In the past few years, game randomizers have become increasingly popular. In general, a game randomizer takes some aspect of a game that is usually static and shuffles it somehow. In particular, in this paper we will discuss the type of randomizer that shuffles the locations of items in a game where certain key items are needed to traverse the game world and access some of these locations. Examples of these types of games include series such as The Legend of Zelda and Metroid. In order to accomplish this shuffling in such a way that the player is able to reach the end of the game, some novel algorithms in graph theory must be utilized, where the game world and its item locations are represented as a graph and each edge on the graph has some rule for which items are required to traverse it. In this paper, we define these algorithms formally and evaluate them with different metrics that can guide a developer's decision about which algorithm works best for their game.INDEX TERMS Game analysis, game randomization, graph theory.
iChain is an application which was created to help meet the growing demand of machine learning. It allows users to pay those with powerful machines to run machine learning tasks for them, bypassing the need for a significant investment in a powerful computer to run it themselves. This is similar to services like a render farm. Our application functions using the Ethereum blockchain which ensures security and decentralization, as well as providing a platform for payment transactions. This article will discuss the background on machine learning and blockchain, the application, how it works, how the data moves through it, and how to use it. We hope our application will enable many without the funds to build or buy a powerful computer to experiment with and utilize complex machine learning tasks.
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