diabetes (T1D) is a chronic disease where the body is unable to regulate the Blood Glucose Level (BGL), leading to severe health consequences if not regulated. Accurate BGL predictions can enable better disease management and improve treatment decisions. However, predicting future BGLs is a complex problem due to the inherent complexity and variability of the human body. This paper investigates using a new technique to outperform a State-of-the-Art (SotA) Convolutional Recurrent Neural Network (CRNN) model by forecasting BGLs on the same dataset. The problem is structured, and the data is preprocessed as a multivariate multi-step time series. The Distinct Sequential Models for Inference Boosting (DSMIB) technique is used, which manages missing data and counters potential issues from other techniques by employing both a Long Short-Term Memory (LSTM) model and a Transformer-based model together. The experimental results show that this technique reduces the Root Mean Squared Error (RMSE) by approximately 14.28% when predicting the BGL 30 min in the future compared to the SotA model. This improvement highlights the potential of this approach to assist diabetes patients with effective disease management.

• Evensen, Vetle Nesland; Henriksen, Gabriel Bergman; Melhus, Sondre; Olsen, Ole Steine; Haugen, Kristina & Dolmen, Dag [Show all 11 contributors for this article] (2023). ReFrogID: Pattern Recognition for Pool Frog Identification Using Deep Learning and Feature Matching. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14381, p. 365–376. doi: 10.1007/978-3-031-47994-6_33. Show summary

  The global decline in amphibian populations is a pressing issue, with numerous species facing the threat of extinction. One such species is the pool frog, Pelophylax lessonae, whose Norwegian population has experienced a significant long-term decline since monitoring began in 1996. This decline has pushed the species to the verge of local extinction. A substantial knowledge gap in the species’ biology hinders the proposal and evaluation of effective management actions. Consequently, there is a pressing need for efficient techniques to gather data on population size and composition. Recent advancements in Machine Learning (ML) and Deep Learning (DL) have shown promising results in various domains, including ecology and evolution. Current research in these fields primarily focuses on species modeling, behavior detection, and identity recognition. The progress in mobile technology, ML, and DL has equipped researchers across numerous disciplines, including ecology, with innovative data collection methods for building knowledge bases on species and ecosystems. This study addresses the need for systematic field data collection for monitoring endangered species like the pool frog by employing deep learning and image processing techniques. In this research, a multidisciplinary team developed a technique, termed ReFrogID, to identify individual frogs using their unique abdominal patterns. Utilizing RGB images, the system operates on two main principles: (1) a DL algorithm for automatic segmentation achieving AP@89.147, AP50@99.123, and AP75@98.942, and (2) pattern matching via local feature detection and matching methods. A new dataset, pelophylax_lessonae, addresses the identity recognition problem in pool frogs. The effectiveness of ReFrogIDis validated by its ability to identify frogs even when human experts fail. Source code is available at here.

• Jyhne, Sander; Jacobsen, Jørgen Åsbu; Goodwin, Morten & Andersen, Per-Arne (2023). DeNISE: Deep Networks for Improved Segmentation Edges. IFIP Advances in Information and Communication Technology. ISSN 1868-4238. 675, p. 81–89. doi: 10.1007/978-3-031-34111-3_8. Show summary

  This paper presents Deep Networks for Improved Segmentation Edges (DeNISE), a novel data enhancement technique using edge detection and segmentation models to improve the boundary quality of segmentation masks. DeNISE utilizes the inherent differences in two sequential deep neural architectures to improve the accuracy of the predicted segmentation edge. DeNISE applies to all types of neural networks and is not trained end-to-end, allowing rapid experiments to discover which models complement each other. We test and apply DeNISE for building segmentation in aerial images. Aerial images are known for difficult conditions as they have a low resolution with optical noise, such as reflections, shadows, and visual obstructions. Overall the paper demonstrates the potential for DeNISE. Using the technique, we improve the baseline results with a building IoU of 78.9%.

• Anderson, Abraham; Olafarson, Einar Julius; Andersen, Per-Arne & Noori, Nadia Saad (2022). The ODeLIndA Dataset for Field-of-View Obstruction Detection Using Transfer Learning for Real-Time Industrial Applications. In Bramer, Max & Stahl, Frederic (Ed.), Artificial Intelligence XXXIX 42nd SGAI International Conference on Artificial Intelligence, AI 2022. Springer. ISSN 978-3-031-21441-7. p. 197–210. doi: 10.1007/978-3-031-21441-7_14. Show summary

  Several challenges arise in industrial applications when using machine vision in safety-critical operations, such as field of view (FoV) obstruction, limited visibility of the equipment’s operational perimeter, difficulty obtaining datasets, and high accuracy requirement. This research shows how machine learning methods such as convolutional neural networks (CNN) and transfer learning can tackle these challenges. Transfer learning works well when the source and target domain images are similar, which is the case for identifying the operational conditions and FoV status in a fixed industrial environment where heavy equipment is operating with the aid of a machine vision module. In this work, eight categories describing FoV status and environmental conditions were selected as follows: clean lens/clear view, cracked lens, dirty lens, foggy, frost, dark view, flares, and rainy images. We used 21 616 images for the training set and 9450 for the test and validation. We benchmark the performance of several transfer learning models and networks (i.e., MobileNet, VGG16, VGG19, DenseNet, Inceptionv3, ResNet50, and Xception) against a CNN model/network trained from scratch. The experiments show that MobileNet combined with transfer learning performs best, with 98% accuracy compared to 93% for the traditional CNN model. Furthermore, the model can detect obstructions in real-time using commodity hardware when tested with a video stream. Finally, we provide a novel dataset for multi-class obstruction detection, which can be found here.

• Jyhne, Sander; Goodwin, Morten; Andersen, Per-Arne; Oveland, Ivar; Nossum, Alexander Salveson & Ormseth, Karianne Øydegard [Show all 8 contributors for this article] (2022). MapAI: Precision in BuildingSegmentation. Nordic Machine Intelligence (NMI). ISSN 2703-9196. 2, p. 1–3. doi: 10.5617/nmi.9849. Full text in Research Archive Show summary

  MapAI: Precision in Building Segmentation is a competition arranged with the Norwegian Artificial Intelligence Research Consortium (NORA) 1 in collaboration with Centre for Artificial Intelligence Research at the University of Agder (CAIR)2 , the Norwegian Mapping Authority3 , AI:Hub4 , Norkart5 , and the Danish Agency for Data Supply and Infrastructure6 . The competition will be held in the fall of 2022. It will be concluded at the Northern Lights Deep Learning conference focusing on the segmentation of buildings using aerial images and laser data. We propose two different tasks to segment buildings, where the first task can only utilize aerial images, while the second must use laser data (LiDAR) with or without aerial images. Furthermore, we use IoU and Boundary IoU [1] to properly evaluate the precision of the models, with the latter being an IoU measure that evaluates the results’ boundaries. We provide the participants with a training dataset and keep a test dataset for evaluation.

• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2022). CaiRL: A High-Performance Reinforcement Learning Environment Toolkit, 2022 IEEE Conference on Games (CoG). IEEE conference proceedings. ISSN 978-1-6654-5989-1. p. 361–368. doi: 10.1109/CoG51982.2022.9893661. Show summary

  This paper addresses the dire need for a platform that efficiently provides a framework for running reinforcement learning (RL) experiments. We propose the CaiRL Environment Toolkit as an efficient, compatible, and more sustainable alternative for training learning agents and propose methods to develop more efficient environment simulations. There is an increasing focus on developing sustainable artificial intelligence. However, little effort has been made to improve the efficiency of running environment simulations. The most popular development toolkit for reinforcement learning, OpenAI Gym, is built using Python, a powerful but slow programming language. We propose a toolkit written in C++ with the same flexibility level but works orders of magnitude faster to make up for Python's inefficiency. This would drastically cut climate emissions. CaiRL also presents the first reinforcement learning toolkit with a built-in JVM and Flash support for running legacy flash games for reinforcement learning research. We demonstrate the effectiveness of CaiRL in the classic control benchmark, comparing the execution speed to OpenAI Gym. Furthermore, we illustrate that CaiRL can act as a drop-in replacement for OpenAI Gym to leverage significantly faster training speeds because of the reduced environment computation time.

• Rasmussen, Ingeborg; Kvalsvik, Sigurd; Andersen, Per-Arne; Aune, Teodor N. & Hagen, Daniel (2022). Development of a Novel Object Detection System Based on Synthetic Data Generated from Unreal Game Engine. Applied Sciences. ISSN 2076-3417. 12(17). doi: 10.3390/app12178534. Full text in Research Archive
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2021). ORACLE: End-to-End Model Based Reinforcement Learning. In Bramer, Max & Ellis, Richard (Ed.), Artificial Intelligence XXXVIII, 41st SGAI International Conference on Artificial Intelligence. Springer. ISSN 978-3-030-91099-0. doi: 10.1007/978-3-030-91100-3_4.
• Sharma, Jivitesh; Andersen, Per-Arne; Granmo, Ole-Christoffer & Goodwin, Morten (2021). Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment. IEEE Transactions on Systems, Man & Cybernetics. Systems. ISSN 2168-2216. 51(12), p. 7363–7381. doi: 10.1109/TSMC.2020.2967936. Full text in Research Archive
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2020). Increasing sample efficiency in deep reinforcement learningusing generative environment modelling. Expert Systems. ISSN 0266-4720. doi: 10.1111/exsy.12537.
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2020). Safer Reinforcement Learning for Agents in Industrial Grid-Warehousing, Machine Learning, Optimization, and Data Science: 6th International Conference, LOD 2020. Springer Nature. ISSN 978-3-030-64579-3. p. 169–180. doi: 10.1007/978-3-030-64580-9_14.
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2020). Increasing sample efficiency in deep reinforcement learning using generative environment modelling. Expert Systems. ISSN 0266-4720. doi: 10.1111/exsy.12537. Full text in Research Archive
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2020). Towards safe reinforcement-learning in industrial grid-warehousing. Information Sciences. ISSN 0020-0255. 537, p. 467–484. doi: 10.1016/j.ins.2020.06.010. Full text in Research Archive
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2019). Towards Model-Based Reinforcement Learning for Industry-Near Environments. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11927 LNAI, p. 36–49. doi: 10.1007/978-3-030-34885-4_3.
• Sharma, Jivitesh; Andersen, Per-Arne; Granmo, Ole-Christoffer & Goodwin, Morten (2019). Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment. arXiv.org. ISSN 2331-8422. doi: 10.1109/tsmc.2020.2967936. Show summary

  We focus on the important problem of emergency evacuation, which clearly could benefit from reinforcement learning that has been largely unaddressed. Emergency evacuation is a complex task which is difficult to solve with reinforcement learning, since an emergency situation is highly dynamic, with a lot of changing variables and complex constraints that makes it difficult to train on. In this paper, we propose the first fire evacuation environment to train reinforcement learning agents for evacuation planning. The environment is modelled as a graph capturing the building structure. It consists of realistic features like fire spread, uncertainty and bottlenecks. We have implemented the environment in the OpenAI gym format, to facilitate future research. We also propose a new reinforcement learning approach that entails pretraining the network weights of a DQN based agents to incorporate information on the shortest path to the exit. We achieved this by using tabular Q-learning to learn the shortest path on the building model's graph. This information is transferred to the network by deliberately overfitting it on the Q-matrix. Then, the pretrained DQN model is trained on the fire evacuation environment to generate the optimal evacuation path under time varying conditions. We perform comparisons of the proposed approach with state-of-the-art reinforcement learning algorithms like PPO, VPG, SARSA, A2C and ACKTR. The results show that our method is able to outperform state-of-the-art models by a huge margin including the original DQN based models. Finally, we test our model on a large and complex real building consisting of 91 rooms, with the possibility to move to any other room, hence giving 8281 actions. We use an attention based mechanism to deal with large action spaces. Our model achieves near optimal performance on the real world emergency environment.

• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2019). Towards Model-based Reinforcement Learning for Industry-near Environments. arXiv.org. ISSN 2331-8422. doi: 10.1007/978-3-030-34885-4_3.
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2018). The dreaming variational autoencoder for reinforcement learning environments. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11311 LNAI, p. 143–155. doi: 10.1007/978-3-030-04191-5_11. Full text in Research Archive Show summary

  Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.

• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2018). Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games , 2018 IEEE Conference on Computational Intelligence and Games (CIG). IEEE conference proceedings. ISSN 978-1-5386-4359-4. doi: 10.1109/CIG.2018.8490409.
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2017). FlashRL: A Reinforcement Learning Platform for Flash Games. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713. Full text in Research Archive
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2017). Towards a deep reinforcement learning approach for tower line wars. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 10630 LNAI, p. 101–114. doi: 10.1007/978-3-319-71078-5_8.
• Andersen, Per-Arne; Haraldseid, Christian Kråkevik; Goodwin, Morten & Yazidi, Anis (2016). Adaptive Task Assignment in Online Learning Environments. In NN, NN (Eds.), Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016. Association for Computing Machinery (ACM). ISSN 978-1-4503-4056-4. p. 1–10. doi: 10.1145/2912845.2912854.

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• Andersen, Per-Arne; Goodwin, Morten & Lyngroth, Maren Thormodsæter (2024). Vi spår kunstig intelligens-året 2024 og fem år frem i tid. [Internet]. Podcast Addict.
• Lanestedt, Gjermund; Goodwin, Morten & Andersen, Per-Arne (2023). Tid for en (mer) intelligent statsforvaltning? Stat og styring. ISSN 0803-0103. 33(3), p. 7–14. doi: 10.18261/stat.33.3.2.
• Andersen, Per-Arne (2023). Teknologipositivisme for verdier og demokrati. [Journal]. Stat & Styring.
• Andersen, Per-Arne & Haugen, Martin (2023). Vil bruke kunstig intelligens til å håndtere kriser. [Newspaper]. Grimstad Adressetidene.
• Andersen, Per-Arne & Bjørkeng, Per Kristian (2023). KI lurte menneske trill rundt. Nå krever over 1000 eksperter forbud. [Newspaper]. Aftenposten.
• Andersen, Per-Arne (2023). Uforberedt og ubeskyttet: Norges manglende forsvar mot cyberangrep. Digi.no.
• Andersen, Per-Arne (2023). Norge i skyggen av AI-revolusjonen: Cybersikkerhet krever en opptrapping av innsatsen. Digi.no.
• Goodwin, Morten & Andersen, Per-Arne (2022). Næringen henger ikke med i kunstig intelligens-kappløpet. Norsk Fiskeoppdrett. ISSN 0332-7132.
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2020). CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning, SGAI 2020: Artificial Intelligence XXXVII. Springer. ISSN 9783030637989. doi: 10.1007/978-3-030-63799-6_7.
• Sveen, Emil Mühlbradt; Sand, Kristoffer; Solberg, Trygve Andre Olsøy & Andersen, Per-Arne (2023). Utilizing Reinforcement Learning and Computer Vision in a Pick-and-Place Operation for Sorting Objects in Motion. Universitetet i Agder.
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2022). Advancements in Safe Deep Reinforcement Learning for Real-Time Strategy Games and Industry Applications. Universitetet i Agder. ISSN 978-82-8427-070-8.
• Balcik, Burcu; Akca, B; Yucesoy, E; Andersen, Per-Arne; Boey, Lise & Baharmand, Hossein (2021). Project report #2: Mathematical Modelling and Performance Measurement. Universitetet i Agder. ISSN 978-82-8427-036-4.
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2018). Deep Reinforcement Learning using Capsules in Advanced Game Environments. Universitetet i Agder. Full text in Research Archive Show summary

  Reinforcement Learning (RL) is a research area that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games. This success is primarily due to vast capabilities of Convolutional Neural Networks (ConvNet), enabling algorithms to extract useful information from noisy environments. Capsule Network (CapsNet) is a recent introduction to the Deep Learning algorithm group and has only barely begun to be explored. The network is an architecture for image classification, with superior performance for classification of the MNIST dataset. CapsNets have not been explored beyond image classification. This thesis introduces the use of CapsNet for Q-Learning based game algorithms. To successfully apply CapsNet in advanced game play, three main contributions follow. First, the introduction of four new game environments as frameworks for RL research with increasing complexity, namely Flash RL, Deep Line Wars, Deep RTS, and Deep Maze. These environments fill the gap between relatively simple and more complex game environments available for RL research and are in the thesis used to test and explore the CapsNet behavior. Second, the thesis introduces a generative modeling approach to produce artificial training data for use in Deep Learning models including CapsNets. We empirically show that conditional generative modeling can successfully generate game data of sufficient quality to train a Deep Q-Network well. Third, we show that CapsNet is a reliable architecture for Deep Q-Learning based algorithms for game AI. A capsule is a group of neurons that determine the presence of objects in the data and is in the literature shown to increase the robustness of training and predictions while lowering the amount training data needed. It should, therefore, be ideally suited for game plays.

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