In the early visual cortex V1, there are currently only two known neural substrates for color perception: single-opponent and double-opponent cells. Our aim was to explore the relative contributions of these neurons to color perception. We measured the perceptual scaling of color saturation for equiluminant color checkerboard patterns (designed to stimulate double-opponent neurons preferentiall...

The contribution of colour opponent mechanisms to detection thresholds is investigated at different spatial frequencies by presenting monochromatic, sinusoidal gratings on a uniform white background. Colour opponent mechanisms, characterised by a triple peaked spectral sensitivity function, determine threshold at low spatial frequencies (below 1 c/deg) and their contribution flattens the Weber ...

Information about the opponent is essential to improve automated negotiation strategies for bilateral multi-issue negotiation. In this paper we propose a negotiation strategy that combines a Bayesian technique to learn the preferences of an opponent during bidding and a Tit-for-Tat-like strategy to avoid exploitation by the opponent. The learned opponent model is used to achieve two important g...

Stimuli varying in intensity and chromaticity, presented on numerous backgrounds, were classified into red/green, blue/yellow and white/black opponent color categories. These measurements revealed the shapes of the boundaries that separate opponent colors in three-dimensional color space. Opponent color classification boundaries were generally not planar, but their shapes could be summarized by...

In a world of limited resources, scarcity and rivalry are central challenges for decision makers. We examine choice behavior in competitive probability learning environments that reinforce one of two strategies. The optimality of a strategy is dependent on the behavior of a computerized opponent: if the opponent mimics participant choices, probability matching is optimal; if the opponent is ind...

The paper argues for the equipment of argument games with richer game-theoretic features. Concretely, it tackles the question of what happens to argument games when proponent and opponent are uncertain about the attack graph upon which they are playing. This simple sort of uncertainty, we argue, caters for the modeling of several strategic phenomena of real-life arguments. Using the argument ga...

This paper describes the modeling approaches used for the Subfigure Classification subtask at ImageCLEF 2016 by the FHDO Biomedical Computer Science Group (BCSG). Besides traditional feature engineering, modern Deep Convolutional Neural Networks (DCNN) were used, trained from scratch and using a transfer learning scenario. In addition Bag-of-Visual-Words (BoVW) were computed in Opponent color s...

Robust AI systems need to be able to reason about their goals and formulate new goals based on the given situation. Case-based goal formulation is a technique for formulating new goals for an agent using a library of examples. We provide a formalization of this term and two algorithms that implement this definition. The algorithms are compared against classification techniques on the tasks of o...

One challenge for cognitive architectures is to effectively use different forms of knowledge and learning. We present a case study of Soar agents that play a multiplayer dice game, in which probabilistic reasoning and heuristic symbolic knowledge appear to play a central role. We develop and evaluate a collection of agents that use different combinations of probabilistic decision making, heuris...

Living organisms adapt to challenges through evolution and adaptation. These survival mechanisms have proven to be a key difficulty in developing therapies, since the challenged organisms develop resistance. It would be desirable to harness evolution/adaptation for therapeutic, technological, and scientific goals. I propose steering them strategically using computational game theory and opponen...