Automatic Mapping of NES Games with Mappy

Game maps are useful for human players, general-game-playing agents, and data-driven procedural content generation. These maps are generally made by hand-assembling manually-created screenshots of game levels. Besides being tedious and error-prone, this approach requires additional effort for each new game and level to be mapped. The results can still be hard for humans or computational systems to make use of, privileging visual appearance over semantic information. We describe a software system, Mappy, that produces a good approximation of a linked map of rooms given a Nintendo Entertainment System game program and a sequence of button inputs exploring its world. In addition to visual maps, Mappy outputs grids of tiles (and how they change over time), positions of non-tile objects, clusters of similar rooms that might in fact be the same room, and a set of links between these rooms. We believe this is a necessary step towards developing larger corpora of high-quality semantically-annotated maps for PCG via machine learning and other applications.Comment: 9 pages, 7 figures. Appearing at Procedural Content Generation Workshop 201

The Personalization Paradox: the Conflict between Accurate User Models and Personalized Adaptive Systems

Personalized adaptation technology has been adopted in a wide range of digital applications such as health, training and education, e-commerce and entertainment. Personalization systems typically build a user model, aiming to characterize the user at hand, and then use this model to personalize the interaction. Personalization and user modeling, however, are often intrinsically at odds with each other (a fact some times referred to as the personalization paradox). In this paper, we take a closer look at this personalization paradox, and identify two ways in which it might manifest: feedback loops and moving targets. To illustrate these issues, we report results in the domain of personalized exergames (videogames for physical exercise), and describe our early steps to address some of the issues arisen by the personalization paradox.Comment: arXiv admin note: substantial text overlap with arXiv:2101.1002

Music as complex emergent behaviour : an approach to interactive music systems

Access to the full-text thesis is no longer available at the author's request, due to 3rd party copyright restrictions. Access removed on 28.11.2016 by CS (TIS).Metadata merged with duplicate record (http://hdl.handle.net/10026.1/770) on 20.12.2016 by CS (TIS).This is a digitised version of a thesis that was deposited in the University Library. If you are the author please contact PEARL Admin ([email protected]) to discuss options.This thesis suggests a new model of human-machine interaction in the domain of non-idiomatic musical improvisation. Musical results are viewed as emergent phenomena issuing from complex internal systems behaviour in relation to input from a single human performer. We investigate the prospect of rewarding interaction whereby a system modifies itself in coherent though non-trivial ways as a result of exposure to a human interactor. In addition, we explore whether such interactions can be sustained over extended time spans. These objectives translate into four criteria for evaluation; maximisation of human influence, blending of human and machine influence in the creation of machine responses, the maintenance of independent machine motivations in order to support machine autonomy and finally, a combination of global emergent behaviour and variable behaviour in the long run. Our implementation is heavily inspired by ideas and engineering approaches from the discipline of Artificial Life. However, we also address a collection of representative existing systems from the field of interactive composing, some of which are implemented using techniques of conventional Artificial Intelligence. All systems serve as a contextual background and comparative framework helping the assessment of the work reported here. This thesis advocates a networked model incorporating functionality for listening, playing and the synthesis of machine motivations. The latter incorporate dynamic relationships instructing the machine to either integrate with a musical context suggested by the human performer or, in contrast, perform as an individual musical character irrespective of context. Techniques of evolutionary computing are used to optimise system components over time. Evolution proceeds based on an implicit fitness measure; the melodic distance between consecutive musical statements made by human and machine in relation to the currently prevailing machine motivation. A substantial number of systematic experiments reveal complex emergent behaviour inside and between the various systems modules. Music scores document how global systems behaviour is rendered into actual musical output. The concluding chapter offers evidence of how the research criteria were accomplished and proposes recommendations for future research

Personalized Game Content Generation and Recommendation for Gamified Systems

Gamification, that is, the usage of game content in non-game contexts, has been successfully employed in several application domains to foster engagement, as well as to influence the behavior of end users. Although gamification is often effective in inducing behavioral changes in citizens, the difficulty in retaining players and sustaining the acquired behavior over time, shows some limitations of this technology. That is especially unfortunate, because changing players’ demeanor (which have been shaped for a long time), cannot be immediately internalized; rather, the gamification incentive must be reinforced to lead to stabilization. This issue could be sourced from utilizing static game content and a one-size-fits-all strategy in generating the content during the game. This reveals the need for dynamic personalization over the course of the game. Our research hypothesis is that we can overcome these limitations with Procedural Content Generation (PCG) of playable units that appeal to each individual player and make her user experience more varied and compelling. In this thesis, we propose a deep, large and long solution, deployed in two main phases of Design and Integration to tackle these limitations. To support the former phase, we present a “PCG and Recommender system” to automate the generation and recommendation of playable units, named “Challenges”, which are Personalized and Contextualized on the basis of players’ preferences, skills, etc., and the game ulterior objectives. To this end, we develop a multi-layered framework to generate the personalized game content to be assigned and recommended to the players involved in the gamified system. To support the latter phase, we integrate two modules into the system including Machine Learning (ML) and Player Modeling, in order to optimize the challenge selection process and learning players’ behavior to further improve the personalization, by deriving the style of the player, respectively. We have carried out the implementation and evaluation of the proposed framework and its integration in two different contexts. First, we assess our Automatic Procedural Content Generation and Recommendation (APCGR) system within a large-scale and long-running open field experiment promoting sustainable urban mobility that lasted twelve weeks and involved more than 400 active players. Then, we implement the “Player Modeling” module (in the integration phase) in an educational interactive game domain to assess the performance of the proposed play style extraction approach. The contributions of this dissertation are a first step toward the application of machine learning in automating the procedural content generation and recommendation in gamification systems