10 research outputs found
General general game AI
Arguably the grand goal of artificial intelligence
research is to produce machines with general intelligence: the
capacity to solve multiple problems, not just one. Artificial
intelligence (AI) has investigated the general intelligence capacity
of machines within the domain of games more than any other
domain given the ideal properties of games for that purpose:
controlled yet interesting and computationally hard problems.
This line of research, however, has so far focused solely on
one specific way of which intelligence can be applied to games:
playing them. In this paper, we build on the general game-playing
paradigm and expand it to cater for all core AI tasks within a
game design process. That includes general player experience
and behavior modeling, general non-player character behavior,
general AI-assisted tools, general level generation and complete
game generation. The new scope for general general game AI
beyond game-playing broadens the applicability and capacity of
AI algorithms and our understanding of intelligence as tested
in a creative domain that interweaves problem solving, art, and
engineering.peer-reviewe
Artificial Beings Worthy of Moral Consideration in Virtual Environments: An Analysis of Ethical Viability
This article explores whether and under which circumstances it is ethically viable to include artificial beings worthy of moral consideration in virtual environments. In particular, the article focuses on virtual environments such as those in digital games and training simulations – interactive and persistent digital artifacts designed to fulfill specific purposes, such as entertainment, education, training, or persuasion.
The article introduces the criteria for moral consideration that serve as a framework for this analysis.
Adopting this framework, the article tackles the question of whether including artificial intelligences that are entitled to moral consideration in virtual environments constitutes an immoral action on the part of human creators. To address this problem, the article draws on three conceptual lenses from the philosophical branch of ethics: the problem of parenthood and procreation, the question concerning the moral status of animals, and the classical problem of evil.
Using a thought experiment, the concluding section proposes a contractualist answer to the question posed in this article. The same section also emphasizes the potential need to reframe our understanding of the design of virtual environments and their future stakeholders
Towards general models of player affect
While the primary focus of affective computing has
been on constructing efficient and reliable models of affect,
the vast majority of such models are limited to a specific task
and domain. This paper, instead, investigates how computational
models of affect can be general across dissimilar tasks; in
particular, in modeling the experience of playing very different
video games. We use three dissimilar games whose players
annotated their arousal levels on video recordings of their own
playthroughs. We construct models mapping ranks of arousal to
skin conductance and gameplay logs via preference learning and
we use a form of cross-game validation to test the generality of the
obtained models on unseen games. Our initial results comparing
between absolute and relative measures of the arousal annotation
values indicate that we can obtain more general models of player
affect if we process the model output in an ordinal fashion.peer-reviewe
Towards general models of player experience : a study within genres
This project has received funding from the EU’s Horizon 2020 programme
under grant agreement No 951911, and from the University of Malta internal
research grants programme Research Excellence Fund under grant agreement
No 202003.To which degree can abstract gameplay metrics
capture the player experience in a general fashion within a game
genre? In this comprehensive study we address this question
across three different videogame genres: racing, shooter, and
platformer games. Using high-level gameplay features that feed
preference learning models we are able to predict arousal
accurately across different games of the same genre in a largescale dataset of over 1, 000 arousal-annotated play sessions. Our
genre models predict changes in arousal with up to 74% accuracy
on average across all genres and 86% in the best cases. We also
examine the feature importance during the modelling process
and find that time-related features largely contribute to the
performance of both game and genre models. The prominence of
these game-agnostic features show the importance of the temporal
dynamics of the play experience in modelling, but also highlight
some of the challenges for the future of general affect modelling
in games and beyond.peer-reviewe
Generation and Analysis of Content for Physics-Based Video Games
The development of artificial intelligence (AI) techniques that can assist with the creation and analysis of digital content is a broad and challenging task for researchers. This topic has been most prevalent in the field of game AI research, where games are used as a testbed for solving more complex real-world problems. One of the major issues with prior AI-assisted content creation methods for games has been a lack of direct comparability to real-world environments, particularly those with realistic physical properties to consider. Creating content for such environments typically requires physics-based reasoning, which imposes many additional complications and restrictions that must be considered. Addressing and developing methods that can deal with these physical constraints, even if they are only within simulated game environments, is an important and challenging task for AI techniques that intend to be used in real-world situations.
The research presented in this thesis describes several approaches to creating and analysing levels for the physics-based puzzle game Angry Birds, which features a realistic 2D environment. This research was multidisciplinary in nature and covers a wide variety of different AI fields, leading to this thesis being presented as a compilation of published work. The central part of this thesis consists of procedurally generating levels for physics-based games similar to those in Angry Birds. This predominantly involves creating and placing stable structures made up of many smaller blocks, as well as other level elements. Multiple approaches are presented, including both fully autonomous and human-AI collaborative methodologies. In addition, several analyses of Angry Birds levels were carried out using current state-of-the-art agents. A hyper-agent was developed that uses machine learning to estimate the performance of each agent in a portfolio for an unknown level, allowing it to select the one most likely to succeed. Agent performance on levels that contain deceptive or creative properties was also investigated, allowing determination of the current strengths and weaknesses of different AI techniques. The observed variability in performance across levels for different AI techniques led to the development of an adaptive level generation system, allowing for the dynamic creation of increasingly challenging levels over time based on agent performance analysis. An additional study also investigated the theoretical complexity of Angry Birds levels from a computational perspective.
While this research is predominately applied to video games with physics-based simulated environments, the challenges and problems solved by the proposed methods also have significant real-world potential and applications
Примена виртуелних светова у истраживању теорије агената и инжењерском образовању
The focus of this doctoral dissertation is on exploring the potentials of virtual worlds, for
applications in research and education. Regarding this, there are two central aspects that are
explored in the dissertation. The first one considers the concept of autonomous agents, and agent
theory in general, in the context of virtual worlds. The second aspect is related to the educational
applications of virtual worlds, while especially focusing on the concept of virtual laboratories. An
introduction to basic terminology related to the subject is given at the start of the dissertation. After
that, a thorough analysis of the role of agents in virtual worlds is presented. This, among others,
includes the analysis of the techniques that shape the agent’s behavior. The development of the
virtual gamified educational system, specially dedicated to agents is then presented in the
dissertation, along with a thorough description. While, in the end, analysis of the concept of virtual
laboratories in STE (Science, Technology, and Engineering) disciplines is performed, and existing
solutions are evaluated according to the criteria defined in the dissertation.Фокус ове докторске дисертације је на истраживању потенцијала виртуелних светова за
примене у истраживањима и образовању. У вези са тим, постоје два главна аспекта која су
обрађена у дисертацији. Први аспект се тиче концепта аутономних агената, као и теорије
агената у целини, а у контексту виртуелних светова. Други аспект је везан за примену
виртуелних светова у образовању, при чему је посебан акценат стављен на виртуелне
лабораторије. На почетку дисертације је дат кратак увод који се тиче терминологије и
појединих појмова везаних за област којом се ова дисертција бави. Након тога је
представљена систематична и темељна анализа улоге агената у виртуелним световима.
Између осталог, ово укључује и анализу техника потребних за обликовање понашања
агената. Потом је у дисертацији детаљно представљен развој оригиналног виртуелног
образовног система посвећеног агентима. На крају, анализиран је концепт виртуелних
лабораторија у НТИ (наука, технологија, инжењерство) дисциплинама и извршена је
евалуација постојећих решења у складу са критеријумима који су дефинисани у дисертацији
A generic data representation for predicting player behaviours
A common use of predictive models in game analytics is to predict the behaviours of players so that pre-emptive measures can be taken before they make undesired decisions. A standard data pre-processing step in predictive modelling includes both data representation and category definition.
Data representation extracts features from the raw dataset to represent the whole dataset. Much research has been done towards predicting important player behaviours with game-specific data representations. Some of the resulting efforts have achieved competitive performance; however, due to the game-specific data representations they apply, game companies need to spend extra efforts to reuse the proposed methods in more than one products. This work proposes an event-frequency-based data representation that is generally applicable to games. This method of data representation relies only on counts of in-game events instead of prior knowledge of the game. To verify the generality and performance of this data-representation, it was applied to three different genres of games for predicting player first-purchasing, disengagement and churn behaviours. Experiments show that this data representation method can provide a competitive performance across different games.
Category definition is another essential component of classification problems. As labelling method that relies on some specific conditions to distribute players into classes can often lead to imbalanced classification problems, this work applied two commonly used approaches, i.e., random undersampling and Synthetic Minority Over-Sampling Technique (SMOTE), for rebalancing the imbalanced tasks. Results suggested that undersampling is able to provide better performance in the cases where the quantity of data is sufficient whereas the SMOTE has more chances when the dataset is too small to be balanced with the undersampling approach. Besides, this work also proposes a new category-definition method which can maintain a distribution of the resultant classes that is closer to balanced. In addition, the parameters used in this method can also be used to gain insight into the health of the game. Preliminary experimental results show that this method of category definition is able to improve the balance of the class distribution when it is applied to different games and provide significantly better performance than random classifiers
Intrinsic Motivation in Computational Creativity Applied to Videogames
PhD thesisComputational creativity (CC) seeks to endow artificial systems with creativity.
Although human creativity is known to be substantially driven by
intrinsic motivation (IM), most CC systems are extrinsically motivated. This
restricts their actual and perceived creativity and autonomy, and consequently
their benefit to people. In this thesis, we demonstrate, via theoretical arguments
and through applications in videogame AI, that computational intrinsic
reward and models of IM can advance core CC goals.
We introduce a definition of IM to contextualise related work. Via two
systematic reviews, we develop typologies of the benefits and applications of
intrinsic reward and IM models in CC and game AI. Our reviews highlight
that related work is limited to few reward types and motivations, and we thus
investigate the usage of empowerment, a little studied, information-theoretic
intrinsic reward, in two novel models applied to game AI.
We define coupled empowerment maximisation (CEM), a social IM model,
to enable general co-creative agents that support or challenge their partner
through emergent behaviours. Via two qualitative, observational vignette
studies on a custom-made videogame, we explore CEM’s ability to drive
general and believable companion and adversary non-player characters which
respond creatively to changes in their abilities and the game world.
We moreover propose to leverage intrinsic reward to estimate people’s
experience of interactive artefacts in an autonomous fashion. We instantiate
this proposal in empowerment-based player experience prediction (EBPXP)
and apply it to videogame procedural content generation. By analysing think-aloud
data from an experiential vignette study on a dedicated game, we
identify several experiences that EBPXP could predict.
Our typologies serve as inspiration and reference for CC and game AI
researchers to harness the benefits of IM in their work. Our new models can
increase the generality, autonomy and creativity of next-generation videogame
AI, and of CC systems in other domains