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Ole-Christoffer Granmo

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13 papers

2 author rows

AAAI Conference 2025 Conference Paper

Generalized Convergence Analysis of Tsetlin Automaton Based Algorithms: A Probabilistic Approach to Concept Learning

Mohamed-Bachir Belaid
Jivitesh Sharma
Lei Jiao
Ole-Christoffer Granmo
Per-Arne Andersen
Anis Yazidi

Tsetlin Machines (TMs) have garnered increasing interest for their ability to learn concepts via propositional formulas and their proven efficiency across various application domains. Despite this, the convergence proof for the TMs, particularly for the AND operator (conjunction of literals), in the generalized case (inputs greater than two bits) remains an open problem. This paper aims to fill this gap by presenting a comprehensive convergence analysis of Tsetlin automaton-based Machine Learning algorithms. We introduce a novel framework, referred to as Probabilistic Concept Learning (PCL), which simplifies the TM structure while incorporating dedicated feedback mechanisms and dedicated inclusion/exclusion probabilities for literals. Given n features, PCL aims to learn a set of conjunction clauses Ci each associated with a distinct inclusion probability pi. Most importantly, we establish a theoretical proof confirming that, for any clause k, PCL converges to a conjunction of literals when pk is between 0.5 and 1. This result serves as a stepping stone for future research on the convergence properties of Tsetlin automaton-based learning algorithms. Our findings not only contribute to the theoretical understanding of Tsetlin automaton-based learning algorithms but also have implications for their practical application, potentially leading to more robust and interpretable machine learning models.

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NMR Workshop 2023 Conference Paper

A Logic-Based Explainable Framework for Relation Classification of Human Rights Violations

Bimal Bhattarai
Rupsa Saha
Ole-Christoffer Granmo
Vladimir Zadorozhny
Jiawei Xu 0003

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.

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IJCAI Conference 2023 Conference Paper

Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size

K. Darshana Abeyrathna
Ahmed A. O. Abouzeid
Bimal Bhattarai
Charul Giri
Sondre Glimsdal
Ole-Christoffer Granmo
Lei Jiao
Rupsa Saha

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 classification, 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 and BBC Sports. After the accuracy peaks, it drops gracefully as the clause size approaches one literal. We finally analyze CSC-TM power consumption and derive new convergence properties.

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AAAI Conference 2023 Conference Paper

Drop Clause: Enhancing Performance, Robustness and Pattern Recognition Capabilities of the Tsetlin Machine

Jivitesh Sharma
Rohan Yadav
Ole-Christoffer Granmo
Lei Jiao

Logic-based machine learning has the crucial advantage of transparency. However, despite significant recent progress, further research is needed to close the accuracy gap between logic-based architectures and deep neural network ones. This paper introduces a novel variant of the Tsetlin machine (TM) that randomly drops clauses, the logical learning element of TMs. In effect, TM with Drop Clause ignores a random selection of the clauses in each epoch, selected according to a predefined probability. In this way, the TM learning phase becomes more diverse. To explore the effects that Drop Clause has on accuracy, training time and robustness, we conduct extensive experiments on nine benchmark datasets in natural language processing (IMDb, R8, R52, MR, and TREC) and image classification (MNIST, Fashion MNIST, CIFAR-10, and CIFAR-100). Our proposed model outperforms baseline machine learning algorithms by a wide margin and achieves competitive performance compared with recent deep learning models, such as BERT-Large and AlexNet-DFA. In brief, we observe up to +10% increase in accuracy and 2x to 4x faster learning than for the standard TM. We visualize the patterns learnt by Drop Clause TM in the form of heatmaps and show evidence of the ability of drop clause to learn more unique and discriminative patterns. We finally evaluate how Drop Clause affects learning robustness by introducing corruptions and alterations in the image/language test data, which exposes increased learning robustness.

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IJCAI Conference 2022 Conference Paper

Robust Interpretable Text Classification against Spurious Correlations Using AND-rules with Negation

Rohan Kumar Yadav
Lei Jiao
Ole-Christoffer Granmo
Morten Goodwin

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 specificity 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.

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AAAI Conference 2022 Conference Paper

Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization

Ahmed Abouzeid
Ole-Christoffer Granmo
Christian Webersik
Morten Goodwin

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.

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NeurIPS Conference 2022 Conference Paper

Tsetlin Machine for Solving Contextual Bandit Problems

Raihan Seraj
Jivitesh Sharma
Ole-Christoffer Granmo

This paper introduces an interpretable contextual bandit algorithm using Tsetlin Machines, which solves complex pattern recognition tasks using propositional (Boolean) logic. The proposed bandit learning algorithm relies on straightforward bit manipulation, thus simplifying computation and interpretation. We then present a mechanism for performing Thompson sampling with Tsetlin Machine, given its non-parametric nature. Our empirical analysis shows that Tsetlin Machine as a base contextual bandit learner outperforms other popular base learners on eight out of nine datasets. We further analyze the interpretability of our learner, investigating how arms are selected based on propositional expressions that model the context.

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AAAI Conference 2021 Conference Paper

Human-Level Interpretable Learning for Aspect-Based Sentiment Analysis

Rohan K Yadav
Lei Jiao
Ole-Christoffer Granmo
Morten Goodwin

This paper proposes a human-interpretable learning approach for 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 retained for sentiment classification. We further adopt 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 from 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 on par with existing neural network models, reaching 78. 02% on Restaurant 14 and 73. 51% on Laptop 14.

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ICML Conference 2021 Conference Paper

Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling

Kuruge Darshana Abeyrathna
Bimal Bhattarai
Morten Goodwin
Saeed Rahimi Gorji
Ole-Christoffer Granmo
Lei Jiao 0001
Rupsa Saha
Rohan Kumar Yadav

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.

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JMLR Journal 2019 Journal Article

Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems

Sondre Glimsdal
Ole-Christoffer Granmo

The multi-armed bandit problem forms the foundation for solving a wide range of online stochastic optimization problems through a simple, yet effective mechanism. One simply casts the problem as a gambler who repeatedly pulls one out of N slot machine arms, eliciting random rewards. Learning of reward probabilities is then combined with reward maximization, by carefully balancing reward exploration against reward exploitation. In this paper, we address a particularly intriguing variant of the multi-armed bandit problem, referred to as the Stochastic Point Location (SPL) problem. The gambler is here only told whether the optimal arm (point) lies to the “left” or to the “right” of the arm pulled, with the feedback being erroneous with probability $1-\pi$. This formulation thus targets optimization in continuous action spaces with both informative and deceptive feedback. To tackle this class of problems, we formulate a compact and scalable Bayesian representation of the solution space that simultaneously captures both the location of the optimal arm as well as the probability of receiving correct feedback. We further introduce the accompanying Thompson Sampling guided Stochastic Point Location (TS-SPL) scheme for balancing exploration against exploitation. By learning $\pi$, TS-SPL also supports deceptive environments that are lying about the direction of the optimal arm. This, in turn, allows us to address the fundamental Stochastic Root Finding (SRF) problem. Empirical results demonstrate that our scheme deals with both deceptive and informative environments, significantly outperforming competing algorithms both for SRF and SPL. [abs] [ pdf ][ bib ] &copy JMLR 2019. ( edit, beta )

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EAAI Journal 2015 Journal Article

Fire simulation-based adaptation of SmartRescue App for serious game: Design, setup and user experience

Jaziar Radianti
Mehdi Ben Lazreg
Ole-Christoffer Granmo

Managing the crisis by embracing game and simulation elements and human participation into an interactive system is a mean to learn about responding to unexpected events. This so-called serious game approach is adopted in a summer school for crisis management attended by doctoral students and practitioners, as a part of its learning curriculum. The participants took part in the Disaster in my Backyard serious game, designed as a realistic crisis environment. A smartphone app encompassing fire simulations of a five-story apartment, showing how the flame, smoke and temperature of the fire developed over time from floor to floor, was tested in this serious game scenario. The color-coding of smoke and temperature information indicating the danger levels was used as a guide and decision support for the rescue team to evacuate victims out of the burning apartment. In this paper, we elaborate the underlying technology, design, and setup of the app. Finally, we discuss the evaluation of the user experience, and the merits and shortcomings of the app for search and rescue activity in a serious game fire situation.

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EAAI Journal 2011 Journal Article

Learning automata-based solutions to the optimal web polling problem modelled as a nonlinear fractional knapsack problem

Ole-Christoffer Granmo
B. John Oommen

We consider the problem of polling web pages as a strategy for monitoring the world wide web. The problem consists of repeatedly polling a selection of web pages so that changes that occur over time are detected. In particular, we consider the case where we are constrained to poll a maximum number of web pages per unit of time, and this constraint is typically dictated by the governing communication bandwidth, and by the speed limitations associated with the processing. Since only a fraction of the web pages can be polled within a given unit of time, the issue at stake is one of determining which web pages are to be polled, and we attempt to do it in a manner that maximizes the number of changes detected. We solve the problem by first modelling it as a stochastic nonlinear fractional knapsack problem. We then present an online learning automata (LA) system, namely, the hierarchy of twofold resource allocation automata (H-TRAA), whose primitive component is a twofold resource allocation automaton (TRAA). Both the TRAA and the H-TRAA have been proven to be asymptotically optimal. Finally, we demonstrate empirically that the H-TRAA provides orders of magnitude faster convergence compared to the learning automata knapsack game (LAKG) which represents the state-of-the-art for this problem. Further, in contrast to the LAKG, the H-TRAA scales sub-linearly. Based on these results, we believe that the H-TRAA has also tremendous potential to handle demanding real-world applications, particularly those which deal with the world wide web.

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EAAI Journal 2010 Journal Article

Combining finite learning automata with GSAT for the satisfiability problem

Noureddine Bouhmala
Ole-Christoffer Granmo

A large number of problems that occur in knowledge-representation, learning, very large scale integration technology (VLSI-design), and other areas of artificial intelligence, are essentially satisfiability problems. The satisfiability problem refers to the task of finding a satisfying assignment that makes a Boolean expression evaluate to True. The growing need for more efficient and scalable algorithms has led to the development of a large number of SAT solvers. This paper reports the first approach that combines finite learning automata with the greedy satisfiability algorithm (GSAT). In brief, we introduce a new algorithm that integrates finite learning automata and traditional GSAT used with random walk. Furthermore, we present a detailed comparative analysis of the new algorithm's performance, using a benchmark set containing randomized and structured problems from various domains.

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