Ole-Christoffer Granmo

• Abouzeid, Ahmed Abdulrahem Othman; Granmo, Ole-Christoffer; Goodwin, Morten & Webersik, Christian (2024). Towards misinformation mitigation on social media: novel user activity representation for modeling societal acceptance. Journal of Computational Social Science (JCSS). ISSN 2432-2717. doi: 10.1007/s42001-024-00256-9.
• Bhattarai, Bimal; Granmo, Ole-Christoffer; Lei, Jiao; Andersen, Per-Arne; Tunheim, Svein Anders & Shafik, Rishad Ahmed [Vis alle 7 forfattere av denne artikkelen] (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.
• Sahu, Prajwal Kumar; Boppu, Srinivas; Shafik, Rishad Ahmed; Tunheim, Svein Anders; Granmo, Ole-Christoffer & Cenkeramaddi, Linga Reddy (2023). Enhancing Inference Performance through Include only Literal Incorporation in Tsetlin Machine, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10455016.
• 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.
• Tarasyuk, Olga; Rahman, Tousif; Shafik, Rishad Ahmed; Yakovlev, Alexandre; Gorbenko, Anatoliy & Granmo, Ole-Christoffer [Vis alle 7 forfattere av denne artikkelen] (2023). Systematic Search for Optimal Hyper-parameters of the Tsetlin Machine on MNIST Dataset, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10454969.
• 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.
• Liland, Kristian Hovde; Tomic, Oliver; Indahl, Ulf Geir; Futsæther, Cecilia Marie; Lei, Jiao & Granmo, Ole-Christoffer [Vis alle 7 forfattere av denne artikkelen] (2023). Tsetlin Machine in DNA sequence classification : Application to prokaryote gene prediction / A match made in silico, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10454960.
• Przybysz, Emilia Katarzyna; Bhattarai, Bimal; Persia, Cosimo Damiano; Ozaki, Ana; Granmo, Ole-Christoffer & Sharma, Jivitesh (2023). Verifying Properties of Tsetlin Machines, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10454997.
• Granmo, Ole-Christoffer; Andersen, Per-Arne; Lei, Jiao; Zhang, Xuan; Blakely, Christian Dallas & Berge, Geir Thore [Vis alle 7 forfattere av denne artikkelen] (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.
• Tunheim, Svein Anders; Lei, Jiao & Granmo, Ole-Christoffer (2023). Model Export for the Convolutional Coalesced Tsetlin Machine, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10455048.
• Sharma, Jivitesh; Yadav, Rohan Kumar; Granmo, Ole-Christoffer & Lei, Jiao (2023). Drop Clause: Enhancing Performance, Robustness and Pattern Recognition Capabilities of the Tsetlin Machine. I Chen, Yiling & Neville, Jennifer (Red.), Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence. AAAI Press. ISSN 978-1-57735-880-0. s. 13547–13555. doi: 10.1609/aaai.v37i11.26588.
• Gorji, Saeed Rahimi; Granmo, Ole-Christoffer & Goodwin, Morten (2023). Natural Language Modeling with the Tsetlin Machine. I Fujita, H. (Red.), Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. . Springer. ISSN 978-3-031-36821-9. s. 141–150. doi: 10.1007/978-3-031-36822-6_12.
• Abeyrathna, Kuruge Darshana; El Mekkaoui, Sara; Edward, L. Yi; Hafver, Andreas & Granmo, Ole-Christoffer (2023). Extension of Regression Tsetlin Machine for Interpretable Uncertainty Assessment. I Fensel, Anna; Ozaki, Ana; Dumitru, Roman & Soylu, Ahmet (Red.), Rules and Reasoning - 7th International Joint Conference, RuleML+RR 2023, Oslo, Norway, September 18-20, 2023, Proceedings. Springer Publishing Company. ISSN 978-3-031-45071-6. s. 19–33. doi: 10.1007/978-3-031-45072-3_2.
• Biermann, Daniel; Goodwin, Morten & Granmo, Ole-Christoffer (2023). Transfer Learning Through Knowledge-Infused Representations with Contextual Experts. I Maglogiannis, Ilias; Iliadis, L.; MacIntyre, J. & Dominguez, Manuel (Red.), Artificial Intelligence Applications and Innovations. Springer. ISSN 978-3-031-34106-9. doi: 10.1007/978-3-031-34107-6_45.
• Bhattarai, Bimal; Saha, Rupsa; Granmo, Ole-Christoffer; Zadorozhny, Vladimir & Xu, Jiawei (2023). A Logic-Based Explainable Framework for Relation Classification of Human Rights Violations. CEUR Workshop Proceedings. ISSN 1613-0073. 3464, s. 14–21. Vis sammendrag

  Using a Relational Tsetlin Machine (RTM) for analysis of semi-structured data allows the use of inherent relational structures present in natural language text to get an explainable classification of data. A finite Herbrand model derives Horn Clauses from the model, which are simple yet powerful logical tools that can build an abstract view of the world. We use the same to analyze human rights violation data. We show concretely how natural language can be transformed into a relational structure, and further use the Relational Tsetlin Machine to not only classify incidents as serious and non-serious violations but also explore the patterns learned by the RTM in order to arrive that those decisions. Furthermore, the distilled Horn Clauses show a precise understanding of the concepts involved without the drawback of textual ambiguity.

• Abeyrathna, Kuruge Darshana; Abouzeid, Ahmed Abdulrahem Othman; Bhattarai, Bimal; Giri, Charul; Glimsdal, Sondre & Granmo, Ole-Christoffer [Vis alle 11 forfattere av denne artikkelen] (2023). Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size . I Elkind, Edith (Red.), Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. AAAI Press. ISSN 978-1-956792-03-4. s. 3395–3403. doi: 10.24963/ijcai.2023/378. Fulltekst i vitenarkiv Vis sammendrag

  Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools tend to produce clauses with many literals (long clauses). As such, they become less interpretable. Further, longer clauses increase the switching activity of the clause logic in hardware, consuming more power. This paper introduces a novel variant of TM learning – Clause Size Constrained TMs (CSC-TMs) – where one can set a soft constraint on the clause size. As soon as a clause includes more literals than the constraint allows, it starts expelling literals. Accordingly, oversized clauses only appear transiently. To evaluate CSC-TM, we conduct classifcation, clustering, and regression experiments on tabular data, natural language text, images, and board games. Our results show that CSC-TM maintains accuracy with up to 80 times fewer literals. Indeed, the accuracy increases with shorter clauses for TREC, IMDb, and BBC Sports. After the accuracy peaks, it drops gracefully as the clause size approaches a single literal. We fnally analyze CSC-TM power consumption and derive new convergence properties.

• Tunheim, Svein Anders; Lei, Jiao; Shafik, Rishad Ahmed; Yakovlev, Alexandre & Granmo, Ole-Christoffer (2023). Convolutional Tsetlin Machine-based Training and Inference Accelerator for 2-D Pattern Classification. Microprocessors and Microsystems: Embedded Hardware Design (MICPRO). ISSN 0141-9331. 103. doi: 10.1016/j.micpro.2023.104949. Fulltekst i vitenarkiv Vis sammendrag

  The Tsetlin Machine (TM) is a machine learning algorithm based on an ensemble of Tsetlin Automata (TAs) that learns propositional logic expressions from Boolean input features. In this paper, the design and implementation of a Field Programmable Gate Array (FPGA) accelerator based on the Convolutional Tsetlin Machine (CTM) is presented. The accelerator performs classification of two pattern classes in 4 × 4 Boolean images with a 2 × 2 convolution window. Specifically, there are two separate TMs, one per class. Each TM comprises 40 propositional logic formulas, denoted as clauses, which are conjunctions of literals. Include/exclude actions from the TAs determine which literals are included in each clause. The accelerator supports full training, including random patch selection during convolution based on parallel reservoir sampling across all clauses. The design is implemented on a Xilinx Zynq XC7Z020 FPGA platform. With an operating clock speed of 40 MHz, the accelerator achieves a classification rate of 4.4 million images per second with an energy per classification of 0.6 J. The mean test accuracy is 99.9% when trained on the 2-dimensional Noisy XOR dataset with 40% noise in the training labels. To achieve this performance, which is on par with the original software implementation, Linear Feedback Shift Register (LFSR) random number generators of minimum 16 bits are required. The solution demonstrates the core principles of a CTM and can be scaled to operate on multi-class systems for larger images.

• Berge, Geir Thore; Granmo, Ole-Christoffer; Tveit, Tor Oddbjørn; Ruthjersen, Anna Linda & Sharma, Jivitesh (2023). Combining unsupervised, supervised and rule-based learning: the case of detecting patient allergies in electronic health records. BMC Medical Informatics and Decision Making. ISSN 1472-6947. 23. doi: 10.1186/s12911-023-02271-8. Fulltekst i vitenarkiv Vis sammendrag

  Background Data mining of electronic health records (EHRs) has a huge potential for improving clinical decision support and to help healthcare deliver precision medicine. Unfortunately, the rule-based and machine learning-based approaches used for natural language processing (NLP) in healthcare today all struggle with various shortcomings related to performance, efciency, or transparency. Methods In this paper, we address these issues by presenting a novel method for NLP that implements unsupervised learning of word embeddings, semi-supervised learning for simplifed and accelerated clinical vocabulary and concept building, and deterministic rules for fne-grained control of information extraction. The clinical language is automatically learnt, and vocabulary, concepts, and rules supporting a variety of NLP downstream tasks can further be built with only minimal manual feature engineering and tagging required from clinical experts. Together, these steps create an open processing pipeline that gradually refnes the data in a transparent way, which greatly improves the interpretable nature of our method. Data transformations are thus made transparent and predictions interpretable, which is imperative for healthcare. The combined method also has other advantages, like potentially being language independent, demanding few domain resources for maintenance, and able to cover misspellings, abbreviations, and acronyms. To test and evaluate the combined method, we have developed a clinical decision support system (CDSS) named Information System for Clinical Concept Searching (ICCS) that implements the method for clinical concept tagging, extraction, and classifcation. Results In empirical studies the method shows high performance (recall 92.6%, precision 88.8%, F-measure 90.7%), and has demonstrated its value to clinical practice. Here we employ a real-life EHR-derived dataset to evaluate the method’s performance on the task of classifcation (i.e., detecting patient allergies) against a range of common supervised learning algorithms. The combined method achieves state-of-the-art performance compared to the alternative methods we evaluate. We also perform a qualitative analysis of common word embedding methods on the task of word similarity to examine their potential for supporting automatic feature engineering for clinical NLP tasks. Conclusions Based on the promising results, we suggest more research should be aimed at exploiting the inherent synergies between unsupervised, supervised, and rule-based paradigms for clinical NLP.

• Maheshwari, Sidharth; Rahman, Tousif; Shafik, Rishad; Yakovlev, Alex; Rafiev, Ashur & Lei, Jiao [Vis alle 7 forfattere av denne artikkelen] (2023). REDRESS: Generating Compressed Models for Edge Inference Using Tsetlin Machines. IEEE Transactions on Pattern Analysis and Machine Intelligence. ISSN 0162-8828. 45(9), s. 11152–11168. doi: 10.1109/TPAMI.2023.3268415. Vis sammendrag

  Inference at-the-edge using embedded machine learning models is associated with challenging trade-offs between resource metrics, such as energy and memory footprint, and the performance metrics, such as computation time and accuracy. In this work, we go beyond the conventional Neural Network based approaches to explore Tsetlin Machine (TM), an emerging machine learning algorithm, that uses learning automata to create propositional logic for classification. We use algorithm-hardware co-design to propose a novel methodology for training and inference of TM. The methodology, called REDRESS, comprises independent TM training and inference techniques to reduce the memory footprint of the resulting automata to target low and ultra-low power applications. The array of Tsetlin Automata (TA) holds learned information in the binary form as bits: {0,1} , called excludes and includes, respectively. REDRESS proposes a lossless TA compression method, called the include-encoding, that stores only the information associated with includes to achieve over 99% compression. This is enabled by a novel computationally minimal training procedure, called the Tsetlin Automata Re-profiling, to improve the accuracy and increase the sparsity of TA to reduce the number of includes, hence, the memory footprint. Finally, REDRESS includes an inherently bit-parallel inference algorithm that operates on the optimally trained TA in the compressed domain, that does not require decompression during runtime, to obtain high speedups when compared with the state-of-the-art Binary Neural Network (BNN) models. In this work, we demonstrate that using REDRESS approach, TM outperforms BNN models on all design metrics for five benchmark datasets viz. MNIST, CIFAR2, KWS6, Fashion-MNIST and Kuzushiji-MNIST. When implemented on an STM32F746G-DISCO microcontroller, REDRESS obtained speedups and energy savings ranging 5-5700× compared with different BNN models.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Lei, Jiao (2023). An Interpretable Knowledge Representation Framework for Natural Language Processing with Cross-Domain Application, Advances in Information Retrieval. ECIR 2023. Springer. ISSN 978-3-031-28244-7. s. 167–181. doi: 10.1007/978-3-031-28244-7_11. Fulltekst i vitenarkiv Vis sammendrag

  Data representation plays a crucial role in natural language processing (NLP), forming the foundation for most NLP tasks. Indeed, NLP performance highly depends upon the effectiveness of the preprocessing pipeline that builds the data representation. Many representation learning frameworks, such as Word2Vec, encode input data based on local contextual information that interconnects words. Such approaches can be computationally intensive, and their encoding is hard to explain. We here propose an interpretable representation learning framework utilizing Tsetlin Machine (TM). The TM is an interpretable logic-based algorithm that has exhibited competitive performance in numerous NLP tasks. We employ the TM clauses to build a sparse propositional (boolean) representation of natural language text. Each clause is a class-specific propositional rule that links words semantically and contextually. Through visualization, we illustrate how the resulting data representation provides semantically more distinct features, better separating the underlying classes. As a result, the following classification task becomes less demanding, benefiting simple machine learning classifiers such as Support Vector Machine (SVM). We evaluate our approach using six NLP classification tasks and twelve domain adaptation tasks. Our main finding is that the accuracy of our proposed technique significantly outperforms the vanilla TM, approaching the competitive accuracy of deep neural network (DNN) baselines. Furthermore, we present a case study showing how the representations derived from our framework are interpretable. (We use an asynchronous and parallel version of Tsetlin Machine: available at https://github.com/cair/PyTsetlinMachineCUDA).

• Biermann, Daniel; Goodwin, Morten & Granmo, Ole-Christoffer (2023). Knowledge Infused Representations Through Combination of Expert Knowledge and Original Input. I Zouganeli, Evi; Yazidi, Anis; Borges Moreno e Mello, Gustavo & Lind, Pedro (Red.), Nordic Artificial Intelligence Research and Development: 4th Symposium of the Norwegian AI Society, NAIS 2022, Oslo, Norway, May 31-June 1, 2022, Revised Selected Papers. Springer Nature. ISSN 9783031170294. s. 3–15. doi: 10.1007/978-3-031-17030-0_1. Vis sammendrag

  Sophisticated applications in natural language processing, such as conversational agents, often need to be able to generalize across a range of different tasks to generate natural-feeling language. In this paper, we introduce a model that aims to improve generalizability with regard to different tasks by combining the original input with the output of a task-specific expert. Through a combination mechanism, we create a new representation that has been enriched with the information given by the expert. These enriched representations then serve as input to a downstream model. We test three different combination mechanisms in two combination paradigms and evaluate the performance of the new enriched representation in a simple encoder-decoder model. We show that even very simple combination mechanisms are able to significantly improve performance of the downstream model. This means that the encoded expert information is transported through the new enriched input representation, leading to a beneficial impact on performance within the task domain. This opens the way for exciting future endeavors such as testing performance on different task domains and the combination of multiple experts.

• Berge, Geir Thore; Granmo, Ole-Christoffer; Tveit, Tor Oddbjørn; Munkvold, Bjørn Erik; Ruthjersen, Anna Linda & Sharma, Jivitesh (2023). Machine learning-driven clinical decision support system for concept-based searching: a field trial in a Norwegian hospital. BMC Medical Informatics and Decision Making. ISSN 1472-6947. 23. doi: 10.1186/s12911-023-02101-x. Fulltekst i vitenarkiv Vis sammendrag

  Background Natural language processing (NLP) based clinical decision support systems (CDSSs) have demonstrated the ability to extract vital information from patient electronic health records (EHRs) to facilitate important decision support tasks. While obtaining accurate, medical domain interpretable results is crucial, it is demanding because realworld EHRs contain many inconsistencies and inaccuracies. Further, testing of such machine learning-based systems in clinical practice has received limited attention and are yet to be accepted by clinicians for regular use. Methods We present our results from the evaluation of an NLP-driven CDSS developed and implemented in a Norwegian Hospital. The system incorporates unsupervised and supervised machine learning combined with rulebased algorithms for clinical concept-based searching to identify and classify allergies of concern for anesthesia and intensive care. The system also implements a semi-supervised machine learning approach to automatically annotate medical concepts in the narrative. Results Evaluation of system adoption was performed by a mixed methods approach applying The Unifed Theory of Acceptance and Use of Technology (UTAUT) as a theoretical lens. Most of the respondents demonstrated a high degree of system acceptance and expressed a positive attitude towards the system in general and intention to use the system in the future. Increased detection of patient allergies, and thus improved quality of practice and patient safety during surgery or ICU stays, was perceived as the most important advantage of the system. Conclusions Our combined machine learning and rule-based approach benefts system performance, efciency, and interpretability. The results demonstrate that the proposed CDSS increases detection of patient allergies, and that the system received high-level acceptance by the clinicians using it. Useful recommendations for further system improvements and implementation initiatives are reducing the quantity of alarms, expansion of the system to include more clinical concepts, closer EHR system integration, and more workstations available at point of care.

• Gorji, Saeed Rahimi & Granmo, Ole-Christoffer (2023). Off-policy and on-policy reinforcement learning with the Tsetlin machine. Applied intelligence (Boston). ISSN 0924-669X. 53, s. 8596–8613. doi: 10.1007/s10489-022-04297-3. Vis sammendrag

  The Tsetlin Machine is a recent supervised learning algorithm that has obtained competitive accuracy- and resource usage results across several benchmarks. It has been used for convolution, classification, and regression, producing interpretable rules in propositional logic. In this paper, we introduce the first framework for reinforcement learning based on the Tsetlin Machine. Our framework integrates the value iteration algorithm with the regression Tsetlin Machine as the value function approximator. To obtain accurate off-policy state-value estimation, we propose a modified Tsetlin Machine feedback mechanism that adapts to the dynamic nature of value iteration. In particular, we show that the Tsetlin Machine is able to unlearn and recover from the misleading experiences that often occur at the beginning of training. A key challenge that we address is mapping the intrinsically continuous nature of state-value learning to the propositional Tsetlin Machine architecture, leveraging probabilistic updates. While accurate off-policy, this mechanism learns significantly slower than neural networks on-policy. However, by introducing multi-step temporal-difference learning in combination with high-frequency propositional logic patterns, we are able to close the performance gap. Several gridworld instances document that our framework can outperform comparable neural network models, despite being based on simple one-level AND-rules in propositional logic. Finally, we propose how the class of models learnt by our Tsetlin Machine for the gridworld problem can be translated into a more understandable graph structure. The graph structure captures the state-value function approximation and the corresponding policy found by the Tsetlin Machine.

• Holen, Martin; Ruud, Else-Line Malene; Warakagoda, Narada Dilp; Granmo, Ole-Christoffer; Engelstad, Paal E. & Knausgård, Kristian Muri (2022). Towards Using Reinforcement Learning for Autonomous Docking of Unmanned Surface Vehicles. I Iliadis, Lazaros; Jayne, Chrisina; Tefas, Anastasios & Pimenidis, Elias (Red.), Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science. Springer Nature. ISSN 978-3-031-08223-8. s. 461–474. doi: 10.1007/978-3-031-08223-8_38. Fulltekst i vitenarkiv Vis sammendrag

  Providing full autonomy to Unmanned Surface Vehicles (USV) is a challenging goal to achieve. Autonomous docking is a subtask that is particularly difficult. The vessel has to distinguish between obstacles and the dock, and the obstacles can be either static or moving. This paper developed a simulator using Reinforcement Learning (RL) to approach the problem. We studied several scenarios for the task of docking a USV in a simulator environment. The scenarios were defined with different sensor inputs and start-stop procedures but a simple shared reward function. The results show that the system solved the task when the IMU (Inertial Measurement Unit) and GNSS (Global Navigation Satellite System) sensors were used to estimate the state, despite the simplicity of the reward function.

• Vishwanath, Ajay; Bøhn, Einar Duenger; Granmo, Ole-Christoffer; Maree, Charl & Omlin, Christian Walter Peter (2022). Towards artificial virtuous agents: games, dilemmas and machine learning. AI and Ethics. ISSN 2730-5953. doi: 10.1007/s43681-022-00251-8.
• Rahman, Tousif; Shafik, Rishad; Granmo, Ole-Christoffer & Yakovlev, Alex (2022). Resilient Biomedical Systems Design Under Noise Using Logic-Based Machine Learning. Frontiers in Control Engineering. ISSN 2673-6268. doi: 10.3389/fcteg.2021.778118.
• Bhattarai, Bimal; Granmo, Ole-Christoffer & Lei, Jiao (2022). Explainable Tsetlin Machine Framework for Fake News Detection with Credibility Score Assessment . I Calzolari, Nicoletta; Béchet, Frédéric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Odijk, Jan & Piperidis, Stelios (Red.), Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association. ISSN 979-10-95546-72-6. s. 4894–4903. Vis sammendrag

  The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as PolitiFact, robust detection techniques are required to deal with the increase in fake news. Several deep learning models show promising results for fake news classification, however, their black-box nature makes it difficult to explain their classification decisions and quality-assure the models. We here address this problem by proposing a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM). In brief, we utilize the conjunctive clauses of the TM to capture lexical and semantic properties of both true and fake news text. Further, we use clause ensembles to calculate the credibility of fake news. For evaluation, we conduct experiments on two publicly available datasets, PolitiFact and GossipCop, and demonstrate that the TM framework significantly outperforms previously published baselines by at least 5% in terms of accuracy, with the added benefit of an interpretable logic-based representation. In addition, our approach provides a higher F1-score than BERT and XLNet, however, we obtain slightly lower accuracy. We finally present a case study on our model’s explainability, demonstrating how it decomposes into meaningful words and their negations.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Lei, Jiao (2022). ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification. I Calzolari, Nicoletta; Béchet, Frédéric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Odijk, Jan & Piperidis, Stelios (Red.), Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association. ISSN 979-10-95546-72-6. s. 3761–3770. Vis sammendrag

  Recent advancements in natural language processing (NLP) have reshaped the industry, with powerful language models such as GPT-3 achieving superhuman performance on various tasks. However, the increasing complexity of such models turns them into “black boxes”, creating uncertainty about their internal operation and decision-making. Tsetlin Machine (TM) employs human-interpretable conjunctive clauses in propositional logic to solve complex pattern recognition problems and has demonstrated competitive performance in various NLP tasks. In this paper, we propose ConvTextTM, a novel convolutional TM architecture for text classification. While legacy TM solutions treat the whole text as a corpus-specific set-of-words (SOW), ConvTextTM breaks down the text into a sequence of text fragments. The convolution over the text fragments opens up for local position-aware analysis. Further, ConvTextTM eliminates the dependency on a corpus-specific vocabulary. Instead, it employs a generic SOW formed by the tokenization scheme of the Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2019a). The convolution binds together the tokens, allowing ConvTextTM to address the out-of-vocabulary problem as well as spelling errors. We investigate the local explainability of our proposed method using clause-based features. Extensive experiments are conducted on seven datasets, to demonstrate that the accuracy of ConvTextTM is either superior or comparable to state-of-the-art baselines.

• Yadav, Rohan Kumar; Lei, Jiao; Granmo, Ole-Christoffer & Goodwin, Morten (2022). Robust Interpretable Text Classification against Spurious Correlations Using AND-rules with Negation. I de Raed, Luc (Red.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. AAAI Press. ISSN 978-1-956792-00-3. s. 4439–4446. Vis sammendrag

  The state-of-the-art natural language processing models have raised the bar for excellent performance on a variety of tasks in recent years. However, concerns are rising over their primitive sensitivity to distribution biases that reside in the training and testing data. This issue hugely impacts the performance of the models when exposed to out-of-distribution and counterfactual data. The root cause seems to be that many machine learning models are prone to learn the shortcuts, modelling simple correlations rather than more fundamental and general relationships. As a result, such text classifiers tend to perform poorly when a human makes minor modifications to the data, which raises questions regarding their robustness. In this paper, we employ a rule-based architecture called Tsetlin Machine (TM) that learns both simple and complex correlations by ANDing features and their negations. As such, it generates explainable AND-rules using negated and non-negated reasoning. Here, we explore how non-negated reasoning can be more prone to distribution biases than negated reasoning. We further leverage this finding by adapting the TM architecture to mainly perform negated reasoning using the specificity parameter s. As a result, the AND-rules becomes robust to spurious correlations and can also correctly predict counterfactual data. Our empirical investigation of the model’s robustness uses the specifcity s to control the degree of negated reasoning. Experiments on publicly available Counterfactually-Augmented Data demonstrate that the negated clauses are robust to spurious correlations and outperform Naive Bayes, SVM, and Bi-LSTM by up to 20%, and ELMo by almost 6% on counterfactual test data.

• Lei, Jiao; Zhang, Xuan; Granmo, Ole-Christoffer & Abeyrathna, Kuruge Darshana (2022). On the Convergence of Tsetlin Machines for the XOR Operator. IEEE Transactions on Pattern Analysis and Machine Intelligence. ISSN 0162-8828. 45(5), s. 6072–6085. doi: 10.1109/TPAMI.2022.3203150. Fulltekst i vitenarkiv Vis sammendrag

  The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct properties, including transparent inference and learning using hardware-near building blocks. Although numerous papers explore the TM empirically, many of its properties have not yet been analyzed mathematically. In this article, we analyze the convergence of the TM when input is non-linearly related to output by the XOR-operator. Our analysis reveals that the TM, with just two conjunctive clauses, can converge almost surely to reproducing XOR, learning from training data over an infinite time horizon. Furthermore, the analysis shows how the hyper-parameter T guides clause construction so that the clauses capture the distinct sub-patterns in the data. Our analysis of convergence for XOR thus lays the foundation for analyzing other more complex logical expressions. These analyses altogether, from a mathematical perspective, provide new insights on why TMs have obtained the state-of-the-art performance on several pattern recognition problems.

• Jenul, Anna Selina; Bhattarai, Bimal; Liland, Kristian Hovde; Lei, Jiao; Schrunner, Stefan & Futsæther, Cecilia Marie [Vis alle 8 forfattere av denne artikkelen] (2022). Component Based Pre-filtering of Noisy Data for Improved Tsetlin Machine Modelling. I Shafik, Rishad (Red.), 2022 International Symposium on the Tsetlin Machine (ISTM 2022). IEEE conference proceedings. ISSN 978-1-6654-7116-9. s. 57–64. doi: 10.1109/ISTM54910.2022.00019. Vis sammendrag

  Tabular data with few observations and many features are common in the healthcare domain. With its rule-based approach to data modelling, the Tsetlin Machine has considerable potential to be a valuable tool in healthcare data analysis by providing interpretability to medical personnel. However, the performance of Tsetlin Machine models may be hampered by the presence of noise, which may often be the case with healthcare data due to individual differences across patients. This study shows that intelligent pre-filtering of healthcare measurement data using so-called component-based methods, such as Principal Component Analysis or Partial Least Squares Regression, can be beneficial for the performance of Tsetlin Machines. Modelling four healthcare data sets shows that the Tsetlin Machine achieved better predictive performance on pre-filtered data for data sets with high features-to-observations ratios.

• Abouzeid, Ahmed Abdulrahem Othman; Granmo, Ole-Christoffer; Webersik, Christian & Goodwin, Morten (2022). Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization. Proceedings of the AAAI Conference on Artificial Intelligence. ISSN 2159-5399. s. 11801–11809. doi: 10.1609/aaai.v36i11.21436. Vis sammendrag

  Recent social networks' misinformation mitigation approaches tend to investigate how to reduce misinformation by considering a whole-network statistical scale. However, unbalanced misinformation exposures among individuals urge to study fair allocation of mitigation resources. Moreover, the network has random dynamics which change over time. Therefore, we introduce a stochastic and non-stationary knapsack problem, and we apply its resolution to mitigate misinformation in social network campaigns. We further propose a generic misinformation mitigation algorithm that is robust to different social networks' misinformation statistics, allowing a promising impact in real-world scenarios. A novel loss function ensures fair mitigation among users. We achieve fairness by intelligently allocating a mitigation incentivization budget to the knapsack, and optimizing the loss function. To this end, a team of Learning Automata (LA) drives the budget allocation. Each LA is associated with a user and learns to minimize its exposure to misinformation by performing a non-stationary and stochastic walk over its state space. Our results show how our LA-based method is robust and outperforms similar misinformation mitigation methods in how the mitigation is fairly influencing the network users.

• Rafiev, Ashur; Morris, Jordan; Xia, Fei; Shafik, Rishad; Yakovlev, Alex & Granmo, Ole-Christoffer [Vis alle 7 forfattere av denne artikkelen] (2022). Visualization of Machine Learning Dynamics in Tsetlin Machines. I Shafik, Rishad (Red.), 2022 International Symposium on the Tsetlin Machine (ISTM 2022). IEEE conference proceedings. ISSN 978-1-6654-7116-9. s. 65–72. doi: 10.1109/ISTM54910.2022.00020. Vis sammendrag

  The black-box approach towards machine learning models is not helpful for creating fast and efficient implementations. optimization of these models requires a clear understanding of every aspect of the system, including the architecture, algorithms, and dynamics of the training process. This paper presents a range of visualizations for investigating the dynamics of the Tsetlin Machine, which is a new machine-learning algorithm with logic underpinning. These include static visualizations such as architecture and algorithm diagrams and dynamic visualizations such as plotting the evolution of the internal state of the machine. The workflow that supports the visualizations is generalized into an extendable open-source development kit that can be used with future generations of Tsetlin Machines. The example visualization diagrams from the MNIST dataset are discussed from the viewpoint of parallel implementation, hardware acceleration, and opportunities for architectural optimization.

• Abouzeid, Ahmed Abdulrahem Othman; Granmo, Ole-Christoffer; Goodwin, Morten & Webersik, Christian (2022). Label-Critic Tsetlin Machine: A Novel Self-supervised Learning Scheme for Interpretable Clustering. I Shafik, Rishad (Red.), 2022 International Symposium on the Tsetlin Machine (ISTM 2022). IEEE conference proceedings. ISSN 978-1-6654-7116-9. s. 41–48. doi: 10.1109/ISTM54910.2022.00016. Vis sammendrag

  Unlike typical machine learning algorithms such as Artificial Neural Networks, the Tsetlin Machine (TM) is based on propositional logic instead of arithmetic operations, promoting it as a novel machine learning paradigm with interpretable learning outcomes. To this end, this paper proposes a self-supervised learning scheme inspired by the self-correction and interpretability provided by a standard TM. The proposed architecture uses a twin of Label-Critic Tsetlin Automata (TAs). The Label-TA learns the individual samples’ correct labels guided by a self-corrected TM logical clause. At the same time, the Critic-TA validates the learning and approves the Label-TA reward. Our empirical results on synthetic and real data show promising capabilities for self-supervised learning and interpretable clustering. Furthermore, the Label-Critic TM architecture demonstrates how propositional logic-based learning provides self-correction with the absence of the ground truths in data.

• Rahman, Tousif; Wheeldon, Adrian; Shafik, Rishad; Yakovlev, Alex; Lei, Jie & Granmo, Ole-Christoffer [Vis alle 7 forfattere av denne artikkelen] (2022). Data Booleanization for Energy Efficient On-Chip Learning using Logic Driven AI. I Shafik, Rishad (Red.), 2022 International Symposium on the Tsetlin Machine (ISTM 2022). IEEE conference proceedings. ISSN 978-1-6654-7116-9. s. 29–36. doi: 10.1109/ISTM54910.2022.00014. Vis sammendrag

  The incorporation of Machine Learning (ML) models into smart devices and wearables has increased design focus on scalability and energy efficiency considerations. To transition towards on-device training, there must be a balance between data compaction to reduce ML complexity and data significance to maintain high learning efficacy. In this paper we examine a novel approach towards low-energy booleanization (i.e. Boolean encoding of raw data, which is different from binarization with positional significance used for encoding raw data) for a recently proposed machine learning method — the Tsetlin Machine. The approach in this paper offers a unique synergy between quantile binning for initial data encoding and logic minimization for significance-driven compaction of the dataset. Through this approach, the paper demonstrates how energy-frugal Tsetlin Machine models can be created with, for some datasets, up to 38.8x less energy consumption per datapoint during training. The proposed method offers significantly better energy efficiency with similar performance when compared with Binary Neural Network counterparts.

• Tunheim, Svein Anders; Lei, Jiao; Shafik, Rishad Ahmed; Yakovlev, Alexandre & Granmo, Ole-Christoffer (2022). A Convolutional Tsetlin Machine-based Field Programmable Gate Array Accelerator for Image Classification. I Shafik, Rishad (Red.), 2022 International Symposium on the Tsetlin Machine (ISTM 2022). IEEE conference proceedings. ISSN 978-1-6654-7116-9. s. 21–28. doi: 10.1109/ISTM54910.2022.00013. Vis sammendrag

  This paper presents a Field Programmable Gate Array (FPGA) implementation of an image classification accelerator based on the Convolutional Tsetlin Machine (CTM). The work is a concept design, and the solution demonstrates recognition of two classes in 4 × 4 images with a 2 × 2 convolution window. More specifically, there are two sub-Tsetlin Machines (TMs), one per class. A single sub-TM employs 40 clauses, each controlled by 20 Tsetlin Automata. The accelerator features random patch selection, in parallel for all clauses, based on reservoir sampling. The design is implemented in a Xilinx Zync XC7Z020 FPGA. With an operating clock speed of 30 MHz, the accelerator is capable of inferring at the rate of 3.3 million images per second with an additional power consumption of 20 mW from idle mode. The average test accuracy is 96.7% when trained on data with 10% noise. A training session with 100 epochs and 8192 examples takes 1.5 seconds. Due to the limited hardware resources required, the CTM accelerator represents a promising concept for online learning in energy-frugal systems. The solution can be scaled to multi-class systems and larger images.

• Tunheim, Svein Anders; Yadav, Rohan Kumar; Lei, Jiao; Shafik, Rishad & Granmo, Ole-Christoffer (2022). Cyclostationary Random Number Sequences for the Tsetlin Machine. I Fournier-Viger, Philippe; Fujita, Hamido; Ali, Moonis & Wang, Yinglin (Red.), Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Kitakyushu, Japan, July 19–22, 2022, Proceedings. Springer. ISSN 978-3-031-08529-1. s. 844–856. doi: https:/doi.org/10.1007/978-3-031-08530-7_71. Fulltekst i vitenarkiv Vis sammendrag

  The Tsetlin Machine (TM) constitutes an emerging machine learning algorithm that has shown competitive performance on several benchmarks. The underlying concept of the TM is propositional logic determined by a group of finite state machines that learns patterns. Thus, TM-based systems naturally lend themselves to low-power operation when implemented in hardware for micro-edge Internet-of-Things applications. An important aspect of the learning phase of TMs is stochasticity. For low-power integrated circuit implementations the random number generation must be carried out efficiently. In this paper, we explore the application of pre-generated cyclostationary random number sequences for TMs. Through experiments on two machine learning problems, i.e., Binary Iris and Noisy XOR, we demonstrate that the accuracy is on par with standard TM. We show that through exploratory simulations the required length of the sequences that meets the conflicting tradeoffs can be suitably identified. Furthermore, the TMs achieve robust performance against reduced resolution of the random numbers. Finally, we show that maximum-length sequences implemented by linear feedback shift registers are suitable for generating the required random numbers.

• 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. s. 361–368. doi: 10.1109/CoG51982.2022.9893661. Vis sammendrag

  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.

• Giri, Charul; Granmo, Ole-Christoffer; Van Hoof, Herke & Blakely, Christian Dallas (2022). Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine, 2022 International Joint Conference on Neural Networks (IJCNN). IEEE conference proceedings. ISSN 978-1-7281-8671-9. doi: 10.1109/IJCNN55064.2022.9892796. Vis sammendrag

  Hex is a turn-based two-player connection game with a high branching factor, making the game arbitrarily complex with increasing board sizes. As such, top-performing algorithms for playing Hex rely on accurate evaluation of board positions using neural networks. However, the limited interpretability of neural networks is problematic when the user wants to understand the reasoning behind the predictions made. In this paper, we propose to use propositional logic expressions to describe winning and losing board game positions, facilitating precise visual interpretation. We employ a Tsetlin Machine (TM) to learn these expressions from previously played games, describing where pieces must be located or not located for a board position to be strong. Extensive experiments on 6×6 boards compare our TM-based solution with popular machine learning algorithms like XGBoost, InterpretML, decision trees, and neural networks, considering various board configurations with 2 to 22 moves played. On average, the TM testing accuracy is 92.1%, outperforming all the other evaluated algorithms. We further demonstrate the global interpretation of the logical expressions, and map them down to particular board game configurations to investigate local interpretability. We believe the resulting interpretability establishes building blocks for accurate assistive AI and human-AI collaboration, also for more complex prediction tasks.

• Abouzeid, Ahmed Abdulrahem Othman & Granmo, Ole-Christoffer (2022). MMSS: A storytelling simulation software to mitigate misinformation on social media[Formula presented]. Software Impacts. 13. doi: 10.1016/j.simpa.2022.100341. Fulltekst i vitenarkiv Vis sammendrag

  This paper proposes a modular python implementation of a storytelling simulation. The software evaluates misinformation mitigation strategies over social media and visualizes the investigated scenarios’ potential outcomes. Our software integrates information diffusion and control models components. The control model mitigates users’ exposure to misinformation with social fairness awareness, while the diffusion model predicts the outcome from the control model. During the interaction of both models, a graph coloring algorithm traces the interaction within specific time intervals. Then, it generates meta-data to construct visuals of predicted near-future states of the social network to help support decision-making and evaluate proposed mitigation strategies.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Lei, Jiao (2022). Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines. Applied intelligence (Boston). ISSN 0924-669X. doi: 10.1007/s10489-022-03281-1. Fulltekst i vitenarkiv Vis sammendrag

  Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult to extract an exact explanation of why a document is considered novel. In addition, dealing with novelty at the word level is crucial to provide a more fine-grained analysis than what is available at the document level. In this work, we propose a Tsetlin Machine (TM)-based architecture for scoring individual words according to their contribution to novelty. Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses. We then adapt this description to measure how much a word contributes to making documents novel. Our experimental results demonstrate how our approach breaks down novelty into interpretable phrases, successfully measuring novelty.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Jiao, Lei (2021). A Tsetlin Machine Framework for Universal Outlier and Novelty Detection, Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021). SciTePress. ISSN 978-989-758-484-8. doi: 10.1007/978-3-031-10161-8_14.
• Sharma, Jivitesh; Granmo, Ole-Christoffer & Goodwin, Morten (2021). Emergency Analysis: Multitask Learning with Deep Convolutional Neural Networks for Fire Emergency Scene Parsing. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743.
• Abeyrathna, Kuruge Darshana; Granmo, Ole-Christoffer & Goodwin, Morten (2021). Convolutional Regression Tsetlin Machine: An Interpretable Approach to Convolutional Regression, 2021 6th International Conference on Machine Learning Technologies. Association for Computing Machinery (ACM). ISSN 978-1-4503-8940-2. s. 65–70. doi: 10.1145/3468891.3468901. Vis sammendrag

  The Convolutional Tsetlin Machine (CTM), a variant of Tsetlin Machine (TM), represents patterns as straightforward AND-rules, to address the high computational complexity and the lack of interpretability of Convolutional Neural Networks (CNNs). CTM has shown competitive performance on MNIST, Fashion-MNIST, and Kuzushiji-MNIST pattern classification benchmarks, both in terms of accuracy and memory footprint. In this paper, we propose the Convolutional Regression Tsetlin Machine (C-RTM) that extends the CTM to support continuous output problems in image analysis. C-RTM identifies patterns in images using the convolution operation as in the CTM and then maps the identified patterns into a real-valued output as in the Regression Tsetlin Machine (RTM). The C-RTM thus unifies the two approaches. We evaluated the performance of C-RTM using 72 different artificial datasets, with and without noise in the training data. Our empirical results show the competitive performance of C-RTM compared to two standard CNNs. Additionally, the interpretability of the identified sub-patterns by C-RTM clauses is analyzed and discussed.

• Abeyrathna, Kuruge Darshana; Bhattarai, Bimal; Goodwin, Morten; Gorji, Saeed Rahimi; Granmo, Ole-Christoffer & Lei, Jiao [Vis alle 8 forfattere av denne artikkelen] (2021). Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling. Proceedings of Machine Learning Research (PMLR). ISSN 2640-3498. Fulltekst i vitenarkiv Vis sammendrag

  Using logical clauses to represent patterns, Tsetlin Machine (TM) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning speed on several benchmarks. Each TM clause votes for or against a particular class, with classification resolved using a majority vote. While the evaluation of clauses is fast, being based on binary operators, the voting makes it necessary to synchronize the clause evaluation, impeding parallelization. In this paper, we propose a novel scheme for desynchronizing the evaluation of clauses, eliminating the voting bottleneck. In brief, every clause runs in its own thread for massive native parallelism. For each training example, we keep track of the class votes obtained from the clauses in local voting tallies. The local voting tallies allow us to detach the processing of each clause from the rest of the clauses, supporting decentralized learning. This means that the TM most of the time will operate on outdated voting tallies. We evaluated the proposed parallelization across diverse learning tasks and it turns out that our decentralized TM learning algorithm copes well with working on outdated data, resulting in no significant loss in learning accuracy. Furthermore, we show that the approach provides up to 50 times faster learning. Finally, learning time is almost constant for reasonable clause amounts (employing from 20 to 7,000 clauses on a Tesla V100 GPU). For sufficiently large clause numbers, computation time increases approximately proportionally. Our parallel and asynchronous architecture thus allows processing of more massive datasets and operating with more clauses for higher accuracy.

• Yadav, Rohan Kumar; Lei, Jiao; Granmo, Ole-Christoffer & Goodwin, Morten (2021). Enhancing Interpretable Clauses Semantically using Pretrained Word Representation, Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP . Association for Computational Linguistics. ISSN 978-1-955917-06-3. s. 265–274. doi: 10.18653/v1/2021.blackboxnlp-1.19.
• Abeyrathna, Kuruge Darshana; Granmo, Ole-Christoffer; Shafik, Rishad; Lei, Jiao; Wheeldon, Adrian & Yakovlev, Alex [Vis alle 8 forfattere av denne artikkelen] (2021). A multi-step finite-state automaton for arbitrarily deterministic Tsetlin Machine learning. Expert Systems. ISSN 0266-4720. doi: 10.1111/exsy.12836. Fulltekst i vitenarkiv
• Saha, Rupsa; Granmo, Ole-Christoffer; Zadorozhny, Vladimir & Goodwin, Morten (2021). A relational Tsetlin Machine with applications to natural language understanding. Journal of Intelligent Information Systems. ISSN 0925-9902. doi: 10.1007/s10844-021-00682-5.
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2021). ORACLE: End-to-End Model Based Reinforcement Learning. I Bramer, Max & Ellis, Richard (Red.), 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.
• Abouzeid, Ahmed Abdulrahem Othman; Granmo, Ole-Christoffer & Goodwin, Morten (2021). Modelling Emotion Dynamics in Chatbots with Neural Hawkes Processes. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. doi: 10.1007/978-3-030-91100-3_12.
• Saha, Rupsa; Granmo, Ole-Christoffer & Goodwin, Morten (2021). Using Tsetlin Machine to discover interpretable rules in natural language processing applications. Expert Systems. ISSN 0266-4720. doi: 10.1111/exsy.12873.
• Gorji, Saeed Rahimi; Granmo, Ole-Christoffer & Wiering, Marco (2021). Explainable Reinforcement Learning with the Tsetlin Machine . I Fujita, Hamido (Red.), Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. Springer Nature. ISSN 978-3-030-79456-9.
• Abeyrathna, Kuruge Darshana; Granmo, Ole-Christoffer & Goodwin, Morten (2021). Adaptive sparse representation of continuous input for tsetlin machines based on stochastic searching on the line. Electronics. ISSN 2079-9292. 10(17). doi: 10.3390/electronics10172107. Fulltekst i vitenarkiv
• Abeyrathna, Kuruge Darshana; Rasca, Sinziana Ioana; Markvica, Karin & Granmo, Ole-Christoffer (2021). Public Transport Passenger Count Forecasting in Pandemic Scenarios Using Regression Tsetlin Machine. Case Study of Agder, Norway. Smart Innovation, Systems and Technologies. ISSN 2190-3018. 231, s. 27–37. doi: 10.1007/978-981-16-2324-0_4.
• Zhang, Xuan; Lei, Jiao; Granmo, Ole-Christoffer & Goodwin, Morten (2021). On the convergence of Tsetlin machines for the IDENTITY- and NOT operators. IEEE Transactions on Pattern Analysis and Machine Intelligence. ISSN 0162-8828. 44(10), s. 6345–6359. doi: 10.1109/TPAMI.2021.3085591.
• Yadav, Rohan Kumar; Lei, Jiao; Goodwin, Morten & Granmo, Ole-Christoffer (2021). Positionless aspect based sentiment analysis using attention mechanism. Knowledge-Based Systems. ISSN 0950-7051. 226. doi: 10.1016/j.knosys.2021.107136. Fulltekst i vitenarkiv
• Yadav, Rohan Kumar; Lei, Jiao; Granmo, Ole-Christoffer & Goodwin, Morten (2021). Human-Level Interpretable Learning for Aspect-Based Sentiment Analysis . Proceedings of the AAAI Conference on Artificial Intelligence. ISSN 2159-5399. s. 14203–14212. Vis sammendrag

  This paper proposes human-interpretable learning of aspect-based sentiment analysis (ABSA), employing the recently introduced Tsetlin Machines (TMs). We attain interpretability by converting the intricate position-dependent textual semantics into binary form, mapping all the features into bag-of-words (BOWs). The binary form BOWs are encoded so that the information on the aspect and context words are nearly lossless for sentiment classification. We further adapt the BOWs as input to the TM, enabling learning of aspect-based sentiment patterns in propositional logic. To evaluate interpretability and accuracy, we conducted experiments on two widely used ABSA datasets of SemEval 2014: Restaurant 14 and Laptop 14. The experiments show how each relevant feature takes part in conjunctive clauses that contain the context information for the corresponding aspect word, demonstrating human-level interpretability. At the same time, the obtained accuracy is competitive with existing neural network models, reaching 78.02% on Restaurant 14 and 73.51% on Laptop 14.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Jiao, Lei (2021). Measuring the Novelty of Natural Language Text using the Conjunctive Clauses of a Tsetlin Machine Text Classifier, Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021). SciTePress. ISSN 978-989-758-484-8. s. 410–417. doi: 10.5220/0010382204100417.
• Abeyrathna, Kuruge Darshana; Granmo, Ole-Christoffer & Goodwin, Morten (2021). Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability. IEEE Access. ISSN 2169-3536. 9, s. 8233–8248. doi: 10.1109/ACCESS.2021.3049569. Fulltekst i vitenarkiv
• Yadav, Rohan Kumar; Lei, Jiao; Granmo, Ole-Christoffer & Goodwin, Morten (2021). Interpretability in Word Sense Disambiguation using Tsetlin Machine, Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021). SciTePress. ISSN 978-989-758-484-8. s. 402–409. doi: 10.5220/0010382104020409.
• Abouzeid, Ahmed Abdulrahem Othman; Granmo, Ole-Christoffer; Webersik, Christian & Goodwin, Morten (2021). Learning Automata-based Misinformation Mitigation via Hawkes Processes. Information Systems Frontiers. ISSN 1387-3326. 23, s. 1169–1188. doi: 10.1007/s10796-020-10102-8. Fulltekst i vitenarkiv Vis sammendrag

  Mitigating misinformation on social media is an unresolved challenge, particularly because of the complexity of information dissemination. To this end, Multivariate Hawkes Processes (MHP) have become a fundamental tool because they model social network dynamics, which facilitates execution and evaluation of mitigation policies. In this paper, we propose a novel light-weight intervention-based misinformation mitigation framework using decentralized Learning Automata (LA) to control the MHP. Each automaton is associated with a single user and learns to what degree that user should be involved in the mitigation strategy by interacting with a corresponding MHP, and performing a joint random walk over the state space. We use three Twitter datasets to evaluate our approach, one of them being a new COVID-19 dataset provided in this paper. Our approach shows fast convergence and increased valid information exposure. These results persisted independently of network structure, including networks with central nodes, where the latter could be the root of misinformation. Further, the LA obtained these results in a decentralized manner, facilitating distributed deployment in real-life scenarios.

• 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), s. 7363–7381. doi: 10.1109/TSMC.2020.2967936. Fulltekst i vitenarkiv
• 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. s. 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. Fulltekst i vitenarkiv
• Abeyrathna, Kuruge Darshana; Granmo, Ole-Christoffer & Goodwin, Morten (2020). Integer Weighted Regression Tsetlin Machines, Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices.. Springer Nature. ISSN 978-3-030-55789-8. s. 686–694. doi: 10.1007/978-3-030-55789-8_59.
• Abeyrathna, Darshana; Granmo, Ole-Christoffer; Shafik, Rishad; Yakovlev, Alex; Wheeldon, Adrian & Lei, Jie [Vis alle 7 forfattere av denne artikkelen] (2020). A Novel Multi-step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning, SGAI 2020: Artificial Intelligence XXXVII. Springer. ISSN 9783030637989. s. 108–122. doi: 10.1007/978-3-030-63799-6_8.
• Saha, Rupsa; Granmo, Ole-Christoffer & Goodwin, Morten (2020). Mining Interpretable Rules for Sentiment and Semantic Relation Analysis Using Tsetlin Machines, SGAI 2020: Artificial Intelligence XXXVII. Springer. ISSN 9783030637989. doi: 10.1007/978-3-030-63799-6_5.
• Abeyrathna, Kuruge Darshana; Granmo, Ole-Christoffer & Goodwin, Morten (2020). On Obtaining Classification Confidence, Ranked Predictions and AUC with Tsetlin Machines, 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-2547-3. s. 662–669. doi: 10.1109/SSCI47803.2020.9308460.
• Abeyrathna, Kuruge Darshana; Gardiyawasam Pussewalage, Harsha; Ranasinghe, Sasanka Niromi; Oleshchuk, Vladimir & Granmo, Ole-Christoffer (2020). Intrusion Detection with Interpretable Rules Generated Using the Tsetlin Machine, 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-2547-3. s. 1121–1130. doi: 10.1109/SSCI47803.2020.9308206.
• Abeyrathna, Kuruge Darshana; Granmo, Ole-Christoffer; Zhang, Xuan & Goodwin, Morten (2020). Adaptive Continuous Feature Binarization for Tsetlin Machines Applied to Forecasting Dengue Incidences in the Philippines, 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-2547-3. s. 2084–2092. doi: 10.1109/SSCI47803.2020.9308291.
• Sharma, Jivitesh; Granmo, Ole-Christoffer & Goodwin, Morten (2020). Emergency Detection with Environment Sound Using Deep Convolutional Neural Networks. I Yang, Xin-She; Sherratt, Simon; Dey, Nilanjan & Joshi, Amit (Red.), Proceedings of Fifth International Congress on Information and Communication Technology. Springer Nature. ISSN 978-981-15-5858-0. s. 144–154. doi: 10.1007/978-981-15-5859-7_14.
• Sharma, Jivitesh; Granmo, Ole-Christoffer & Goodwin, Morten (2020). Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network. Interspeech (USB). ISSN 2308-457X. s. 1186–1190. doi: 10.21437/Interspeech.2020-1303. Fulltekst i vitenarkiv
• Wheeldon, Adrian; Shafik, Rishad; Rahman, Tousif; Lei, Jie; Yakovlev, Alex & Granmo, Ole-Christoffer (2020). Learning automata based energy-efficient AI hardware design for IoT applications: Learning Automata based AI Hardware. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. ISSN 1364-503X. 378(2182), s. 1–18. doi: 10.1098/rsta.2019.0593. Fulltekst i vitenarkiv
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2020). Towards safe reinforcement-learning in industrial grid-warehousing. Information Sciences. ISSN 0020-0255. 537, s. 467–484. doi: 10.1016/j.ins.2020.06.010. Fulltekst i vitenarkiv
• Gorji, Saeed Rahimi; Granmo, Ole-Christoffer; Glimsdal, Sondre; Edwards, Jonathan & Goodwin, Morten (2020). Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing, Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices.. Springer Nature. ISSN 978-3-030-55789-8. s. 695–708. doi: 10.1007/978-3-030-55789-8_60.
• Yadav, Rohan Kumar; Bhattarai, Bimal; Lei, Jiao; Goodwin, Morten & Granmo, Ole-Christoffer (2020). Indoor Space Classification Using Cascaded LSTM. IEEE Conference on Industrial Electronics and Applications. ISSN 2158-2297. s. 1110–1114. doi: 10.1109/ICIEA48937.2020.9248347. Fulltekst i vitenarkiv Vis sammendrag

  Indoor space classification is an important part of localization that helps in precise location extraction, which has been extensively utilized in industrial and domestic domain. There are various approaches that employ Bluetooth Low Energy (BLE), Wi-Fi, magnetic field, object detection, and Ultra Wide Band (UWB) for indoor space classification purposes. Many of the existing approaches need extensive pre-installed infrastructure, making the cost higher to obtain reasonable accuracy. Therefore, improvements are still required to increase the accuracy with minimum requirements of infrastructure. In this paper, we propose an approach to classify the indoor space using geomagnetic field (GMF) and radio signal strength (RSS) as the identity. The indoor space is an open big test bed divided into different indiscernible subspace. We collect GMF and RSS at each subspace and classify it using cascaded Long Short Term Memory (LSTM). The experimental results show that the accuracy is significantly improved when GMF and RSS are combined to make distinct features. In addition, we compare the performance of the proposed model with the state-of-the-art machine learning methods.

• Lazreg, Mehdi Ben; Goodwin, Morten & Granmo, Ole-Christoffer (2020). Combining a context aware neural network with a denoising autoencoder for measuring string similarities . Computer Speech and Language. ISSN 0885-2308. 60. doi: 10.1016/j.csl.2019.101028.
• Lazreg, Mehdi Ben; Goodwin, Morten & Granmo, Ole-Christoffer (2019). An Iterative Information Retrieval Approach from Social Media in Crisis Situations, 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM). IEEE conference proceedings. ISSN 978-1-7281-4920-2. doi: 10.1109/ICT-DM47966.2019.9033008.
• Sharma, Jivitesh; Matheussen, Bernt Viggo; Glimsdal, Sondre & Granmo, Ole-Christoffer (2019). Hydropower Optimization Using Split-Window, Meta-Heuristic and Genetic Algorithms, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) . IEEE conference proceedings. ISSN 978-1-7281-4550-1. doi: 10.1109/ICMLA.2019.00153.

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• Shafik, Rishad; Granmo, Ole-Christoffer; Lei, Jiao & Zadorozhny, Vladimir (2022). 2022 International Symposium on the Tsetlin Machine (ISTM 2022). IEEE conference proceedings. ISBN 978-1-6654-7116-9.
• Sanfilippo, Filippo; Granmo, Ole-Christoffer; Yildirim Yayilgan, Sule & Bajwa, Imran Sarwar (2022). Intelligent Technologies and Applications: 4th International Conference, INTAP 2021, Grimstad, Norway, October 11–13, 2021, Revised Selected Papers. Springer. ISBN 978-3-031-10524-1. 434 s.
• Følstad, Asbjørn; Araujo, Theo; Papadopoulos, Symeon; Law, Effie L.-C.; Granmo, Ole-Christoffer & Luger, Ewa [Vis alle 7 forfattere av denne artikkelen] (2020). Chatbot Research and Design. Third International Workshop, CONVERSATIONS 2019, Amsterdam, The Netherlands, November 19–20, 2019, Revised Selected Papers. Springer. ISBN 978-3-030-39540-7. 273 s.

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• Granmo, Ole-Christoffer (2023). An Introduction to Tsetlin Machines.
• Tunheim, Svein Anders; Lei, Jiao & Granmo, Ole-Christoffer (2023). Model Export for the Convolutional Coalesced Tsetlin Machine.
• Przybysz, Emilia; Bhattarai, Bimal; Persia, Cosimo Damiano; Ozaki, Ana; Granmo, Ole-Christoffer & Sharma, Jivitesh (2023). Verifying Properties of Tsetlin Machines.
• Sahu, Prajwal Kumar; Boppu, Srinivas; Shafik, Rishad Ahmed; Tunheim, Svein Anders; Granmo, Ole-Christoffer & Cenkeramaddi, Linga Reddy (2023). Enhancing Inference Performance through Include only Literal Incorporation in Tsetlin Machine.
• Liland, Kristian Hovde; Tomic, Oliver; Indahl, Ulf Geir; Futsæther, Cecilia Marie; Jiao, Lei & Granmo, Ole-Christoffer [Vis alle 7 forfattere av denne artikkelen] (2023). Tsetlin Machine in DNA sequence classification. Vis sammendrag

  The Tsetlin machine (TM) is a logic-based machine learning model with the crucial advantages of transparency and hardware-friendliness. In TM, groups of Tsetlin Automata (TAs) produce Boolean expressions in the form of conjunctive clauses in AND-rules. In this work, we show that the DNA nucleotide sequence, coded using the four-letter alphabet A, C, G, T, is a perfect match to the Boolean logic-based TM. A coalesced integer-weighted TM is applied to the problem of prokaryote gene prediction demonstrating beyond state-of-the-art performance on reference genomes. This paves the way for highly efficient bioinformatic analysis in a large range of applications.

• Granmo, Ole-Christoffer (2023). Om 5 år vil det være noe helt annet å være utvikler, mener norsk AI-ekspert. [Internett]. kode24.
• Granmo, Ole-Christoffer (2022). Nå skal den norske Tsetlin-maskinen videreutvikles. [Internett]. digi.no.
• Granmo, Ole-Christoffer (2022). The Tsetlin Machine – From Arithmetic to Logic-based AI.
• Granmo, Ole-Christoffer (2022). Sannheten om Kunstig Intelligens.
• Bhattarai, Bimal & Granmo, Ole-Christoffer (2022). Tsetlin Machine Applications.
• Granmo, Ole-Christoffer & Bhattarai, Bimal (2022). Tsetlin Machine – efficient AI technology for language processing.
• Lei, Jiao; Granmo, Ole-Christoffer; Blakely, Christian Dallas & Zadorozhny, Vladimir (2021). Denne chipen skjuler en norsk oppfinnelse som kan redde liv. [Internett]. NRK.
• Sanfilippo, Filippo; Bajwa, Imran Sarwar; Granmo, Ole-Christoffer & Yildirim Yayilgan, Sule (2021). 4th International Conference on Intelligent Technologies and Applications (INTAP'21).
• 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.
• Abouzeid, Ahmed Abdulrahem Othman & Granmo, Ole-Christoffer (2023). Optimization of Multivariate Hawkes Processes via Decentralized Learning Automata: Towards Learning How to Boost Factual Information on Social Networks. Universitetet i Agder. ISSN 978-82-8427-156-9. Fulltekst i vitenarkiv
• 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.
• Glimsdal, Sondre; Granmo, Ole-Christoffer & Nyberg, Svein Olav Glesaaen (2020). Towards Thompson Sampling for Complex Bayesian Reasoning. Universitetet i Agder. ISSN 978-82-7117-976-2. Fulltekst i vitenarkiv

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