Monsters of Darwin: a strategic game based on Artificial Intelligence and Genetic Algorithms

The production of video games is a complex process, which involves several disciplines, spanning from art to computer science. The final goal is to keep entertained the players, by continuously providing them novel and challenging contents. However, the availability of a large variety of pre-produced material is often not possible. A similar problem can be found in many single-player game genres, where the simulated behaviour generated by the Artificial Intelligence algorithms must be coherent, believable, but also adequately variegate to maintain a satisfactory user experience. To this aim, there is a growing interest in the introduction of automatic or semi-automatic techniques to produce and manage the video game contents. In this paper, we present an example of strategic card battle video game based on the applications of Artificial Intelligence and Genetic Algorithms, where the game contents are dynamically adapted and produced during the game sessions

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp