Super Mario Bros, Neural Network with Genetic Algorithm [check comments for more info] (twitch.tv) It's a machine learning neural network made in lua by SethBling The source code is here: http://pastebin.com/ZZmSNaHX You can find a video by the creator with more explanation: https://www.youtube.com/watch?v qv6UVOQ0F44 You don't give credit there.

The rise of neural networks and generative design have created new opportunities for designers. But what if it went the other way, and robots created a Skynet that kills off human designers (or at least their careers) once and for all? Depending on whether you embrace or fear the robo-future of design, Mark Maker (via Sidebar) could be considered either the beginning of the end, or proof that such fears are overstated, because bots are still pretty crap at design. The system then uses a genetic algorithm--a kind of program that mimics natural selection--to generate an endless succession of logos. When you like a logo, you click a heart, which tells the system to generate more logos like it.

This paper presents a solution to Autonomous Underwater Vehicles (AUVs) large scale route planning and task assignment joint problem. Given a set of constraints (e.g., time) and a set of task priority values, the goal is to find the optimal route for underwater mission that maximizes the sum of the priorities and minimizes the total risk percentage while meeting the given constraints. Making use of the heuristic nature of genetic and swarm intelligence algorithms in solving NP-hard graph problems, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to find the optimum solution, where each individual in the population is a candidate solution (route). To evaluate the robustness of the proposed methods, the performance of the all PS and GA algorithms are examined and compared for a number of Monte Carlo runs. Simulation results suggest that the routes generated by both algorithms are feasible and reliable enough, and applicable for underwater motion planning. However, the GA-based route planner produces superior results comparing to the results obtained from the PSO based route planner.

If you have been searching for AI books to help you with as good start then you have come to the right place these book covers the basics to high end stuff. Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning. An introduction to Prolog programming for artificial intelligence covering both basic and advanced AI material.

This series of tutorial is about evolutionary computation: what it is, how it works and how to implement it in your projects and games. At the end of this series you'll be able to harness the power of evolution to find the solution to problems you have no idea how to solve. As a toy example, this tutorial will show how evolutionary computation can be used to teach a simple creature to walk. If you want to try the power of evolutionary computation directly in your browser, try Genetic Algorithm Walkers. As a programmer, you might be familiar with the concept of algorithm.

This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized, method for real-parameter (continuous domain) optimization of non-linear, non-convex functions. We try to motivate and derive the algorithm from intuitive concepts and from requirements of non-linear, non-convex search in continuous domain.

Recent successes in AI have shown that machines can now perform at human levels in many tasks that, just a few years ago, were considered to be decades away, like driving cars, understanding spoken language, and recognizing objects. But these are all tasks where we know what needs to be done, and the machine is just imitating us. What about tasks where the right answers are not known? Can machines be programmed to find solutions on their own, and perhaps even come up with creative solutions that humans would find difficult? The answer is a definite yes!

Some metaphors end up forgotten by all but the most dedicated historians, while others lead long, productive lives. It's only a select few, though, that become so entwined with how we understand the world that we barely even recognize them as metaphors, seeing them instead as something real. Of course, why some fizzle and others flourish can be tricky to account for, but their career in science provides some clues. Metaphors, as we all by now know, aren't just ornamental linguistic flourishes--they're basic building blocks of everyday reasoning. And they're at their most potent when they recast a difficult-to-understand phenomenon as something familiar: The brain becomes a computer; the atom, a tiny solar system; space-time, a fabric. Metaphors that tap into something familiar are the ones that generally gain traction.

This article outlines a method for automatically generating models of dynamic decision-making that both have strong predictive power and are interpretable in human terms. This is useful for designing empirically grounded agent-based simulations and for gaining direct insight into observed dynamic processes. We use an efficient model representation and a genetic algorithm-based estimation process to generate simple approximations that explain most of the structure of complex stochastic processes. This method, implemented in C++ and R, scales well to large data sets. We apply our methods to empirical data from human subjects game experiments and international relations. We also demonstrate the method's ability to recover known data-generating processes by simulating data with agent-based models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity.

Scientists in the US have succeeded in developing the first living cell to be controlled entirely by synthetic DNA. The researchers constructed a bacterium's "genetic software" and transplanted it into a host cell. The resulting microbe then looked and behaved like the species "dictated" by the synthetic DNA. The advance, published in Science, has been hailed as a scientific landmark, but critics say there are dangers posed by synthetic organisms. Some also suggest that the potential benefits of the technology have been over-stated.