Checking the Difficulty of EvolutionaryGenerated Maps in a NBody Inspired Mobile Game This paper presents the design and development of an Android application (using Unreal Engine 4) called GravityVolve. It is a twodimensions game based on the NBody problem previously presented by some of the authors. In order to complete a map, the player will have to push the particle from its initial position until it reaches the circumferencexe2x80x99s position. Thus, the maps of GravityVolve are made up of a particle, a circumference and a set of planets. These maps are procedurally generated by an evolutionary algorithm, and are assigned a difficulty level (xe2x80x98Easyxe2x80x99, xe2x80x98Mediumxe2x80x99, xe2x80x98Hardxe2x80x99). When a player completes a map, heshe will have access to a selection system where heshe will have to choose the level of difficulty heshe considers appropriate. So, the objectives of this study are two: first, to gather a considerable amount of votes from players with respect to their perception about the difficulty of every map; and two, to compare both, the userxe2x80x99s difficulty feeling and the difficulty given by the algorithm in order to check their correlation and reach some conclusions regarding the quality of the proposed method.

Evolving Structures in Complex Systems In this paper we propose an approach for measuring growth of complexity of emerging patterns in complex systems such as cellular automata. We discuss several ways how a metric for measuring the complexity growth can be defined. This includes approaches based on compression algorithms and artificial neural networks. We believe such a metric can be useful for designing systems that could exhibit openended evolution, which itself might be a prerequisite for development of general artificial intelligence. We conduct experiments on 1D and 2D grid worlds and demonstrate that using the proposed metric we can automatically construct computational models with emerging properties similar to those found in the Conwayu0027s Game of Life, as well as many other emergent phenomena. Interestingly, some of the patterns we observe resemble forms of artificial life. Our metric of structural complexity growth can be applied to a wide range of complex systems, as it is not limited to cellular automata.