Artificial Intelligence (AI) and Personalization are both essential - How do we relate content (the factual knowledge aspects of all games, be they serious or entertainment contained, game mechanics) and context (experiences based. In this research the role of AI and Personalization is and activities) to pedagogical goals towards supporting however focused upon the context of Serious Games (SG) in pedagogically-driven design and development of SGs? particular. A concerted research direction is necessary in this From these two high-level questions we derived a more area so as to establish future benchmarks and metrics for the pragmatic approach to AI and Personalization based on: In effective use of AI and Personalization in serious games design what ways can personalization improve learning and adapt and will benefit relevant research communities in providing best to learner requirements?

In this paper, methods and models for the design of educational interventions and usable systems are presented and synthesized. The purpose is to suplliment the design process with educational considerations and discern design principles for the development of serious STEM games. This synthesis can contribute to the design of the next generation of technologically enhanced learning environments.

Manual image annotation is a tedious and time-consuming task, while automated methods are error prone and limited in their results. Human computation, and especially games with a purpose, have shown potential to create high quality annotations by "hiding the complexity" of the actual annotation task and employing the "wisdom of the crowds". In this demo paper we present two games with a single purpose: finding regions in images that correspond to given terms. We discuss approach, implementation, and preliminary results of our work and give an outlook to immediate future work.

This paper describes a prototype of a two-player collaborative 2D puzzle game, designed to elicit task-oriented situated dialog. In this game players use a text-based chat to coordinate their actions in pushing a ball through a maze of obstacles. The game will be used to collect corpora of human-human interactions in this environment. The data will be used to study how language with actions are interleaved and influence each other in situated dialog. The ultimate goal is to build a computational model of these behaviors.

This paper presents a tool geared towards the collaboration of a human and an artificial designer for the creation of game content. The framework combines procedural content generation using stochastic search with user input in the form of an initial goal statement as well as preference of generated results. Feedback from industry experts in a pilot user experiment showcased the limitations of this approach and the protocol chosen for evaluating the authoring tool. The limitations are discussed with respect to the suitability of interactive evolution for creative design and the design of experimental protocols for evaluating authoring tools for games.

Location-based games are an emerging paradigm for training, simulation, entertainment, health and many other domains. In this paper, we consider the role of location-based games as a platform for data collection and analysis of human behavior. We also examine how human teams perform in a disaster scenario when such a scenario is mapped to a game environment conducted as a location-based augmented reality game. We use a pilot experiment to study human behavior between simulated disaster rescue teams and an integrated commander for the purpose of future research into improving exploitation of local tasks versus exploration of assigned objectives by disaster response teams. We show the results of our pilot experiment, analyze the effectiveness of this game as a data collection platform and then investigate how additional experiments may be conducted to formalize this problem further.

Many crowdsourcing systems have a contribution model that is shallow but massively parallel, with contributors rarely processing or iterating upon the work of others. Few systems, even those crowdsourcing creativity or artistic talent, are designed to allow deep chains where the ideas of one individual feed into and directly inspire another individual. To explore the ways in which creative ideas arise and evolve under the influence of specific artifacts created by others, we examine patterns from over 50,000 sketches created and uploaded with Sketch-a-bit, a collaborative mobile drawing application in which each sketch is directly prompted by a previous sketch. In this paper, we report results from two analyses of content created in the system's first two years of deployment. First, we apply qualitative coding to survey the range of effort and creativity in user actions (including actions ranging from unintentioned scribbles to subtly inspired reimaginations of source material through the unexpected preparation of blank canvases for others). Second, we perform an exploratory analysis of large-scale behaviors manifest in chains or trees of sketches (such as open-ended conversations and structured gameplay). The intent of this work is to describe an iterative model of collaborative creativity and to demonstrate a range of remixing behaviors that can be expected to arise in unrestricted, anonymous collaborative creativity applications.

Xa-lan is a computer program written in Common-LISP to generate music scores with a high level of notational/symbolic expressivity. Generation is driven by audio-analysis of melodic profiles. Once a melodic contour is input to the software, graphic transformations of the original profile stochastically control the different notational elements of the score. The Xa-lan routines display their final output using the ‘Expressive Notation Package’ of PWGL, a LISP-based visual composition environment. A full range of traditional and non-conventional music notation elements can be algorithmically generated with Xa-lan, retrieving to the user a ‘ready-to-play’ or fully ex-pressive music score.

This demonstration presents MaestroGenesis, a program that helps users create complete polyphonic musical pieces from as little as a simple, human composed monophonic melody. MaestroGenesis creates music by exploiting two key ideas behind the functional scaffolding for musical composition (FSMC) approach: (1) that music a function of time and (2) that functional transformations of initial human starting melodies, or scaffolds, inherit some of the essential human qualities contained in the scaffold. Music in FSMC is represented as a functional relationship between the scaffold and a generated accompaniment. The GUI helps users evolve these functions by importing and developing their music through a breeding process akin to animal breeding, called interactive evolutionary computation. Some resulting pieces are indistinguishable from completely human-composed pieces.

In recent years various games have been developed to generate useful data for scientific and commercial purposes. Current human computation games are tailored around a task they aim to solve, adding game mechanics to conceal monotonous workflows. These gamification approaches, although providing valuable gaming experience, do not cover the wide range of experiences seen in digital games today. This work presents a new use for design concepts for human computation games and an evaluation of player experiences.