John Basantkumar Oommen

• Hassan, Ismail; Oommen, John & Yazidi, Anis (2023). Adaptive learning with artificial barriers yielding Nash equilibria in general games. Knowledge engineering review (Print). ISSN 0269-8889. 38. doi: 10.1017/S0269888923000103. Vis sammendrag

  Artificial barriers in Learning Automata (LA) is a powerful and yet under-explored concept although it was first proposed in the 1980s. Introducing artificial non-absorbing barriers makes the LA schemes resilient to being trapped in absorbing barriers, a phenomenon which is often referred to as lock in probability leading to an exclusive choice of one action after convergence. Within the field of LA and reinforcement learning in general, there is a sacristy of theoretical works and applications of schemes with artificial barriers. In this paper, we devise a LA with artificial barriers for solving a general form of stochastic bimatrix game. Classical LA systems possess properties of absorbing barriers and they are a powerful tool in game theory and were shown to converge to game’s of Nash equilibrium under limited information. However, the stream of works in LA for solving game theoretical problems can merely solve the case where the Saddle Point of the game exists in a pure strategy and fail to reach mixed Nash equilibrium when no Saddle Point exists for a pure strategy. Furthermore, we provide experimental results that are in line with our theoretical findings.

• Omslandseter, Rebekka Olsson; Lei, Jiao & Oommen, John (2023). Pioneering approaches for enhancing the speed of hierarchical LA by ordering the actions. Information Sciences. ISSN 0020-0255. 647, s. 1–17. doi: 10.1016/j.ins.2023.119487. Fulltekst i vitenarkiv Vis sammendrag

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

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

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

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

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

• Omslandseter, Rebekka Olsson; Jiao, Lei; Zhang, Xuan; Yazidi, Anis & Oommen, John (2022). The Hierarchical Discrete Pursuit Learning Automaton: A Novel Scheme with Fast Convergence and Epsilon-Optimality. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X. 35(6), s. 8278–8292. doi: 10.1109/TNNLS.2022.3226538. Fulltekst i vitenarkiv
• Omslandseter, Rebekka Olsson; Jiao, Lei; Zhang, Xuan; Yazidi, Anis & Oommen, John (2022). The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13151, s. 507–518. doi: 10.1007/978-3-030-97546-3_41. Fulltekst i vitenarkiv Vis sammendrag

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

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

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

• Oommen, John; Zhang, Xuan & Lei, Jiao (2022). A Comprehensive Survey of Estimator Learning Automata and Their Recent Convergence Results. Lecture Notes in Networks and Systems. ISSN 2367-3370. 289, s. 33–52. doi: 10.1007/978-3-030-87049-2_2. Vis sammendrag

  The pre-cursor field to Reinforcement Learning is that of Learning Automata (LA). Within this field, Estimator Algorithms (EAs) can be said to be the state-of-the-art. Further, the subset of Pursuit Algorithms (PAs), discovered by Thathachar and Sastry [34, 39], were the pioneering schemes. This chapter contains a comprehensive survey of the various EAs, and the most recent convergence results for PAs. Unlike the prior LA, EAs are based on a fundamentally distinct phenomenon. They are also the most accurate LA, converging in the least time. EAs operate on two vectors, namely, the action probability vector which is updated using responses from the Environment, and quickly-computed estimates of the reward probabilities of the various actions. The proofs that they are ε-optimal is thus very complex. They have to incorporate two rather snon-orthogonal phenomena, which are the convergence of these estimates and the convergence of the probabilities of selecting the various actions. For almost three decades, the reported proofs of PAs possessed an infirmity (or flaw), which we refer to as the claim of the “monotonicity” property. This flaw was discovered by the authors of [37], who also provided an alternate proof for a specific PA where the scheme’s parameter decreased with time. This paper first records all the reported EAs. It then reports a comprehensive survey of the proofs from a different perspective. These proofs have not required that the sequence of action probabilities of selecting the optimal action satisfies the property of monotonicity. On the other hand, whenever any action probability is close enough to unity, we require that the process jumps to an absorbing barrier at the next time instant, i.e., in a single step. By requiring such a constraint, these proofs invoke the weaker property, i.e., the submartinagale property of pm(t), to demonstrate the ε-optimality. We have thus proven the ε-optimality for the Absorbing CPA [49, 50], the Discretized PA [51, 52], and for the family of Bayesian PA [53], where the estimates are obtained by a Bayesian (rather than a Maximum Likelihood (ML)) process.

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

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

• Omslandseter, Rebekka Olsson; Jiao, Lei & Oommen, John (2021). Object Migration Automata for Non-equal Partitioning Problems with Known Partition Sizes. IFIP Advances in Information and Communication Technology. ISSN 1868-4238. doi: 10.1007/978-3-030-79150-6_11. Fulltekst i vitenarkiv
• Omslandseter, Rebekka Olsson; Jiao, Lei & Oommen, John (2021). A Learning-Automata Based Solution for Non-equal Partitioning: Partitions with Common GCD Sizes. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 12799, s. 227–239. doi: 10.1007/978-3-030-79463-7_19. Fulltekst i vitenarkiv
• Ghani, Tahira & Oommen, John (2021). On utilizing 2D features from 3D scans to enhance the prediction of lung cancer survival rates. Pattern Recognition Letters. ISSN 0167-8655. 152, s. 56–62. doi: 10.1016/j.patrec.2021.09.001.
• Yazidi, Anis; Silvestre, Daniel & Oommen, John (2021). Solving Two-Person Zero-Sum Stochastic Games With Incomplete Information Using Learning Automata With Artificial Barriers. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X. 34(2), s. 650–661. doi: 10.1109/TNNLS.2021.3099095. Fulltekst i vitenarkiv
• Bisong, O. Ekaba & Oommen, John (2021). On utilizing the transitivity pursuit-enhanced object partitioning to optimize self-organizing lists-on-lists. Evolving Systems. ISSN 1868-6478. 12(3), s. 655–686. doi: 10.1007/s12530-021-09378-1.
• Yazidi, Anis; Hassan, Ismail; Hammer, Hugo Lewi & Oommen, John (2021). Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X. 32(8), s. 3444–3457. doi: 10.1109/TNNLS.2020.3010888. Fulltekst i vitenarkiv
• Helmy, Ibrahim & Oommen, John (2020). A Novel Learning Automata-Based Strategy to Generate Melodies from Chordal Inputs. I Maglogiannis, Ilias; Iliadis, Lazaros & Pimenidis, Elias (Red.), Artificial Intelligence Applications and Innovations. AIAI 2020. Springer Nature. ISSN 978-3-030-49160-4. s. 203–215. doi: 10.1007/978-3-030-49. Fulltekst i vitenarkiv
• Bisong, O. Ekaba & Oommen, John (2020). Optimizing Self-organizing Lists-on-Lists Using Transitivity and Pursuit-Enhanced Object Partitioning. I Maglogiannis, Ilias; Iliadis, Lazaros & Pimenidis, Elias (Red.), Artificial Intelligence Applications and Innovations. AIAI 2020. Springer Nature. ISSN 978-3-030-49160-4. s. 227–240. doi: 10.1007/978-3-030-49161-1_20. Fulltekst i vitenarkiv
• Ghani, Tahira & Oommen, John (2020). Enhancing the Prediction of Lung Cancer Survival Rates Using 2D Features from 3D Scans. I Campilho, A. (Red.), Image Analysis and Recognition. ICIAR 2020. Springer Nature. ISSN 978-3-030-50515-8. s. 202–215. doi: 10.1007/978-3-030-50516-5_18. Fulltekst i vitenarkiv
• Ghani, Tahira & Oommen, John (2020). Novel Block Diagonalization for Reducing Features and Computations in Medical Diagnosis. I Gallagher, Marcus; Moustafa, Nour & Lakshika, Erandi (Red.), AI 2020: Advances in Artificial Intelligence. Springer Nature. ISSN 978-3-030-64983-8. s. 42–54. doi: 10.1007/978-3-030-64984-5_4. Fulltekst i vitenarkiv
• Mahmoudi, Fatemeh; Razmkhah, Mostafa & Oommen, John (2020). Nonparametric “anti-Bayesian” quantile-based pattern classification. Pattern Analysis and Applications. ISSN 1433-7541. 24, s. 75–87. doi: 10.1007/s10044-020-00903-7. Fulltekst i vitenarkiv
• Ghaleb, Omar & Oommen, John (2020). On solving single elevator-like problems using a learning automata-based paradigm. Evolving Systems. ISSN 1868-6478. doi: 10.1007/s12530-020-09325-6. Fulltekst i vitenarkiv
• Bisong, O. Ekaba & Oommen, John (2020). On utilizing an enhanced object partitioning scheme to optimize self-organizing lists-on-lists. Evolving Systems. ISSN 1868-6478. doi: 10.1007/s12530-020-09327-4. Fulltekst i vitenarkiv
• Omslandseter, Rebekka Olsson; Lei, Jiao; Liu, Yuanwei & Oommen, John (2020). User Grouping and Power Allocation in NOMA Systems: A Reinforcement Learning-Based Solution, Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices.. Springer Nature. ISSN 978-3-030-55789-8. s. 299–311. doi: 10.1007/978-3-030-55789-8_27. Fulltekst i vitenarkiv
• Ghaleb, Omar & Oommen, John (2019). Learning Automata-Based Solutions to the Single Elevator Problem. I MacIntyre, J.; Maglogiannis, Ilias; Iliadis, L. & Pimenidis, Elias (Red.), Artificial Intelligence Applications and Innovations. AIAI 2019. Springer. ISSN 978-3-030-19822-0. s. 439–450. doi: 10.1007/978-3-030-19823-7_37.
• Ghaleb, Omar & Oommen, John (2019). Learning Automata-Based Solutions to the Multi-Elevator Problem. I Huang, D. S.; Huang, Z. K. & Hussain, A. (Red.), Intelligent Computing Methodologies. ICIC 2019. Springer. ISSN 978-3-030-26765-0. s. 130–141. doi: 10.1007/978-3-030-26766-7_13.
• Yazidi, Anis; Zhang, Xuan; Lei, Jiao & Oommen, John (2019). The Hierarchical Continuous Pursuit Learning Automation: A Novel Scheme for Environments With Large Numbers of Actions. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X. doi: 10.1109/TNNLS.2019.2905162. Fulltekst i vitenarkiv
• Perez, Nicolas & Oommen, John (2019). Multi-Minimax: A new AI paradigm for simultaneously-played multi-player games. I Liu, Jixue & Bailey, James (Red.), AI 2019: Advances in Artificial Intelligence: 32nd Australasian Joint Conference, Adelaide, SA, Australia, December 2–5, 2019, Proceedings Editors. Springer Nature. ISSN 978-3-030-35288-2. s. 41–53. doi: 10.1007/978-3-030-35288-2_4.
• Havelock, Jessica; Oommen, John & Granmo, Ole-Christoffer (2019). On Using “Stochastic Learning on the Line” to Design Novel Distance Estimation Methods for Three-Dimensional Environments. I Wotawa, F; Friedrich, G; Koitz-Hristov, R. & Ali, M (Red.), Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Springer. ISSN 978-3-030-22998-6. s. 39–49. doi: 10.1007/978-3-030-22999-3_4.
• Tavasoli, Hanane; Oommen, John & Yazidi, Anis (2019). On utilizing weak estimators to achieve the online classification of data streams. Engineering Applications of Artificial Intelligence. ISSN 0952-1976. 86, s. 11–31. doi: 10.1016/j.engappai.2019.08.015. Fulltekst i vitenarkiv
• Shirvani, Abdolreza & Oommen, John (2019). The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data. IFIP Advances in Information and Communication Technology. ISSN 1868-4238. 559, s. 3–16. doi: 10.1007/978-3-030-19823-7_1.
• Shirvani, Abdolreza & Oommen, John (2019). On enhancing the deadlock-preventing object migration automaton using the pursuit paradigm. Pattern Analysis and Applications. ISSN 1433-7541. doi: 10.1007/s10044-019-00817-z.
• Zhang, Xuan; Jiao, Lei; Oommen, John & Granmo, Ole-Christoffer (2019). A Conclusive Analysis of the Finite-Time Behavior of the Discretized Pursuit Learning Automaton. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X. doi: 10.1109/TNNLS.2019.2900639. Fulltekst i vitenarkiv Vis sammendrag

  This paper deals with the finite-time analysis (FTA) of learning automata (LA), which is a topic for which very little work has been reported in the literature. This is as opposed to the asymptotic steady-state analysis for which there are, probably, scores of papers. As clarified later, unarguably, the FTA of Markov chains, in general, and of LA, in particular, is far more complex than the asymptotic steady-state analysis. Such an FTA provides rigid bounds for the time required for the LA to attain to a given convergence accuracy. We concentrate on the FTA of the Discretized Pursuit Automaton (DPA), which is probably one of the fastest and most accurate reported LA. Although such an analysis was carried out many years ago, we record that the previous work is flawed. More specifically, in all brevity, the flaw lies in the wrongly ``derived'' monotonic behavior of the LA after a certain number of iterations. Rather, we claim that the property should be invoked is the submartingale property. This renders the proof to be much more involved and deep. In this paper, we rectify the flaw and reestablish the FTA based on such a submartingale phenomenon. More importantly, from the derived analysis, we are able to discover and clarify, for the first time, the underlying dilemma between the DPA's exploitation and exploration properties. We also nontrivially confirm the existence of the optimal learning rate, which yields a better comprehension of the DPA itself.

• Yazidi, Anis & Oommen, John (2018). Novel Results on Random Walk-Jump Chains That Possess Tree-Based Transitions. Advances in Intelligent Systems and Computing. ISSN 2194-5357. 578, s. 43–52. doi: 10.1007/978-3-319-59162-9_5.
• Taucer, Armando H.; Polk, Spencer & Oommen, John (2018). On Addressing the Challenges of Complex Stochastic Games Using “Representative” Moves. I Lazaros, Iliadis (Red.), Artificial Intelligence Applications and Innovations. Springer. ISSN 978-3-319-92006-1. s. 3–13. doi: 10.1007/978-3-319-92007-8_1. Fulltekst i vitenarkiv
• Yazidi, Anis; Zhang, Xuan; Lei, Jiao & Oommen, John (2018). The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions. I Lazaros, Iliadis (Red.), Artificial Intelligence Applications and Innovations. Springer. ISSN 978-3-319-92006-1. s. 451–461. doi: 10.1007/978-3-319-92007-8_38. Fulltekst i vitenarkiv
• Havelock, Jessica; Oommen, John & Granmo, Ole-Christoffer (2018). On using "Stochastic learning on the line" to design novel distance estimation methods. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 10868 LNAI, s. 34–42. doi: 10.1007/978-3-319-92058-0_4. Fulltekst i vitenarkiv
• Shirvani, Abdolreza & Oommen, John (2018). On Invoking Transitivity to Enhance the Pursuit-Oriented Object Migration Automata. IEEE Access. ISSN 2169-3536. 6, s. 21668–21681. doi: 10.1109/ACCESS.2018.2827305. Fulltekst i vitenarkiv
• Havelock, Jessica; Oommen, John & Granmo, Ole-Christoffer (2018). Novel Distance Estimation Methods Using 'Stochastic Learning on the Line' Strategies. IEEE Access. ISSN 2169-3536. 6, s. 48438–48454. doi: 10.1109/ACCESS.2018.2868233. Fulltekst i vitenarkiv
• Mohan, Ratish; Yazidi, Anis; Feng, Boning & Oommen, John (2018). On optimizing firewall performance in dynamic networks by invoking a novel swapping window-based paradigm. International Journal of Communication Systems. ISSN 1074-5351. 31(15). doi: 10.1002/dac.3773. Fulltekst i vitenarkiv
• Yazidi, Anis & Oommen, John (2018). On the analysis of a random walk-jump chain with tree-based transitions and its applications to faulty dichotomous search. Sequential Analysis. ISSN 0747-4946. 37(1), s. 31–46. doi: 10.1080/07474946.2018.1427971. Fulltekst i vitenarkiv
• Yazidi, Anis; Hammer, Hugo Lewi & Oommen, John (2018). Higher-Fidelity Frugal and Accurate Quantile Estimation Using a Novel Incremental Discretized Paradigm. IEEE Access. ISSN 2169-3536. 6, s. 24362–24374. doi: 10.1109/ACCESS.2018.2820501. Fulltekst i vitenarkiv
• McMahon, Thomas & Oommen, John (2018). Enhancing English-Japanese Translation Using Syntactic Pattern Recognition Methods, Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. Springer. ISSN 9783319591612. doi: 10.1007/978-3-319-59162-9_4.
• Jobava, Akaki; Yazidi, Anis; Oommen, John & Begnum, Kyrre (2018). On achieving intelligent traffic-aware consolidation of virtual machines in a data center using Learning Automata. Journal of Computational Science. ISSN 1877-7503. 24, s. 290–312. doi: 10.1016/j.jocs.2017.08.005. Fulltekst i vitenarkiv
• Hammer, Hugo Lewi; Yazidi, Anis & Oommen, John (2018). On the Classification of Dynamical Data Streams Using Novel “Anti–Bayesian” Techniques. Pattern Recognition. ISSN 0031-3203. 76, s. 108–124. doi: 10.1016/j.patcog.2017.10.031. Fulltekst i vitenarkiv
• Shirvani, Abdolreza & Oommen, John (2017). On Utilizing the Pursuit Paradigm to Enhance the Deadlock-Preventing Object Migration Automaton, International Conference on New Trends in Computing Sciences, ICTCS 2017. IEEE conference proceedings. ISSN 978-1-5386-0527-1. s. 295–302. doi: 10.1109/ICTCS.2017.40.
• Shirvani, Abdolreza & Oommen, John (2017). Partitioning in signal processing using the object migration automaton and the pursuit paradigm, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE Signal Processing Society. ISSN 978-1-5090-6341-3. doi: 10.1109/MLSP.2017.8168149.
• Oommen, John & Kim, Sang-Woon (2017). Occlusion-based estimation of independent multinomial random variables using occurrence and sequential information. Engineering Applications of Artificial Intelligence. ISSN 0952-1976. 63, s. 69–84. doi: 10.1016/j.engappai.2017.05.001.
• Shirvani, Abdolreza & Oommen, John (2017). On enhancing the object migration automaton using the Pursuit paradigm. Journal of Computational Science. ISSN 1877-7503. 24, s. 329–342. doi: 10.1016/j.jocs.2017.08.008. Fulltekst i vitenarkiv
• Thapa, Rajan; Lei, Jiao; Oommen, John & Yazidi, Anis (2017). A learning automaton-based scheme for scheduling domestic shiftable loads in smart grids. IEEE Access. ISSN 2169-3536. 6, s. 5348–5361. doi: 10.1109/ACCESS.2017.2788051. Fulltekst i vitenarkiv
• Polk, Spencer & Oommen, John (2017). Challenging state-of-the-art move ordering with Adaptive Data Structures. Applied intelligence (Boston). ISSN 0924-669X. s. 1–20. doi: 10.1007/s10489-017-1006-0.
• Thapa, Rajan; Lei, Jiao; Oommen, John & Yazidi, Anis (2017). Scheduling domestic shiftable loads in smart grids: A learning automata-based scheme. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. ISSN 1867-8211. 203, s. 58–68. doi: 10.1007/978-3-319-61813-5_6.
• Yazidi, Anis; Oommen, John & Goodwin, Morten (2017). Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 10604 , s. 741–753. doi: 10.1007/978-3-319-69179-4_52. Fulltekst i vitenarkiv
• Yazidi, Anis; Hammer, Hugo Lewi & Oommen, John (2017). A higher-fidelity frugal quantile estimator. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 10604 LNAI, s. 76–86. doi: 10.1007/978-3-319-69179-4_6.
• Hammer, Hugo Lewi; Yazidi, Anis & Oommen, John (2017). On using novel 'Anti-Bayesian' techniques for the classification of dynamical data streams, 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE conference proceedings. ISSN 978-1-5090-4601-0. s. 1173–1182. doi: 10.1109/CEC.2017.7969439. Fulltekst i vitenarkiv
• Hammer, Hugo Lewi; Yazidi, Anis & Oommen, John (2017). “Anti-Bayesian” flat and hierarchical clustering using symmetric quantiloids. Information Sciences. ISSN 0020-0255. 418-419, s. 495–512. doi: 10.1016/j.ins.2017.08.017. Fulltekst i vitenarkiv
• Yazidi, Anis & Oommen, John (2017). A novel technique for stochastic root-finding: Enhancing the search with adaptive d-ary search. Information Sciences. ISSN 0020-0255. 393, s. 108–129. doi: 10.1016/j.ins.2017.02.014. Fulltekst i vitenarkiv
• Mohan, Ratish; Yazidi, Anis; Feng, Boning & Oommen, John (2016). Dynamic Ordering of Firewall Rules Using a Novel Swapping Window-based Paradigm. I NN, NN (Red.), Proceedings of the 6th International Conference on Communication and Network Security (ICCNS '16). Association for Computing Machinery (ACM). ISSN 978-1-4503-4783-9. s. 11–20. doi: 10.1145/3017971.3017975. Fulltekst i vitenarkiv
• Oommen, John & Kim, Sang-Woon (2016). Multinomial Sequence Based Estimation Using Contiguous Subsequences of Length Three. I Campilho, Aurélio & Karray, Fakhri (Red.), 13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel. Springer. ISSN 978-3-319-41501-7. s. 243–253. doi: 10.1007/978-3-319-41501-7_28. Fulltekst i vitenarkiv
• Oommen, John; Qin, Ke & Calitoiu, Dragos (2016). The Science and Art of Chaotic Pattern Recognition. I Skiadas, Christos H: & Skiadas, Charilaos (Red.), Applications of Chaos Theory. CRC Press. ISSN 9781466590441. s. 745–802. doi: 10.1201/b20232-47.
• Jobava, Akaki; Yazidi, Anis; Oommen, John & Begnum, Kyrre (2016). Achieving Intelligent Traffic-aware Consolidation of Virtual Machines in a Data Center Using Learning Automata . I Badra, Mohamad; Pau, Giovanni & Vassiliou, Vasos (Red.), 2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS). IEEE conference proceedings. ISSN 978-1-5090-2914-3. doi: 10.1109/NTMS.2016.7792430. Fulltekst i vitenarkiv
• Polk, Spencer & Oommen, John (2016). Challenging Established Move Ordering Strategies with Adaptive Data Structures. I Fujita, Hamido; Ali, Moonis; Selamat, Ali; Sasaki, Jun & Kurematsu, Masaki (Red.), Trends in Applied Knowledge-Based Systems and Data Science. 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, August 2-4, 2016, Proceedings. Springer. ISSN 978-3-319-42006-6. s. 862–872. doi: 10.1007/978-3-319-42007-3_73. Fulltekst i vitenarkiv
• Tavasoli, Hanane; Oommen, John & Yazidi, Anis (2016). On the Online Classification of Data Streams Using Weak Estimators. I Fujita, Hamido; Ali, Moonis; Selamat, Ali; Sasaki, Jun & Kurematsu, Masaki (Red.), Trends in Applied Knowledge-Based Systems and Data Science. 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, August 2-4, 2016, Proceedings. Springer. ISSN 978-3-319-42006-6. s. 68–79. doi: 10.1007/978-3-319-42007-3_7. Fulltekst i vitenarkiv
• Oommen, John & Kim, Sang-Woon (2016). On the Foundations of Multinomial Sequence Based Estimation, Computational Collective Intelligence, 8th International Conference, ICCCI 2016, Halkidiki, Greece, September 28-30, 2016. Proceedings, Part I. Springer. ISSN 978-3-319-45242-5. s. 218–229. doi: 10.1007/978-3-319-45243-2_20. Fulltekst i vitenarkiv
• Astudillo, César A.; Gonzalez, Javier I.; Oommen, John & Yazidi, Anis (2016). Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers, AI 2016: Advances in Artificial Intelligence. Springer. ISSN 978-3-319-50126-0. s. 175–182. doi: 10.1007/978-3-319-50127-7_14. Fulltekst i vitenarkiv Vis sammendrag

  In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of information theory to automatically identify changes in the performance of the classifier, and consequently, forces the reconstruction of the classification model in run-time as and when it is needed. These properties have been confirmed experimentally over numerous data sets (In the interest of space and brevity, we present here only a subset of the available results. More detailed results are found in [2].) from different domains. As far as we know, our histogram-based Naïve Bayes classification paradigm for time-varying datasets is both novel and of a pioneering sort.

• Astudillo, César A.; Poblete, Jorge; Resta, Marina & Oommen, John (2016). A Cluster Analysis of Stock Market Data Using Hierarchical SOMs, AI 2016: Advances in Artificial Intelligence. Springer. ISSN 978-3-319-50126-0. s. 101–112. doi: 10.1007/978-3-319-50127-7_8. Fulltekst i vitenarkiv Vis sammendrag

  The analysis of stock markets has become relevant mainly because of its financial implications. In this paper, we propose a novel methodology for performing a structured cluster analysis of stock market data. Our proposed method uses a tree-based neural network called the TTOSOM. The TTOSOM performs self-organization to construct tree-based clusters of vector data in the multi-dimensional space. The resultant tree possesses interesting mathematical properties such as a succinct representation of the original data distribution, and a preservation of the underlying topology. In order to demonstrate the capabilities of our method, we analyze 206 assets of the Italian stock market. We were able to establish topological relationships between various companies traded on the Italian stock market and visually inspect the resultant taxonomy. The results that we obtained, briefly reported here (but more elaborately in [10]), were amazingly accurate and reflected the real-life relationships between the stocks.

• Oommen, John; Khoury, Richard & Schmidt, Aron (2016). Text Classification Using “Anti”-Bayesian Quantile Statistics-Based Classifiers. Transactions on Computational Collective Intelligence. ISSN 2190-9288. doi: 10.1007/978-3-662-53580-6_7. Fulltekst i vitenarkiv Vis sammendrag

  The problem of Text Classification (TC) has been studied for decades, and this problem is particularly interesting because the features are derived from syntactic or semantic indicators, while the classification, in and of itself, is based on statistical Pattern Recognition (PR) strategies. Thus, all the recorded TC schemes work using the fundamental paradigm that once the statistical features are inferred from the syntactic/semantic indicators, the classifiers themselves are the well-established ones such as the Bayesian, the Na¨ıve Bayesian, the SVM etc. and those that are neural or fuzzy. In this paper, we shall demonstrate that by virtue of the skewed distributions of the features, one could advantageously work with information latent in certain “non-central” quantiles (i.e., those distant from the mean) of the distributions. We, indeed, demonstrate that such classifiers exist and are attainable, and show that the design and implementation of such schemes work with the recentlyintroduced paradigm of Quantile Statistics (QS)-based classifiers(The foundational properties for CMQS (for generic and some straightforward distributions) were initially described in [17]. Their properties for uni-dimensional distributions of the exponential family are included in [9], and for multi-dimensional distributions in [18]. The authors of [17], [9] and [18] had initially proposed their results as being based on the Order-Statistics of the distributions. This was later corrected in [19], where they showed that their results were rather based on their Quantile Statistics.). These classifiers, referred to as Classification by Moments of Quantile Statistics (CMQS), are essentially “Anti”-Bayesian in their modus operandi. To achieve our goal, in this paper we demonstrate the power and potential of CMQS to describe the very high-dimensional TC-related vector spaces in terms of a limited number of “outlier-based” statistics. Thereafter, the PR task in classification invokes the CMQS classifier for the underlying multi-class problem by using a linear number of pair-wise CMQS-based classifiers. By a rigorous testing on the standard 20-Newsgroups corpus we show that CMQS-based TC attains accuracy that is comparable to the best-reported classifiers. We also propose the potential of fusing the results of a CMQS-based methodology with those obtained from a more traditional scheme.

• Zhang, Xuan; Oommen, John & Granmo, Ole-Christoffer (2016). The design of absorbing Bayesian pursuit algorithms and the formal analyses of their ε-optimality. Pattern Analysis and Applications. ISSN 1433-7541. s. 1–12. doi: 10.1007/s10044-016-0535-1. Fulltekst i vitenarkiv Vis sammendrag

  The fundamental phenomenon that has been used to enhance the convergence speed of learning automata (LA) is that of incorporating the running maximum likelihood (ML) estimates of the action reward probabilities into the probability updating rules for selecting the actions. The frontiers of this field have been recently expanded by replacing the ML estimates with their corresponding Bayesian counterparts that incorporate the properties of the conjugate priors. These constitute the Bayesian pursuit algorithm (BPA), and the discretized Bayesian pursuit algorithm. Although these algorithms have been designed and efficiently implemented, and are, arguably, the fastest and most accurate LA reported in the literature, the proofs of their ϵϵ-optimal convergence has been unsolved. This is precisely the intent of this paper. In this paper, we present a single unifying analysis by which the proofs of both the continuous and discretized schemes are proven. We emphasize that unlike the ML-based pursuit schemes, the Bayesian schemes have to not only consider the estimates themselves but also the distributional forms of their conjugate posteriors and their higher order moments—all of which render the proofs to be particularly challenging. As far as we know, apart from the results themselves, the methodologies of this proof have been unreported in the literature—they are both pioneering and novel.

• Polk, Spencer & Oommen, John (2016). Novel threat-based AI strategies that incorporate adaptive data structures for multi-player board games. Applied intelligence (Boston). ISSN 0924-669X. s. 1–19. doi: 10.1007/s10489-016-0835-6. Fulltekst i vitenarkiv Vis sammendrag

  This paper considers the problem of designing novel techniques for multi-player game playing, in a range of board games and configurations. Compared to the well-known case of two-player game playing, multi-player game playing is a more complex problem with unique requirements. To address the unique challenges of this domain, we examine the potential of employing techniques inspired by Adaptive Data Structures (ADSs) to rank opponents based on their relative threats, and using this information to achieve gains in move ordering and tree pruning. We name our new technique the Threat-ADS heuristic. We examine the Threat-ADS’ performance within a range of game models, employing a number of different, well-understood update mechanisms for ADSs. We then extend our analysis to specifically consider intermediate board states, which are more interesting than the initial board state, as we do not assume the availability of “Opening book” moves, and where substantial variation can exist, in terms of available moves and threatening opponents. We expand this analysis to include an exploration of the Threat-ADS heuristic’s performance in deeper ply game trees, to confirm that it maintains its benefits even when lookahead is greater, and with an expanded examination of how the number of players present in the game impacts the performance of the Threat-ADS heuristic. We find that in nearly all environments, the Threat-ADS heuristic is able to produce meaningful, statistically significant improvements in tree pruning, demonstrating that it serves as a very reliable move ordering heuristic for multi-player game playing under a wide range of configurations, thus motivating the use of ADS-based techniques within the field of game playing.

• Bell, Nathan & Oommen, John (2016). A novel abstraction for swarm intelligence: particle field optimization. Autonomous Agents and Multi-Agent Systems. ISSN 1387-2532. 31(2), s. 362–385. doi: 10.1007/s10458-016-9350-8. Fulltekst i vitenarkiv Vis sammendrag

  Particle swarm optimization (PSO) is a popular meta-heuristic for black-box optimization. In essence, within this paradigm, the system is fully defined by a swarm of “particles” each characterized by a set of features such as its position, velocity and acceleration. The consequent optimized global best solution is obtained by comparing the personal best solutions of the entire swarm. Many variations and extensions of PSO have been developed since its creation in 1995, and the algorithm remains a popular topic of research. In this work we submit a new, abstracted perspective of the PSO system, where we attempt to move away from the swarm of individual particles, but rather characterize each particle by a field or distribution. The strategy that updates the various fields is akin to Thompson’s sampling. By invoking such an abstraction, we present the novel particle field optimization algorithm which harnesses this new perspective to achieve a model and behavior which is completely distinct from the family of traditional PSO systems

• Yazidi, Anis; Hammer, Hugo Lewi & Oommen, John (2016). “Anti-Bayesian” Flat and Hierarchical Clustering Using Symmetric Quantiloids. I Fujita, Hamido; Ali, Moonis; Selamat, Ali; Sasaki, Jun & Kurematsu, Masaki (Red.), Trends in Applied Knowledge-Based Systems and Data Science. 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, August 2-4, 2016, Proceedings. Springer. ISSN 978-3-319-42006-6. s. 56–67. doi: 10.1007/978-3-319-42007-3_6. Fulltekst i vitenarkiv
• Yazidi, Anis; Oommen, John; Horn, Geir Henrik & Granmo, Ole-Christoffer (2016). Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments. Pattern Recognition. ISSN 0031-3203. 60, s. 430–443. doi: 10.1016/j.patcog.2016.05.001. Fulltekst i vitenarkiv
• Yazidi, Anis; Oommen, John & Goodwin, Morten (2016). On solving the problem of identifying unreliable sensors without a knowledge of the ground truth: the case of stochastic environments. IEEE Transactions on Cybernetics. ISSN 2168-2267. 47(7), s. 1604–1617. doi: 10.1109/TCYB.2016.2552979. Fulltekst i vitenarkiv Vis sammendrag

  The purpose of this paper is to propose a solution to an extremely pertinent problem, namely, that of identifying unreliable sensors (in a domain of reliable and unreliable ones) without any knowledge of the ground truth. This fascinating paradox can be formulated in simple terms as trying to identify stochastic liars without any additional information about the truth. Though apparently impossible, we will show that it is feasible to solve the problem, a claim that is counterintuitive in and of itself. One aspect of our contribution is to show how redundancy can be introduced, and how it can be effectively utilized in resolving this paradox. Legacy work and the reported literature (for example, in the so-called weighted majority algorithm) have merely addressed assessing the reliability of a sensor by comparing its reading to the ground truth either in an online or an offline manner. Unfortunately, the fundamental assumption of revealing the ground truth cannot be always guaranteed (or even expected) in many real life scenarios. While some extensions of the Condorcet jury theorem [9] can lead to a probabilistic guarantee on the quality of the fused process, they do not provide a solution to the unreliable sensor identification problem. The essence of our approach involves studying the agreement of each sensor with the rest of the sensors, and not comparing the reading of the individual sensors with the ground truth—as advocated in the literature. Under some mild conditions on the reliability of the sensors, we can prove that we can, indeed, filter out the unreliable ones. Our approach leverages the power of the theory of learning automata (LA) so as to gradually learn the identity of the reliable and unreliable sensors. To achieve this, we resort to a team of LA, where a distinct automaton is associated with each sensor. The solution provided here has been subjected to rigorous experimental tests, and the results presented are, in our opinion, both novel and conclusive.

• Polk, Spencer & Oommen, John (2016). On Achieving History-Based Move Ordering in Adversarial Board Games using Adaptive Data Structures. Transactions on Computational Collective Intelligence. ISSN 2190-9288. 9655, s. 10–44. doi: 10.1007/978-3-662-49619-0_2. Fulltekst i vitenarkiv
• Lei, Jiao; Zhang, Xuan; Oommen, John & Granmo, Ole-Christoffer (2016). Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach. Applied intelligence (Boston). ISSN 0924-669X. 44(2), s. 307–321. doi: 10.1007/s10489-015-0682-x. Fulltekst i vitenarkiv
• Yazidi, Anis & Oommen, John (2016). Novel Discretized Weak Estimators Based on the Principles of the Stochastic Search on the Line Problem. IEEE Transactions on Cybernetics. ISSN 2168-2267. 46(12), s. 2732–2744. doi: 10.1109/TCYB.2015.2487338. Vis sammendrag

  Generally speaking, research in the field of estimation involves designing strong estimators, i.e., those which converge with probability 1, as the number of samples increases indefinitely. But when the underlying distribution is nonstationary, one should rather seek for weak estimators, i.e., those which can unlearn when the distribution has changed. One such estimator, the so-called stochastic learning weak estimator (SLWE) was based on the principles of continuous stochastic learning automata (LA). A problem that has been unsolved has been that of designing such weak estimators in the context of systems with finite memory, which is what we investigate here. In this paper, we propose a new family of stochastic discretized weak estimators which can track time-varying binomial1 distributions. As opposed to the SLWE, our proposed estimator is discretized,2 i.e., the estimate can assume only a finite number of values. By virtue of discretization, our estimator realizes extremely fast adjustments of the running estimates by executing jumps, and it is thus able to robustly, and very quickly, track changes in the parameters of the distribution after a switch has occurred. The design principle of our strategy is based on a solution for the stochastic search on the line problem. In order to achieve efficient estimation, we have to first infer (or rather simulate) an Artificial Oracle which informs the LA whether to go right or left, which is then utilized to infer whether we are to increase the current estimate or to decrease it. This paper briefly reports pioneering and conclusive experimental results that demonstrate the ability of the proposed estimator to cope with nonstationary environments.

• Yazidi, Anis; Oommen, John & Goodwin, Morten (2015). On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth. I Einoshin, Suzuki (Red.), IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015, Volume II.. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4673-9618-9. s. 104–111. doi: 10.1109/WI-IAT.2015.237. Fulltekst i vitenarkiv
• Polk, Spencer & Oommen, John (2015). Novel AI Strategies for Multi-Player Games at Intermediate Board States. I Ali, Moonis; Sig Kwon, Young; Lee, Chang-Hwan; Kim, Juntae & Kim, Yongdai (Red.), Current Approaches in Applied Artificial Intelligence, 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, South Korea, June 10-12, 2015, Proceedings. Springer. ISSN 978-3-319-19066-2. s. 33–42. doi: 10.1007/978-3-319-19066-2_4. Fulltekst i vitenarkiv
• Bell, Nathan & Oommen, John (2015). Particle Field Optimization: A New Paradigm for Swarm Intelligence, AAMAS '15 Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems. The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). ISSN 978-1-4503-3413-6. s. 257–265. Fulltekst i vitenarkiv
• Astudillo, César A. & Oommen, John (2015). Pattern Recognition using the TTOCONROT. I Ali, Moonis; Sig Kwon, Young; Lee, Chang-Hwan; Kim, Juntae & Kim, Yongdai (Red.), Current Approaches in Applied Artificial Intelligence, 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, South Korea, June 10-12, 2015, Proceedings. Springer. ISSN 978-3-319-19066-2. s. 435–444. doi: 10.1007/978-3-319-19066-2_42. Fulltekst i vitenarkiv
• Polk, Spencer & Oommen, John (2015). Space and depth-related enhancements of the history-ADS strategy in game playing, 2015 IEEE Conference on Computational Intelligence and Games. IEEE conference proceedings. ISSN 978-1-4799-8621-7. s. 322–327. doi: 10.1109/CIG.2015.7317956. Fulltekst i vitenarkiv
• Polk, Spencer & Oommen, John (2015). Enhancing History-Based Move Ordering in Game Playing Using Adaptive Data Structures. I Nunez, Manuel (Red.), Computational Collective Intelligence, 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part I. Springer. ISSN 978-3-319-24069-5. s. 225–235. doi: 10.1007/978-3-319-24069-5_21. Fulltekst i vitenarkiv
• Yazidi, Anis & Oommen, John (2015). Solving Stochastic Root-Finding with adaptive d-ary search, 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4673-6698-4. doi: 10.1109/EAIS.2015.7368782. Fulltekst i vitenarkiv
• Qin, Ke & Oommen, John (2015). On the Cryptanalysis of Two Cryptographic Algorithms That Utilize Chaotic Neural Networks. Mathematical Problems in Engineering. ISSN 1024-123X. 2015. doi: 10.1155/2015/468567. Fulltekst i vitenarkiv
• Oommen, John; Khoury, Richard & Schmidt, Aron (2015). Text Classification Using Novel “Anti-Bayesian” Techniques. I Nunez, Manuel (Red.), Computational Collective Intelligence, 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part I. Springer. ISSN 978-3-319-24069-5. doi: 10.1007/978-3-319-24069-51. Fulltekst i vitenarkiv
• Li, Yifeng; Oommen, John; Ngom, Alioune & Rueda, Luis (2015). Pattern classification using a new border identification paradigm: The nearest border technique. Neurocomputing. ISSN 0925-2312. 157, s. 105–117. doi: 10.1016/j.neucom.2015.01.030. Fulltekst i vitenarkiv
• Zhang, Xuan; Oommen, John; Granmo, Ole-Christoffer & Lei, Jiao (2015). A formal proof of the e-optimality of discretized pursuit algorithms. Applied intelligence (Boston). ISSN 0924-669X. doi: 10.1007/s10489-015-0670-1. Fulltekst i vitenarkiv
• Hammer, Hugo Lewi; Yazidi, Anis & Oommen, John (2015). A Novel Clustering Algorithm based on a Non-parametric "Anti-Bayesian" Paradigm. I Ali, Moonis; Kwon, Sig Young; Chang-Hwan, Lee; Kim, Juntae & Kim, Yongdai (Red.), 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer. ISSN 978-3-319-19066-2. s. 536–545. doi: 10.1007/978-3-319-19066-2_52. Fulltekst i vitenarkiv
• Sakhravi, Rokhsareh; Omran, Masoud T. & Oommen, John (2014). On the existence and heuristic computation of the solution for the commons game. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 8615, s. 71–99. doi: 10.1007/978-3-662-44509-9_4.

Se alle arbeider i Cristin

• Oommen, John (2020). Anti-Bayesian Statistical Pattern Recognition.
• Yazidi, Anis & Oommen, John (2017). The theory and application of the stochastic point localization problem, International Conference on New Trends in Computing Sciences, ICTCS 2017. IEEE conference proceedings. ISSN 978-1-5386-0527-1. s. 333–341. doi: http:/doi.ieeecomputersociety.org/10.1109/ICTCS.2017.70.
• Yazidi, Anis & Oommen, John (2017). The theory and application of the stochastic point localization problem.
• Oommen, John (2016). Sequence Based Estimation of Multinomial Random Variables. I Khoury, Richard & Drummond, Christopher (Red.), Advances in Artificial Intelligence. Springer. ISSN 9783319341118. s. XIII–XIV.
• Yazidi, Anis & Oommen, John (2015). Solving Stochastic Root-Finding with adaptive d-ary search.
• Yazidi, Anis; Oommen, John & Goodwin, Morten (2015). On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth.
• Oommen, John (2015). Text Classification Using Novel “Anti-Bayesian” Techniques.
• Hammer, Hugo Lewi; Yazidi, Anis & Oommen, John (2015). A Novel Clustering Algorithm based on a Non-parametric "Anti-Bayesian" Paradigm.
• Oommen, John (2015). Anti-Bayesian Statistical Pattern Recognition.
• Oommen, John (2015). Anti-Bayesian Statistical Pattern Recognition.
• Oommen, John (2015). Optimal and Information Theoretic Syntactic Pattern Recognition.
• Thomas, Anu & Oommen, John (2014). Erratum: Three papers that deal with Anti -Bayesian Pattern Recognition (Pattern Recognition). Pattern Recognition. ISSN 0031-3203. 47(6), s. 2301–2302. doi: 10.1016/j.patcog.2014.01.001.

Se alle arbeider i Cristin