10,722 research outputs found
Generative Design in Minecraft (GDMC), Settlement Generation Competition
This paper introduces the settlement generation competition for Minecraft,
the first part of the Generative Design in Minecraft challenge. The settlement
generation competition is about creating Artificial Intelligence (AI) agents
that can produce functional, aesthetically appealing and believable settlements
adapted to a given Minecraft map - ideally at a level that can compete with
human created designs. The aim of the competition is to advance procedural
content generation for games, especially in overcoming the challenges of
adaptive and holistic PCG. The paper introduces the technical details of the
challenge, but mostly focuses on what challenges this competition provides and
why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018
proceedings, as part of the workshop on Procedural Content Generatio
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Happy software developers solve problems better: psychological measurements in empirical software engineering
For more than 30 years, it has been claimed that a way to improve software
developers' productivity and software quality is to focus on people and to
provide incentives to make developers satisfied and happy. This claim has
rarely been verified in software engineering research, which faces an
additional challenge in comparison to more traditional engineering fields:
software development is an intellectual activity and is dominated by
often-neglected human aspects. Among the skills required for software
development, developers must possess high analytical problem-solving skills and
creativity for the software construction process. According to psychology
research, affects-emotions and moods-deeply influence the cognitive processing
abilities and performance of workers, including creativity and analytical
problem solving. Nonetheless, little research has investigated the correlation
between the affective states, creativity, and analytical problem-solving
performance of programmers. This article echoes the call to employ
psychological measurements in software engineering research. We report a study
with 42 participants to investigate the relationship between the affective
states, creativity, and analytical problem-solving skills of software
developers. The results offer support for the claim that happy developers are
indeed better problem solvers in terms of their analytical abilities. The
following contributions are made by this study: (1) providing a better
understanding of the impact of affective states on the creativity and
analytical problem-solving capacities of developers, (2) introducing and
validating psychological measurements, theories, and concepts of affective
states, creativity, and analytical-problem-solving skills in empirical software
engineering, and (3) raising the need for studying the human factors of
software engineering by employing a multidisciplinary viewpoint.Comment: 33 pages, 11 figures, published at Peer
Mixed-initiative co-creativity
Creating and designing with a machine: do we merely create together (co-create) or can a machine truly foster our
creativity as human creators? When does such co-creation
foster the co-creativity of both humans and machines? This
paper investigates the simultaneous and/or iterative process
of human and computational creators in a mixed-initiative
fashion within the context of game design and attempts to
draw from both theory and praxis towards answering the
above questions. For this purpose, we first discuss the strong
links between mixed-initiative co-creation and theories of
human and computational creativity. We then introduce
an assessment methodology of mixed-initiative co-creativity
and, as a proof of concept, evaluate Sentient Sketchbook as a
co-creation tool for game design. Core findings suggest that
tools such as Sentient Sketchbook are not mere game authoring systems or mere enablers of creation but, instead, foster
human creativity and realize mixed-initiative co-creativity.peer-reviewe
Interweaving story coherence and player creativity through story-making games
In story-making games, players create stories together by
using narrative tokens. Often there is a tension between players playing to
win using the rules of a story-making game, and collaboratively creating a
good story. In this paper, we introduce a competitive story-making game
prototype coupled with computational methods intended to be used for
both supporting playersâ creativity and narrative coherence.peer-reviewe
Evaluating content generators
Evaluating your content generator is a very important task, but difficult to
do well. Creating a game content generator in general is much easier than creating
a good game content generatorâbut what is a âgoodâ content generator? That depends
very much on what you are trying to create and why. This chapter discusses
the importance and the challenges of evaluating content generators, and more generally
understanding a generatorâs strengths and weaknesses and suitability for your
goals. In particular, we discuss two different approaches to evaluating content generators:
visualizing the expressive range of generators, and using questionnaires to
understand the impact of your generator on the player. These methods could broadly
be called top-down and bottom-up methods for evaluating generators.peer-reviewe
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