configure -enable-client=sdl & make -j8 & make install Yast -i lib SDL-devel libSDL-image-devel sdl-mixer-devel sdl-mixer-devel gcc-c++ make
This is an example that worked on with freeciv 2.2.3 on SUSE Linux 11.3 If for some reason the easy way does not work, here is how you can get freeciv the hard way:
If you have a x86_64 system, visit and download the x86_64 variant of freeciv.If you have a 32bit Intel system, visit and download the i586 variant of freeciv.Find out if you have a 32bit or 64bit system.Results show that this analysis can successfully capture feature importance and uncertainty in predictions to guide additional data collection for mission design exploration.To download, install and start freeciv, find out your distribution and proceed accordingly: Design changes to a sample game are introduced based on important features of the game identified by SHAP analysis. The model confidence is evaluated using Monte Carlo Dropout Networks (MCDN), and an explanation model is built using SHapley Additive exPlanations (SHAP). A neural network model is then trained based on gameplay data obtained from the specified experiments to predict the probability of a player winning given any game state. Mission parameters are varied according to a DOE over chosen player bots and possible initial conditions of the microRTS game. The objective considered in this use case is game balance, observed through the probability of each player winning. We demonstrate this framework using an open-source real-time strategy (RTS) game called microRTS as our mission environment. This framework combines design of experiments (DOEs) techniques for data collection, meta-modeling with machine learning models, and uncertainty quantification (UQ) and explainable AI (XAI) techniques to validate the model and explore the mission design space. We present a framework to enable efficient mission design by efficiently building a surrogate model of the mission simulation environment to assist with design tasks. In this paper, we adapt computational design approaches, widely used by the engineering design community, to address unique challenges associated with mission design. Mission design is a challenging problem, requiring designers to consider complex design spaces and dynamically evolving mission environments. Journal of Verification, Validation and Uncertainty Quantification.Journal of Thermal Science and Engineering Applications.Journal of Offshore Mechanics and Arctic Engineering.Journal of Nuclear Engineering and Radiation Science.Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems.Journal of Nanotechnology in Engineering and Medicine.Journal of Micro and Nano-Manufacturing.Journal of Manufacturing Science and Engineering.Journal of Engineering Materials and Technology.Journal of Engineering for Sustainable Buildings and Cities.Journal of Engineering for Gas Turbines and Power.Journal of Engineering and Science in Medical Diagnostics and Therapy.Journal of Electrochemical Energy Conversion and Storage.Journal of Dynamic Systems, Measurement, and Control.Journal of Computing and Information Science in Engineering.Journal of Computational and Nonlinear Dynamics.