7 research outputs found
Pitako -- Recommending Game Design Elements in Cicero
Recommender Systems are widely and successfully applied in e-commerce. Could
they be used for design? In this paper, we introduce Pitako1, a tool that
applies the Recommender System concept to assist humans in creative tasks. More
specifically, Pitako provides suggestions by taking games designed by humans as
inputs, and recommends mechanics and dynamics as outputs. Pitako is implemented
as a new system within the mixed-initiative AI-based Game Design Assistant,
Cicero. This paper discusses the motivation behind the implementation of Pitako
as well as its technical details and presents usage examples. We believe that
Pitako can influence the use of recommender systems to help humans in their
daily tasks.Comment: Paper accepted in the IEEE Conference on Games 2019 (COG 2019
Learning the Designer's Preferences to Drive Evolution
This paper presents the Designer Preference Model, a data-driven solution
that pursues to learn from user generated data in a Quality-Diversity
Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the
user's design style to better assess the tool's procedurally generated content
with respect to that user's preferences. Through this approach, we aim for
increasing the user's agency over the generated content in a way that neither
stalls the user-tool reciprocal stimuli loop nor fatigues the user with
periodical suggestion handpicking. We describe the details of this novel
solution, as well as its implementation in the MI-CC tool the Evolutionary
Dungeon Designer. We present and discuss our findings out of the initial tests
carried out, spotting the open challenges for this combined line of research
that integrates MI-CC with Procedural Content Generation through Machine
Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European
Conference on the Applications of Evolutionary and bio-inspired Computation,
EvoApplications 202
Evaluation of a Recommender System for Assisting Novice Game Designers
Game development is a complex task involving multiple disciplines and
technologies. Developers and researchers alike have suggested that AI-driven
game design assistants may improve developer workflow. We present a recommender
system for assisting humans in game design as well as a rigorous human subjects
study to validate it. The AI-driven game design assistance system suggests game
mechanics to designers based on characteristics of the game being developed. We
believe this method can bring creative insights and increase users'
productivity. We conducted quantitative studies that showed the recommender
system increases users' levels of accuracy and computational affect, and
decreases their levels of workload.Comment: The 15th AAAI Conference on Artificial Intelligence and Interactive
Digital Entertainment (AIIDE 19
Boosting Mixed-Initiative Co-Creativity in Game Design: A Tutorial
In recent years, there has been a growing application of mixed-initiative
co-creative approaches in the creation of video games. The rapid advances in
the capabilities of artificial intelligence (AI) systems further propel
creative collaboration between humans and computational agents. In this
tutorial, we present guidelines for researchers and practitioners to develop
game design tools with a high degree of mixed-initiative co-creativity
(MI-CCy). We begin by reviewing a selection of current works that will serve as
case studies and categorize them by the type of game content they address. We
introduce the MI-CCy Quantifier, a framework that can be used by researchers
and developers to assess co-creative tools on their level of MI-CCy through a
visual scheme of quantifiable criteria scales. We demonstrate the usage of the
MI-CCy Quantifier by applying it to the selected works. This analysis enabled
us to discern prevalent patterns within these tools, as well as features that
contribute to a higher level of MI-CCy. We highlight current gaps in MI-CCy
approaches within game design, which we propose as pivotal aspects to tackle in
the development of forthcoming approaches.Comment: 34 pages, 11 figure
O impacto da inteligência artificial no negócio eletrónico
Pela importância que a Inteligência Artificial exibe na atualidade, revela-se de grande interesse verificar até que ponto ela está a transformar o Negócio Eletrónico. Para esse efeito, delineou-se uma revisão sistemática com o objetivo de avaliar os impactos da proliferação destes instrumentos.
A investigação empreendida pretendeu identificar artigos científicos que, através de pesquisas realizadas a Fontes de Dados Eletrónicas, pudessem responder às questões de investigação implementadas: a) que tipo de soluções, baseadas na Inteligência Artificial (IA), têm sido usadas para melhorar o Negócio Eletrónico (NE); b) em que domínios do NE a IA foi aplicada; c) qual a taxa de sucesso ou fracasso do projeto. Simultaneamente, tiveram de respeitar critérios de seleção, nomeadamente, estar escritos em inglês, encontrarem-se no intervalo temporal 2015/2021 e tratar-se de estudos empíricos, suportados em dados reais.
Após uma avaliação de qualidade final, procedeu-se à extração dos dados pertinentes para a investigação, para formulários criados em MS Excel. Estes dados estiveram na base da análise quantitativa e qualitativa que evidenciaram as descobertas feitas e sobre os quais se procedeu, posteriormente, à sua discussão. A dissertação termina com as conclusão e discussão de trabalhos futuros.Due to the importance that Artificial Intelligence exhibits today, it is of great interest to see to what extent it is transforming the Electronic Business. To this end, a systematic review was designed to evaluate the impacts of the proliferation of these instruments.
The research aimed to identify scientific articles that, through research carried out on Electronic Data Sources, could answer the research questions implemented: a) what kind of solutions, based on Artificial Intelligence, have been used to improve the Electronic Business; b) in which areas of the Electronic Business Artificial Intelligence has been applied; c) what the success rate or failure of the project is. At the same time, they must comply with selection criteria, to be written in English, to be found in the 2015/2021-time interval and to be empirical studies supported by actual data.
After a final quality evaluation, the relevant data for the investigation were extracted for forms created in MS Excel. These data were the basis of the quantitative and qualitative analysis that evidenced the findings found and on which they were subsequently discussed. The dissertation ends with the conclusion and discussion of future works