Particle Field Optimization: A New Paradigm for Swarm Intelligence

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Enhancing History-Based Move Ordering in Game Playing Using Adaptive Data Structures

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Multinomial Sequence Based Estimation Using Contiguous Subsequences of Length Three

On the Classification of Dynamical Data Streams Using Novel “Anti–Bayesian” Techniques

The classification of dynamical data streams is among the most complex problems encountered in classification. This is, firstly, because the distribution of the data streams is non-stationary, and it changes without any prior “warning”. Secondly, the manner in which it changes is also unknown. Thirdly, and more interestingly, the model operates with the assumption that the correct classes of previously-classified patterns become available at a juncture after their appearance. This paper pioneers the use of unreported novel schemes that can classify such dynamical data streams by invoking the recently-introduced “Anti- Bayesian” (AB) techniques. Contrary to the Bayesian paradigm, that compar…

On the Foundations of Multinomial Sequence Based Estimation

Solving Stochastic Root-Finding with adaptive d-ary search

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Novel AI Strategies for Multi-Player Games at Intermediate Board States

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The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements

Author's accepted manuscript 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 …

Identifying unreliable sensors without a knowledge of the ground truth in deceptive environments

This paper deals with the extremely fascinating area of “fusing” the outputs of sensors without any knowledge of the ground truth. In an earlier paper, the present authors had recently pioneered a solution, by mapping it onto the fascinating paradox of trying to identify stochastic liars without any additional information about the truth. Even though that work was significant, it was constrained by the model in which we are living in a world where “the truth prevails over lying”. Couched in the terminology of Learning Automata (LA), this corresponds to the Environment (Since the Environment is treated as an entity in its own right, we choose to capitalize it, rather than refer to it as an “…

Challenging Established Move Ordering Strategies with Adaptive Data Structures

Text Classification Using “Anti”-Bayesian Quantile Statistics-Based Classifiers

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 …

Higher-Fidelity Frugal and Accurate Quantile Estimation Using a Novel Incremental Discretized Paradigm

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Pattern Recognition using the TTOCONROT

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On Achieving History-Based Move Ordering in Adversarial Board Games using Adaptive Data Structures

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Space and depth-related enhancements of the history-ADS strategy in game playing

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On optimizing firewall performance in dynamic networks by invoking a novel swapping window-based paradigm

Designing and implementing efficient firewall strategies in the age of the Internet of Things (IoT) is far from trivial. This is because, as time proceeds, an increasing number of devices will be connected, accessed and controlled on the Internet. Additionally, an everincreasingly amount of sensitive information will be stored on various networks. A good and efficient firewall strategy will attempt to secure this information, and to also manage the large amount of inevitable network traffic that these devices create. The goal of this paper is to propose a framework for designing optimized firewalls for the IoT. This paper deals with two fundamental challenges/problems encountered in such firewalls…

On using "Stochastic learning on the line" to design novel distance estimation methods

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Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments

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On Invoking Transitivity to Enhance the Pursuit-Oriented Object Migration Automata

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