Evidence-based pandemonium.

Letter perception: from pixels to pandemonium.

In 1959, Oliver Selfridge proposed a model of letter perception, the Pandemonium model, in which the central hypothesis was that letters are identified via their component features. Although a consensus developed around this general approach over the years, key evidence in its favor remained lacking. Recent research has started to provide important evidence in favor of feature-based letter perc...

Multi Agent Systems meet Pandemonium

AKIRA is an open-source framework integrating MAS and Pandemonium features. Here we discuss AKIRA’s peculiarities and compare it with some of the most known MAS platforms. We show how to use AKIRA for Agent based modeling, providing some examples. We also describe a client-server scheme allowing for a kind of ‘middleware’, e.g. an AI server for game actors, intelligent tutoring systems, or deci...

The field of psychological science is in a pandemonium. With failures to replicate well-established effects, evidence for a skewed picture of science in the published literature, and a media hype

© Schmalz This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and redistribution in any medium, provided that the original author and source are credited. The field of psychological science is in a pandemonium. With failures to replicate well-established effects, evidence for a skewed picture of...

A template-matching pandemonium recognizes unconstrained handwritten characters with high accuracy

Psychological data suggest that internal representations such as mental images can be used as templates in visual pattern recognition. But computational studies suggest that traditional template matching is insufficient for high-accuracy recognition of real-life patterns such as handwritten characters. Here we explore a model for visual pattern recognition that combines a template-matching and ...

Session 3 Paper 6 Pandemonium : a Paradigm for Learning

The pandemonium system of reflective agents

The Pandemonium system of reflective MINOS agents solves problems by automatic dynamic modularization of the input space. The agents contain feedforward neural networks which adapt using the backpropagation algorithm. We demonstrate the performance of Pandemonium on various categories of problems. These include learning continuous functions with discontinuities, separating two spirals, learning...