A Trees on Manifolds approach to N-Gram predictors on Monte Carlo Geometric Parsings of Dirichlet Domains
A Trees on Manifolds approach to SLAM inspired A.I for 3D Games using .NET.
We built a Cellular Automata that queries Sensorial Location Awareness for Avatars on Dirichlet Domains; In some way, we want to solve an NP-Hard problem of 'puzzle building'. We are Inspired by extensive research in Cognitive Robotics and Remote Sensing employing Monte-Carlo Geometric Parsing of Z-predictive Trees on Manifolds. Our work is a generalization that is console agnostic to Unity3D, our platform of interest.
Our experimentation outdid Unity3D’s built-in Pathfinders and custom Agents; impressively responsive to changes and a lifelike abidance to real-time constraint parameterization and vertex actualization. A character, or avatar, is able to sense using ANN, converting a bayesian classification of stochastic adversarial states based on Causality and Kinematics. We smell footsteps, we hear a movement, and so forth.