Collaborating Authors

Agent-Based Adaptive Level Generation for Dynamic Difficulty Adjustment in Angry Birds Artificial Intelligence

Section is a key area of investigation for video game research 2 describes the large amount of background and related (Hendrikx et al. 2013; Togelius et al. 2011). PLG work, both for Angry Birds and adaptive level generation in can be extremely useful for increasing a game's length and general. Section 3 presents our proposed adaptive generation replayability, as it allows a large number of levels to be created method. Section 4 describes our conducted experiments and in a relatively short time. It is also possible to tailor the results. Sections 5 discusses what these results could mean generated levels towards specific user's playstyles, known as for both human players and agents, Section 6 concludes this adaptive level generation, which allows for a unique and personalised work and outlines future possibilities.

Intentional Computational Level Design Artificial Intelligence

The procedural generation of levels and content in video games is a challenging AI problem. Often such generation relies on an intelligent way of evaluating the content being generated so that constraints are satisfied and/or objectives maximized. In this work, we address the problem of creating levels that are not only playable but also revolve around specific mechanics in the game. We use constrained evolutionary algorithms and quality-diversity algorithms to generate small sections of Super Mario Bros levels called scenes, using three different simulation approaches: Limited Agents, Punishing Model, and Mechanics Dimensions. All three approaches are able to create scenes that give opportunity for a player to encounter or use targeted mechanics with different properties. We conclude by discussing the advantages and disadvantages of each approach and compare them to each other.

How Did Humans Become So Creative? A Computational Approach Artificial Intelligence

This paper summarizes efforts to computationally model two transitions in the evolution of human creativity: its origins about two million years ago, and the 'big bang' of creativity about 50,000 years ago. Using a computational model of cultural evolution in which neural network based agents evolve ideas for actions through invention and imitation, we tested the hypothesis that human creativity began with onset of the capacity for recursive recall. We compared runs in which agents were limited to single-step actions to runs in which they used recursive recall to chain simple actions into complex ones. Chaining resulted in higher diversity, open-ended novelty, no ceiling on the mean fitness of actions, and greater ability to make use of learning. Using a computational model of portrait painting, we tested the hypothesis that the explosion of creativity in the Middle/Upper Paleolithic was due to onset of con-textual focus: the capacity to shift between associative and analytic thought. This resulted in faster convergence on portraits that resembled the sitter, employed painterly techniques, and were rated as preferable. We conclude that recursive recall and contextual focus provide a computationally plausible explanation of how humans evolved the means to transform this planet.

How AI in Fitness is revolutionizing the Fitness industry?


The advent of artificial intelligence and its subsets (computer vision, machine learning, NLP, and more) is modernizing the health and fitness industry at an unprecedented rate. By making fitness machines, gadgets, wearables, and mobile applications smarter, this technology is helping people to stay fit and healthy. Right from helping businesses in this industry in improving their marketing and sales strategies to assisting people to reshape their day-to-day habits, AI is playing a big role in the fitness world. And if you are wondering how AI has become a game-changer, then this article is for you. Here, we have listed all the benefits it renders to the fitness world.

Apple Watch could soon track your sleep and fitness levels


The Apple Watch is billed as a fitness-focused device, but it doesn't really make sense of fitness data -- you're supposed to interpret the numbers yourself. However, Apple might soon give its wristwear some added smarts. Bloomberg sources claim that the Apple Watch will get apps that track sleeping patterns and fitness levels. It's not certain how the sleep tracking would work (most likely through motion), but the watch would gauge your fitness by recording the time it takes for your heart rate to drop from its peak to its resting level. It's not certain when you'd get the apps.