A Progressive Approach to Content Generation

Abstract. PCG approaches are commonly categorised as constructive, generate-and-test or search-based. Each of these approaches has its distinctive advantages and drawbacks. In this paper, we propose an approach to Content Generation (CG) – in particular level generation – that combines the advantages of construc-tive and search-based approaches thus providing a fast, flexible and reliable way of generating diverse content of high quality. In our framework, CG is seen from a new perspective which differentiates between two main aspects of the game-play experience, namely the order of the in-game interactions and the associated level design. The framework first generates timelines following the search-based paradigm. Timelines are game-independent and they reflect the rhythmic feel of the levels. A progressive, constructive-based approach is then implemented to evaluate timelines by mapping them into level designs. The framework is applied for the generation of puzzles for the Cut the Rope game and the results in terms of performance, expressivity and controllability are characterised and discussed.

A Projection-Based Approach for Real-Time Assessment and Playability Check for Physics-Based Games

Abstract. This paper introduces an authoring tool for physics-based puzzle games that supports game designers through providing visual feedback about the space of interactions. The underlying algorithm accounts for the type and physical prop-erties of the different game components. An area of influence, which identifies the possible space of interaction, is identified for each component. The influence areas of all components in a given design are then merged considering the com-ponents ’ type and the context information. The tool can be used offline where complete designs are analyzed and the final interactive space is projected, and online where edits in the interactive space are projected on the canvas in realtime permitting continuous assistance for game designers and providing informative feedback about playability.

AI Researchers, Video Games Are Your Friends!

If you are an artificial intelligence researcher, you should look to video games as ideal testbeds for the work you do. If you are a video game developer, you should look to AI for the technology that makes completely new types of games possible. This chapter lays out the case for both of these propositions. It asks the question "what can video games do for AI", and discusses how in particular general video game playing is the ideal testbed for artificial general intelligence research. It then asks the question "what can AI do for video games", and lays out a vision for what video games might look like if we had significantly more advanced AI at our disposal. The chapter is based on my keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad audience.Comment: in Studies in Computational Intelligence Studies in Computational Intelligence, Volume 669 2017. Springe

A spatially-structured PCG method for content diversity in a Physics-based simulation game

This paper presents a spatially-structured evolutionary algorithm (EA) to procedurally generate game maps of di ferent levels of di ficulty to be solved, in Gravityvolve!, a physics-based simulation videogame that we have implemented and which is inspired by the n- body problem, a classical problem in the fi eld of physics and mathematics. The proposal consists of a steady-state EA whose population is partitioned into three groups according to the di ficulty of the generated content (hard, medium or easy) which can be easily adapted to handle the automatic creation of content of diverse nature in other games. In addition, we present three fitness functions, based on multiple criteria (i.e:, intersections, gravitational acceleration and simulations), that were used experimentally to conduct the search process for creating a database of maps with di ferent di ficulty in Gravityvolve!.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

General general game AI

Arguably the grand goal of artificial intelligence research is to produce machines with general intelligence: the capacity to solve multiple problems, not just one. Artificial intelligence (AI) has investigated the general intelligence capacity of machines within the domain of games more than any other domain given the ideal properties of games for that purpose: controlled yet interesting and computationally hard problems. This line of research, however, has so far focused solely on one specific way of which intelligence can be applied to games: playing them. In this paper, we build on the general game-playing paradigm and expand it to cater for all core AI tasks within a game design process. That includes general player experience and behavior modeling, general non-player character behavior, general AI-assisted tools, general level generation and complete game generation. The new scope for general general game AI beyond game-playing broadens the applicability and capacity of AI algorithms and our understanding of intelligence as tested in a creative domain that interweaves problem solving, art, and engineering.peer-reviewe

Literature review of procedural content generation in puzzle games

This is the third chapter from my Master Thesis (Automatic Game Generation). This chapter will provide a review of the past work on Procedural Content Generation. It highlights different efforts towards generating levels and rules for games. These efforts are grouped according to their similarity and sorted chronologically within each group.N/