431 research outputs found
Towards a Standard-based Domain-specific Platform to Solve Machine Learning-based Problems
Machine learning is one of the most important subfields of computer science and can be used to solve a variety of interesting artificial intelligence problems. There are different languages, framework and tools to define the data needed to solve machine learning-based problems. However, there is a great number of very diverse alternatives which makes it difficult the intercommunication, portability and re-usability of the definitions, designs or algorithms that any developer may create. In this paper, we take the first step towards a language and a development environment independent of the underlying technologies, allowing developers to design solutions to solve machine learning-based problems in a simple and fast way, automatically generating code for other technologies. That can be considered a transparent bridge among current technologies. We rely on Model-Driven Engineering approach, focusing on the creation of models to abstract the definition of artifacts from the underlying technologies
Game AI revisited
More than a decade after the early research efforts on the
use of artificial intelligence (AI) in computer games and the
establishment of a new AI domain the term “game AI” needs
to be redefined. Traditionally, the tasks associated with
game AI revolved around non player character (NPC) behavior at different levels of control, varying from navigation
and pathfinding to decision making. Commercial-standard
games developed over the last 15 years and current game
productions, however, suggest that the traditional challenges
of game AI have been well addressed via the use of sophisticated AI approaches, not necessarily following or inspired
by advances in academic practices. The marginal penetration of traditional academic game AI methods in industrial
productions has been mainly due to the lack of constructive communication between academia and industry in the
early days of academic game AI, and the inability of academic game AI to propose methods that would significantly
advance existing development processes or provide scalable
solutions to real world problems. Recently, however, there
has been a shift of research focus as the current plethora
of AI uses in games is breaking the non-player character AI
tradition. A number of those alternative AI uses have already shown a significant potential for the design of better
games.
This paper presents four key game AI research areas that
are currently reshaping the research roadmap in the game
AI field and evidently put the game AI term under a new
perspective. These game AI flagship research areas include
the computational modeling of player experience, the procedural generation of content, the mining of player data on
massive-scale and the alternative AI research foci for enhancing NPC capabilities.peer-reviewe
A panorama of artificial and computational intelligence in games
This paper attempts to give a high-level overview
of the field of artificial and computational intelligence (AI/CI)
in games, with particular reference to how the different core
research areas within this field inform and interact with each
other, both actually and potentially. We identify ten main
research areas within this field: NPC behavior learning, search
and planning, player modeling, games as AI benchmarks,
procedural content generation, computational narrative, believable
agents, AI-assisted game design, general game artificial
intelligence and AI in commercial games. We view and analyze
the areas from three key perspectives: (1) the dominant AI
method(s) used under each area; (2) the relation of each area
with respect to the end (human) user; and (3) the placement of
each area within a human-computer (player-game) interaction
perspective. In addition, for each of these areas we consider how
it could inform or interact with each of the other areas; in those
cases where we find that meaningful interaction either exists or
is possible, we describe the character of that interaction and
provide references to published studies, if any. We believe that
this paper improves understanding of the current nature of the
game AI/CI research field and the interdependences between
its core areas by providing a unifying overview. We also believe
that the discussion of potential interactions between research
areas provides a pointer to many interesting future research
projects and unexplored subfields.peer-reviewe
A holistic approach for semantic-based game generation
The Web contains vast sources of content that could
be reused to reduce the development time and effort to create
games. However, most Web content is unstructured and lacks
meaning for machines to be able to process and infer new
knowledge. The Web of Data is a term used to describe a trend
for publishing and interlinking previously disconnected datasets
on the Web in order to make them more valuable and useful as
a whole. In this paper, we describe an innovative approach that
exploits Semantic Web technologies to automatically generate
games by reusing Web content. Existing work on automatic game
content generation through algorithmic means focuses primarily
on a set of parameters within constrained game design spaces
such as terrains or game levels, but does not harness the potential
of already existing content on the Web for game generation. We
instead propose a holistic and more generally-applicable game
generation solution that would identify suitable Web information
sources and enrich game content with semantic meta-structures.The research work disclosed in this publication is partially
funded by the REACH HIGH Scholars Programme — Post-
Doctoral Grants. The grant is part-financed by the European
Union, Operational Programme II — Cohesion Policy 2014-
2020 Investing in human capital to create more opportunities
and promote the wellbeing of society — European Social
Fund.peer-reviewe
StarCraft Bots and Competitions
International audienceDefinition Real-Time Strategy (RTS) games is a sub-genre of strategy games where players need to build an economy (gathering resources and building a base) and military power (training units and researching technologies) in order to defeat their opponents (destroying their army and base). Artificial Intelligence (AI) problems related to RTS games deal with the behavior of an artificial player. Since 2010, many international competitions have been organized to match AIs, or bots, playing to the RTS game StarCraft. This chapter presents a review of all major international competitions from 2010 until 2015, and details some competing StarCraft bots. State of the Art Bots for StarCraft Thanks to the recent organization of international game AI competitions fo-cused around the popular StarCraft game, several groups have been working on integrating many of the techniques developed for RTS game AI into complete "bots", capable of playing complete StarCraft games. In this chapter we will overview some of the currently available top bots, and their results of recent competitions
Computer Simulation of Musical Evolution: A Lesson from Whales
Simulating musical creativity using computers needs more than the ability to devise elegant computational implementations of sophisticated algorithms. It requires, firstly, an understanding of what phenomena might be regarded as music; and, secondly, an understanding of the nature of such phenomena — including their evolutionary history, their recursive-hierarchic structure, and the mechanisms by which they are transmitted within cultural groups. To understand these issues it is fruitful to compare human music, and indeed human language, with analogous phenomena in other areas of the animal kingdom. Whale song, specifically that of the humpback (Megaptera novaeangeliae), possesses many structural and functional similarities to human music (as do certain types of birdsong). Using a memetic perspective, this paper compares the “musilanguage” of humpbacks with the music of humans, and aims to identify a number of shared characteristics. A consequence of nature and nurture, these commonalities appear to arise partly from certain constraints of perception and cognition (and thus they determine an aspect of the environment within which the “musemes” (musical memes) constituting whale vocalizations and human music is replicated), and partly from the social-emotive-embodied and sexual-selective nature of musemic transmission. The paper argues that Universal-Darwinian forces give rise to uniformities of structure in phenomena we might regard as “music”, irrespective of the animal group — certain primates, cetaceans or birds - within which it occurs. It considers the extent to which whale song might be regarded as creative, by invoking certain criteria used to assess this attribute in human music. On the basis of these various comparisons, the paper concludes by attempting to draw conclusions applicable to those engaged in designing evolutionary music simulation/generation algorithms
- …