Rebekka Olsson Omslandseter

• Jeeru, Sindhusha; Omslandseter, Rebekka Olsson; Andersen, Per-Arne; Morrison, Aiden James; Sokolova, Nadezda & Granmo, Ole-Christoffer [Show all 7 contributors for this article] (2025). MixCTME: A Mixture of Convolutional Tsetlin Machine Experts Using Diverse Spectrogram Visualizations for Jamming Signal Classification. IEEE Internet of Things Journal. ISSN 2327-4662. 12(22), p. 47667–47678. doi: 10.1109/jiot.2025.3606366. Full text in Research Archive Show summary

  global navigation satellite systems (GNSSs) are vulnerable to jamming, which can degrade or even disable their services by interfering with signal reception. Such interference may interrupt GNSS positioning, navigation, timing, and communication functions. Therefore, robust jamming detection strategies are essential to ensure continuous and reliable services. Many existing jamming detection approaches use closed box machine learning (ML) models, which lack transparency and accuracy. In this article, we propose a novel mixture of convolutional Tsetlin machine experts (MixCTME) approach, using diverse spectrogram visualizations to enhance the accuracy and interpretability of jamming signal classification. To facilitate this, we collected raw in-phase and quadrature (IQ) data and named it the RealRFI dataset, which corresponds to ten different jamming classes. The data were collected through multiple radio-frequency interference (RFI) monitoring stations deployed at SINTEF companies across Europe and Scandinavia. Next, diverse visualizations were generated using short time Fourier transform (STFT), dominant frequency, fast Fourier transform (FFT), power spectral density, and standard deviation. These visualizations were binarized using the Otsu thresholding technique, and the binarized spectrograms were fed to the MixCTME model. Our method utilizes a combination of distinct convolutional Tsetlin machine (CTM) experts and employs a novel nonlinear technique, referred to as confidence-based gating, for weighting the experts’ inputs to make the final decision. We compared our method against state-of-the-art approaches via the self-collected and labeled dataset. Through experiments, our model achieved an accuracy of 90.70% on the collected data and 99.46% on a benchmark dataset, outperforming the state-of-the-art approaches. Additionally, we highlight the trustworthiness, scalability, and interpretability of the proposed approach, presenting a promising solution for accurate and interpretable jamming classification.

• Omslandseter, Rebekka Olsson; Lei, Jiao & Oommen, John (2023). Pioneering approaches for enhancing the speed of hierarchical LA by ordering the actions. Information Sciences. ISSN 0020-0255. 647, p. 1–17. doi: 10.1016/j.ins.2023.119487. Full text in Research Archive Show summary

  Fixed Structure Stocastic Automata (FSSA), Variable Structure Learning Automata (VSSA), and their discretized versions have been significantly improved by utilizing inexpensive estimates of the actions' reward probabilities. These represent the fastest LA to date. However, the concept of ordering the actions has never been used within the field, and the reason for this is that there is no way to order the actions a priori. The recently-introduced Hierarchical Discrete Pursuit Automaton (HDPA) has an interesting concept of placing two-action LA along the nodes of a tree, implying that the leaves signify an underlying ordering. In this paper, we show that if estimates are available (as in the case of estimator algorithms), these can be used to place the actions at the leaf level to further enhance the convergence capabilities of the overall ensemble of the two-action LAs. This paper contains the design of this HDPA, the proof of this assertion, and the simulation results on benchmark Environments. Based on the results, we believe that it is the fastest and most accurate LA to date. Our position is that it will be very hard to beat its performance, since it has been incorporated all the salient features of the entire field of LA.

• Oommen, John; Omslandseter, Rebekka Olsson & Lei, Jiao (2023). The object migration automata: its field, scope, applications, and future research challenges. Pattern Analysis and Applications. ISSN 1433-7541. 26, p. 917–928. doi: 10.1007/s10044-023-01163-x. Full text in Research Archive Show summary

  Partitioning, in and of itself, is an NP-hard problem. Prior to the Artificial Intelligence (AI)-based solutions, it was solved in the 1970s by optimization-based strategies. However, AI-based solutions appeared in the 1980s in a pioneering way, by using a Learning Automaton (LA)-motivated strategy known as the so-called Object Migrating Automaton (OMA). Although the OMA and its derivatives have been used in numerous applications since then, the basic kernel has remained the same. Because the number of possible partitions in a partitioning problem can be combinatorially exponential and the underlying tasks are NP-hard, the most advanced OMA algorithms could, until recently, only solve issues involving equally sized groups. Due to our recent innovations cited in the body of this paper, the enhanced OMA now also handles non-equally sized groups. Earlier, we had presented in Omslandseter (Pattern Anal Appl, 2023), a comprehensive survey of the state-of-the-art enhancements of the best-known OMA. We believe that these results will be the benchmark for a few decades and that it will be very hard to beat these results. This is a companion paper, intended to augment the contents of Omslandseter (Pattern Anal Appl, 2023). In this paper, we first discuss the OMA’s prior applications, its historical and current innovations, and the OMA-based algorithms’ relevance to societal needs. We also provide well-specified guidelines for future researchers so that they can use them for unresolved tasks, and also develop further advancements.

• Oommen, John; Omslandseter, Rebekka Olsson & Lei, Jiao (2023). Learning automata-based partitioning algorithms for stochastic grouping problems with non-equal partition sizes. Pattern Analysis and Applications. ISSN 1433-7541. 26, p. 751–772. doi: 10.1007/s10044-023-01131-5. Full text in Research Archive Show summary

  It is very fascinating that the principles of Artificial Intelligence, that were proposed many decades ago, have remained to be the foundations of many of the modern-day techniques, and a brief summary of these principles is given in the paper itself. While this truism is valid for problem solving and game playing, in the context of this paper, we emphasize that it is also pertinent for the so-called partitioning problems. In this paper, we consider the general partitioning problem that has been studied for decades. Unlike the heuristic and search strategies, our attention focuses on learning automata (LA) and reinforcement learning methods, and their powerful ability to solve problems in stochastic environments. These render the latter tools to be applicable to various complex tasks. As we shall explain, LA have been employed for partitioning, and in particular, the paradigm of Object Migration Automata (OMA) has offered state-of-the-art adaptive methods for solving grouping and partitioning problems. However, because the number of possible partitions is combinatorially exponential, and of the NP-hardness of the underlying tasks, the existing state-of-the-art OMA algorithms can only solve problems of equal-sized groups. To resolve this, in this paper, we propose two new OMA variants that can solve both equally and unequally sized partitioning problems. The key here is to provide additional information to the algorithms, and in doing so, to expand the solution space. The first variant, the Greatest Common Divisor OMA (GCD-OMA), relaxes the constraint of having equally sized groups by mapping the actions onto a larger state space, and thus permitting it to solve problems where the group sizes have a GCD that is greater than unity. Subsequently, we propose a second variant named the Partition Size Required OMA (PSR-OMA) to make the algorithm more versatile. The PSR-OMA can handle groups of arbitrary sizes when the group sizes are known a priori. We demonstrate the proposed algorithms’ strength in solving stochastic grouping problems through extensive simulations, even with high noise levels. The GCD-OMA and the PSR-OMA represent the current state-of-the-art when it concerns resolving the extremely complex problem of partitioning and can be used in any of the numerous applications in which the OMA itself has been utilized .

• Omslandseter, Rebekka Olsson; Lei, Jiao; Liu, Yuanwei & Oommen, John (2022). User grouping and power allocation in NOMA systems: a novel semi-supervised reinforcement learning-based solution. Pattern Analysis and Applications. ISSN 1433-7541. 26. doi: 10.1007/s10044-022-01091-2. Full text in Research Archive Show summary

  In this paper, we present a pioneering solution to the problem of user grouping and power allocation in non-orthogonal multiple access (NOMA) systems. The problem is highly pertinent because NOMA is a well-recognized technique for future mobile radio systems. The salient and difficult issues associated with NOMA systems involve the task of grouping users together into the pre-specified time slots, which are augmented with the question of determining how much power should be allocated to the respective users. This problem is, in and of itself, NP-hard. Our solution is the first reported reinforcement learning (RL)-based solution, which attempts to resolve parts of this issue. In particular, we invoke the object migration automaton (OMA) and one of its variants to resolve the grouping in NOMA systems. Furthermore, unlike the solutions reported in the literature, we do not assume prior knowledge of the channels’ distributions, nor of their coefficients, to achieve the grouping/partitioning. Thereafter, we use the consequent groupings to heuristically infer the power allocation. The simulation results that we have obtained confirm that our learning scheme can follow the dynamics of the channel coefficients efficiently, and that the solution is able to resolve the issue dynamically.

• Omslandseter, Rebekka Olsson; Jiao, Lei & Oommen, John (2022). Enhancing the Speed of Hierarchical Learning Automata by Ordering the Actions - A Pioneering Approach. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13728, p. 775–788. doi: 10.1007/978-3-031-22695-3_54. Full text in Research Archive Show summary

  For the past six decades, the operation of Learning Automata (LA) has involved states and action probabilities. These have been central to “remembering” the quality of the actions chosen during the learning. The latest enhancements have also incorporated estimates of the actions’ reward probabilities. However, a phenomenon that has never been used to-date is that of considering how these actions themselves, can be ordered. Ordering the actions in traditional LA is rather meaningless unless one resorts to invoking the theory of Random Races [1]. However, we show that such an ordering makes sense if the automata operate hierarchically, within a tree, with the actions being placed at the leaves. In this paper, we shall show that when the LA are arranged “in a tree formation”, and when the learning is achieved within such a tree, the hierarchical LA has a superior performance if the actions located at the leaves of the tree are arranged suitably. While this concept can be incorporated in any hierarchical LA, we demonstrate its power for the most recent machine, i.e., the Hierarchical Discretized Pursuit Automaton (HDPA). These strategies can also be included in the Hierarchical Continuous Pursuit Automaton (HCPA), and to both of these which utilize traditional Maximum Likelihood (ML) or Bayesian estimates [2]. The experimental results presented here are very impressive, and so, if we consider the chronology of LA from FSSA through VSSA, the Estimator schemes, and the recent hierarchical LA, our modest claim is that the inclusion of the ADE represents the state-of-the-art which is not easily surpassed.

• Omslandseter, Rebekka Olsson; Jiao, Lei; Zhang, Xuan; Yazidi, Anis & Oommen, John (2022). The Hierarchical Discrete Pursuit Learning Automaton: A Novel Scheme with Fast Convergence and Epsilon-Optimality. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X. 35(6), p. 8278–8292. doi: 10.1109/TNNLS.2022.3226538. Full text in Research Archive Show summary

  Since the early 1960s, the paradigm of learning automata (LA) has experienced abundant interest. Arguably, it has also served as the foundation for the phenomenon and field of reinforcement learning (RL). Over the decades, new concepts and fundamental principles have been introduced to increase the LA’s speed and accuracy. These include using probability updating functions, discretizing the probability space, and using the “Pursuit” concept. Very recently, the concept of incorporating “structure” into the ordering of the LA’s actions has improved both the speed and accuracy of the corresponding hierarchical machines, when the number of actions is large. This has led to the $\epsilon$ -optimal hierarchical continuous pursuit LA (HCPA). This article pioneers the inclusion of all the above-mentioned phenomena into a new single LA, leading to the novel hierarchical discretized pursuit LA (HDPA). Indeed, although the previously proposed HCPA is powerful, its speed has an impediment when any action probability is close to unity, because the updates of the components of the probability vector are correspondingly smaller when any action probability becomes closer to unity. We propose here, the novel HDPA, where we infuse the phenomenon of discretization into the action probability vector’s updating functionality, and which is invoked recursively at every stage of the machine’s hierarchical structure. This discretized functionality does not possess the same impediment, because discretization prohibits it. We demonstrate the HDPA’s robustness and validity by formally proving the $\epsilon$ -optimality by utilizing the moderation property. We also invoke the submartingale characteristic at every level, to prove that the action probability of the optimal action converges to unity as time goes to infinity. Apart from the new machine being $\epsilon$ -optimal, the numerical results demonstrate that the number of iterations required for convergence is significantly reduced for the HDPA, when compared to the state-of-the-art HCPA scheme.

• Omslandseter, Rebekka Olsson; Jiao, Lei; Zhang, Xuan; Yazidi, Anis & Oommen, John (2022). The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13151, p. 507–518. doi: 10.1007/978-3-030-97546-3_41. Full text in Research Archive Show summary

  Since its early beginning, the paradigm of Learning Automata (LA), has attracted much interest. Over the last decades, new concepts and various improvements have been introduced to increase the LA’s speed and accuracy, including employing probability updating functions, discretizing the probability space, and implementing the “Pursuit” concept. The concept of incorporating “structure” into the ordering of the LA’s actions is one of the latest advancements to the field, leading to the ϵ-optimal Hierarchical Continuous Pursuit LA (HCPA) that has superior performance to other LA variants when the number of actions is large. Although the previously proposed HCPA is powerful, its speed has a handicap when the required action probability of an action is approaching unity. The reason for this slow convergence is that the learning parameter operates in a multiplicative manner within the probability space, making the increment of the action probability smaller as its probability becomes close to unity. Therefore, we propose the novel Hierarchical Discrete Learning Automata (HDPA) in this paper, which does not possess the same impediment as the HCPA. The proposed machine infuse the principle of discretization into the action probability vector’s updating functionality, where this type of updating is invoked recursively at every depth within a hierarchical tree structure and we pursue the best estimated action in all iterations through utilization of the Estimator phenomenon. The proposed machine is ϵ-optimal, and our experimental results demonstrate that the number of iterations required before convergence is significantly reduced for the HDPA, when compared with the HCPA.

• Omslandseter, Rebekka Olsson; Jiao, Lei & Oommen, John (2021). A Learning-Automata Based Solution for Non-equal Partitioning: Partitions with Common GCD Sizes. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 12799, p. 227–239. doi: 10.1007/978-3-030-79463-7_19. Full text in Research Archive Show summary

  The Object Migration Automata (OMA) has been used as a powerful tool to resolve real-life partitioning problems in random Environments. The virgin OMA has also been enhanced by incorporating the latest strategies in Learning Automata (LA), namely the Pursuit and Transitivity phenomena. However, the single major handicap that it possesses is the fact that the number of objects in each partition must be equal. Obviously, one does not always encounter problems with equally-sized groups (When the true underlying problem has non-equally-sized groups, the OMA reports the best equally-sized solution as the recommended partition.). This paper is the pioneering attempt to relax this constraint. It proposes a novel solution that tackles partitioning problems where the partition sizes can be both equal and/or unequal, but when the cardinalities of the true partitions have a Greatest Common Divisor (GCD). However, on attempting to resolve this less-constrained version, we encounter a few problems that deal with implementing the inter-partition migration of the objects. To mitigate these, we invoke a strategy that has been earlier used in the theory of automata, namely that of mapping the machine’s state space onto a larger space. This paper details how this strategy can be incorporated, and how such problems can be solved. In essence, it presents the design, implementation, and testing of a novel OMA-based method that can be implemented with the OMA itself, and also in all of its existing variants, including those incorporating the Pursuit and Transitivity phenomena. Numerical results demonstrate that the new approach can efficiently solve partitioning problems with partitions that have a common GCD.

• Omslandseter, Rebekka Olsson; Jiao, Lei & Oommen, John (2021). Object Migration Automata for Non-equal Partitioning Problems with Known Partition Sizes. IFIP Advances in Information and Communication Technology. ISSN 1868-4238. doi: 10.1007/978-3-030-79150-6_11. Full text in Research Archive Show summary

  Solving partitioning problems in random environments is a classic and challenging task, and has numerous applications. The existing Object Migration Automaton (OMA) and its proposed enhancements, which include the Pursuit and Transitivity phenomena, can solve problems with equi-sized partitions. Currently, these solutions also include one where the partition sizes possess a Greatest Common Divisor (GCD). In this paper, we propose an OMA-based solution that can solve problems with both equally and non-equally-sized groups, without restrictions on their sizes. More specifically, our proposed approach, referred to as the Partition Size Required OMA (PSR-OMA), can solve general partitioning problems, with the only additional requirement being that the unconstrained partitions’ sizes are known a priori. The scheme is a fundamental contribution in the field of partitioning algorithms, and the numerical results presented demonstrate that PSR-OMA can solve both equi-partitioning and non-equi-partitioning problems efficiently, and is the only known solution that resolves this problem.

• Omslandseter, Rebekka Olsson; Lei, Jiao; Liu, Yuanwei & Oommen, John (2020). User Grouping and Power Allocation in NOMA Systems: A Reinforcement Learning-Based Solution, Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices.. Springer Nature. ISSN 9783030557898. p. 299–311. doi: 10.1007/978-3-030-55789-8_27. Full text in Research Archive
• Omslandseter, Rebekka Olsson; Lei, Jiao & Haglund, Magne Arild (2019). Field Measurements and Parameter Calibrations of Propagation Model for Digital Audio Broadcasting in Norway. IEEE Vehicular Technology Conference (VTC). ISSN 1090-3038. doi: 10.1109/VTCFall.2018.8690746. Full text in Research Archive

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• Goodwin, Morten; Borgersen, Karl Audun Kagnes & Omslandseter, Rebekka Olsson (2025). Forsket på KI og strømforbruk: – Skremselspropaganda vi må få bukt med – NRK Sørlandet – Lokale nyheter, TV og radio. Full text in Research Archive Show summary

  Professor Morten Goodwin har undersøkt strømforbruket til vanlige KI-søk. Han mener teknologien feilaktig blir omtalt som miljøversting, men møter motstand.

• Goodwin, Morten; Borgersen, Karl Audun Kagnes & Omslandseter, Rebekka Olsson (2025). Kunstig intelligens får skylden for et enormt strømforbruk den ikke har. doi: https:/www.aftenposten.no/meninger/debatt/i/B05a6G/kunstig-intelligens-faar-skylden-for-et-enormt-stroemforbruk-den-ikke-har. Full text in Research Archive Show summary

  Språkmodeller er offer for grov overdrivelse når det gjelder strøm om ressursforbruk.

• Bønnhoff, Heidi Esma Dahl; Leer-Helgesen, Arnhild; Omslandseter, Rebekka Olsson & Stien, Cathrine (2023). Digitalisering og teknologi i et likestillingsperspektiv | Digin. [Internet]. Digin. doi: https:/digin.no/nyheter/digitalisering-og-teknologi-i-et-likestillingsperspektiv/. Full text in Research Archive Show summary

  Digitalisering og teknologi i et likestillingsperspektiv

• Omslandseter, Rebekka Olsson (2023). On the Theory and Applications of Hierarchical Learning Automata and Object Migration Automata. Universitetet i Agder. ISSN 9788284271613. Full text in Research Archive Show summary

  The paradigm of Artificial Intelligence (AI) and the sub-group of Machine Learning (ML) have attracted exponential interest in our society in recent years. The domain of ML contains numerous methods, and it is desirable (and in one sense, mandatory) that these methods are applicable and valuable to real-life challenges. Learning Automata (LA) is an intriguing and classical direction within ML. In LA, non-human agents can find optimal solutions to various problems through the concept of learning. The LA instances learn through Agent-Environment interactions, where advantageous behavior is rewarded, and disadvantageous behavior is penalized. Consequently, the agent eventually, and hopefully, learns the optimal action from a set of actions. LA has served as a foundation for Reinforcement Learning (RL), and the field of LA has been studied for decades. However, many improvements can still be made to render these algorithms to be even more convenient and effective. In this dissertation, we record our research contributions to the design and applications within the field of LA. Our research includes novel improvements to the domain of hierarchical LA, major advancements to the family of Object Migration Automata (OMA) algorithms, and a novel application of LA, where it was utilized to solve challenges in a mobile radio communication system. More specifically, we introduced the novel Hierarchical Discrete Pursuit Automaton (HDPA), which significantly improved the state of the art in terms of effectiveness for problems with high accuracy requirements, and we mathematically proved its ϵ-optimality. In addition, we proposed the Action Distribution Enhanced (ADE) approach to hierarchical LA schemes which significantly reduced the number of iterations required before the machines reached convergence. The existing schemes in the OMA paradigm, which are able to solve partitioning problems, could only solve problems with equally-sized partitions. Therefore, we proposed two novel methods that could handle unequally-sized partitions. In addition, we rigorously summarized the OMA domain, outlined its potential benefits to society, and listed further development cases for future researchers in the field. With regard to applications, we proposed an OMA-based approach to the grouping and power allocation in Non-orthogonal Multiple Access (NOMA) systems, demonstrating the applicability of the OMA and its advantage in solving fairly complicated stochastic problems. The details of these contributions and their published scientific impacts will be summarized in this dissertation, before we present some of the research contributions in their entirety.

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