Blar i AURA på forfatter "Oommen, B. John"
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Potential AI Strategies to Solve the Commons Game : A Position Paper
Verkhogliad, Petro; Oommen, B. John (Lecture Notes in Computer Science; no. 6085, Journal article; Peer reviewed, 2010)In this paper, we propose the use of hill climbing and particle swarm optimization to find strategies in order to play the Commons Game (CG). The game, which is a non-trivial N-person non-zero-sum game, presents a simple ... -
Random Early Detection for Congestion Avoidance in Wired Networks: A Discretized Pursuit Learning-Automata-Like Solution
Misra, Sudip; Oommen, B. John; Yanamandra, Sreekeerthy; Obaidat, Mohammad S. (Journal article; Peer reviewed, 2010)In this paper, we present a learning-automata-like (LAL) mechanism for congestion avoidance in wired networks. Our algorithm, named as LAL random early detection (LALRED), is founded on the principles of the operations of ... -
Semi-supervised classification using tree-based self-organizing maps
Astudillo, César A.; Oommen, B. John (Lecture Notes in Computer Science;7106, Chapter; Peer reviewed, 2011)This paper presents a classifier which uses a tree-based Neural Network (NN), and uses both, unlabeled and labeled instances. First, we learn the structure of the data distribution in an unsupervised manner. After convergence, ... -
Service selection in stochastic environments: a learning-automaton based solution
Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, B. John (Journal article; Peer reviewed, 2011)In this paper, we propose a novel solution to the problem of identifying services of high quality. The reported solutions to this problem have, in one way or the other, resorted to using so-called “Reputation Systems” ... -
Solving Multiconstraint Assignment Problems Using Learning Automata
Horn, Geir; Oommen, B. John (Journal article; Peer reviewed, 2010)This paper considers the NP-hard problem of object assignment with respect to multiple constraints: assigning a set of elements (or objects) into mutually exclusive classes (or groups), where the elements which are ... -
Solving Stochastic Nonlinear Resource Allocation Problems Using a Hierarchy of Twofold Resource Allocation Automata.
Granmo, Ole-Christoffer; Oommen, B. John (Journal article; Peer reviewed, 2010)In a multitude of real-world situations, resources must be allocated based on incomplete and noisy information. However, in many cases, incomplete and noisy information render traditional resource allocation techniques ... -
The bayesian pursuit algorithm: A new family of estimator learning automata
Zhang, Xuan; Granmo, Ole-Christoffer; Oommen, B. John (Lecture Notes in Computer Science;6704, Chapter; Peer reviewed, 2011)The fastest Learning Automata (LA) algorithms currently available come from the family of estimator algorithms. The Pursuit algorithm (PST), a pioneering scheme in the estimator family, obtains its superior learning speed ... -
The fundamental theory of optimal "Anti-Bayesian" parametric pattern classification using order statistics criteria
Thomas, A.; Oommen, B. John (Journal article; Peer reviewed, 2013)The gold standard for a classifier is the condition of optimality attained by the Bayesian classifier. Within a Bayesian paradigm, if we are allowed to compare the testing sample with only a single point in the feature ... -
Tracking the preferences of users using weak estimators
Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, B. John (Lecture Notes in Computer Science;7106, Chapter; Peer reviewed, 2011)Since a social network, by definition, is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications which range from service recommender systems to ... -
Ultimate Order Statistics-Based Prototype Reduction Schemes
Thomas, Anu; Oommen, B. John (Lecture Notes in Computer Science;8272, Chapter; Peer reviewed, 2013)The objective of Prototype Reduction Schemes (PRSs) and Border Identification (BI) algorithms is to reduce the number of training vectors, while simultaneously attempting to guarantee that the classifier built on the reduced ... -
Using artificial intelligence techniques for strategy generation in the Commons game
Verkhogliad, Petro; Oommen, B. John (Lecture Notes in Computer Science;6678, Chapter; Peer reviewed, 2011)In this paper, we consider the use of artificial intelligence techniques to aid in discovery of winning strategies for the Commons Game (CG). The game represents a common scenario in which multiple parties share the use ...