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Memetic Multi-Agent System (MeMAS) has recently emerged as a combination of memetic automaton and multi-agent system (MAS), wherein all meme-inspired agents acquire increasing learning capacity and intelligence through meme evolution. This paper further presents a study of MeMAS in developing human-like non-player characters in complex first-person shooter (FPS) games. In particular, we consider a...
In this paper we present an implementation of and a proposed algorithm for an easily expandable hardware Artificial Neural Network (ANN) capable of learning using inexpensive, off-the-shelf microprocessors. While significant work has been done in hardware ANN implementations, this research offers a unique, general use, unspecialized, and inexpensive model with a flexible architecture representation...
Many studies of material property estimation and material recognition have been conducted. Previous approaches evaluate the validity or usefulness of hand-designed image features. Thus, we propose a method to directly and naturally acquire image features for material perception using convolutional neural networks. Using a fine-tuned network, we achieved approximately the same recognition accuracy...
A new method of training deep neural networks including the convolutional network is proposed. The method deconvexifies the normalized risk-averting error (NRAE) gradually and switches to the risk-averting error (RAE) whenever RAE is computationally manageable. The method creates tunnels between the depressed regions around saddle points, tilts the plateaus, and eliminates nonglobal local minima....
EEG based vowel classification is currently gaining importance for its increasing applications in the next generation mind-driven type-writing. This paper addresses a novel approach to classify the mentally uttered alphabets in a specific three lettered format, where the first and the last letter represent two vowel sounds and the middle is a space, where no character is imagined. Such formatting...
Brains learn much better than computers. But why? Is there a fundamental reason behind computers being slow learners? Often slow learning is described as computational complexity. This paper discusses that complexity of algorithms is as fundamental as Gödelian incompleteness of logic. Although the Gödel's theory is well recognized, its significance for engineering and modeling of the mind has not...
The theory and experiments outlined in Weng 2015 [1] modeled brains as naturally emerging Turing Machines (TMs) inside Developmental Networks (DNs) — a new class of brain inspired neural networks. However, TMs originally proposed by Alan Turing 1936 [2] were deterministic. If they involved probability to handle uncertainty, the probability was in the mind of the human programmer for a specific task...
D-Wave 2X with more than 1000 qubits was applied to the relatively rugged energy landscape of trained Restricted Boltzmann Machines (RBMs). The D-Wave machine has a Chimera interconnect architecture. A native RBM restricted to the Chimera graph was found difficult to train for large number of RBM units. To overcome this difficulty, a RBM embedding that combined qubits in order to significantly increase...
Transfer learning plays a powerful role in mitigating the discrepancy between test data (target) and auxiliary data (source). There is often the case that multiple sources are available in transfer learning. However, naively combining multiple sources does not lead to valid results, since they will introduce negative transfer as well. Furthermore, each single source from multiple sources may not cover...
Capsule endoscopy (CE), introduced as a modality for non-invasive examination of entire gastrointestinal tract, demands for an efficient computer-aided decision making system to relieve the physician from the responsibility of screening around 60,000 video frames per patient. An automatic and robust segmentation algorithm can aid the automation of CE screening and decision making procedure. In this...
Temporal alignment aligns two temporal sequences and is quite challenging due to drastic differences among temporal sequences and source data from different views. Canonical time warping (CTW) has shown great potential in temporal alignment tasks because it can reduce data redundancy by transforming high-dimensional data to a lower-dimensional subspace via canonical correlation analysis (CCA). However,...
In this study, we present a new approach to the problem of face classification, which relies on the linguistic description of the facial features. In this method, face descriptors are represented through the Analytic Hierarchy Process (AHP) and formalized as information granules. Moreover, neural networks are used to construct efficient classifiers. Furthermore, with usage of AHP we realize a transition...
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