Demystifying the Educational Benefits of Different Gaming Genres

As research continues into the use of computer games for educational purposes, educators still appear reluctant to incorporate them into their teaching. One contributing factor to this reluctance is the lack of information regarding the benefits offered by the different games available today. These differences appear to have been largely overlooked by the academic community, resulting in a lack of information being made available to both the academic and education communities alike. Without this information, educators will find it difficult to determine whether a game will suit their teaching needs, and will continue to avoid using them. This paper studies a selection of games from several different genres, assessing each one in its ability to fulfil a set of previously identified requirements for a good educational resource. The results of the investigation showed that there were indeed strong differences between the genres, allowing for some suggestions to be made regarding their use in education, as well as leaving room for some interesting future work

Modeling crowdsourcing as collective problem solving

Crowdsourcing is a process of accumulating the ideas, thoughts or information from many independent participants, with aim to find the best solution for a given challenge. Modern information technologies allow for massive number of subjects to be involved in a more or less spontaneous way. Still, the full potentials of crowdsourcing are yet to be reached. We introduce a modeling framework through which we study the effectiveness of crowdsourcing in relation to the level of collectivism in facing the problem. Our findings reveal an intricate relationship between the number of participants and the difficulty of the problem, indicating the optimal size of the crowdsourced group. We discuss our results in the context of modern utilization of crowdsourcing.Comment: 19 pages, 3 figure

Language Understanding for Text-based Games Using Deep Reinforcement Learning

In this paper, we consider the task of learning control policies for text-based games. In these games, all interactions in the virtual world are through text and the underlying state is not observed. The resulting language barrier makes such environments challenging for automatic game players. We employ a deep reinforcement learning framework to jointly learn state representations and action policies using game rewards as feedback. This framework enables us to map text descriptions into vector representations that capture the semantics of the game states. We evaluate our approach on two game worlds, comparing against baselines using bag-of-words and bag-of-bigrams for state representations. Our algorithm outperforms the baselines on both worlds demonstrating the importance of learning expressive representations.Comment: 11 pages, Appearing at EMNLP, 201

Automated Game Design Learning

While general game playing is an active field of research, the learning of game design has tended to be either a secondary goal of such research or it has been solely the domain of humans. We propose a field of research, Automated Game Design Learning (AGDL), with the direct purpose of learning game designs directly through interaction with games in the mode that most people experience games: via play. We detail existing work that touches the edges of this field, describe current successful projects in AGDL and the theoretical foundations that enable them, point to promising applications enabled by AGDL, and discuss next steps for this exciting area of study. The key moves of AGDL are to use game programs as the ultimate source of truth about their own design, and to make these design properties available to other systems and avenues of inquiry.Comment: 8 pages, 2 figures. Accepted for CIG 201

Does Language Determine Our Scientific Ideas?

SummaryThis paper argues that the influence of language on science, philosophy and other field is mediated by communicative practices. Where communications is more restrictive, established linguistic structures exercise a tighter control over innovations and scientifically motivated reforms of language. The viewpoint here centers on the thesis that argumentation is crucial in the understanding and evaluation of proposed reforms and that social practices which limit argumentation serve to erode scientific objectivity. Thus, a plea is made for a sociology of scientific belief designed to understand and insure social‐institutional conditions of the possibility of knowledge and its growth. A chief argument draws on work of Axelrod concerning the evolution of cooperation

Are you Charlie or Ahmed? Cultural pluralism in Charlie Hebdo response on Twitter

We study the response to the Charlie Hebdo shootings of January 7, 2015 on Twitter across the globe. We ask whether the stances on the issue of freedom of speech can be modeled using established sociological theories, including Huntington's culturalist Clash of Civilizations, and those taking into consideration social context, including Density and Interdependence theories. We find support for Huntington's culturalist explanation, in that the established traditions and norms of one's "civilization" predetermine some of one's opinion. However, at an individual level, we also find social context to play a significant role, with non-Arabs living in Arab countries using #JeSuisAhmed ("I am Ahmed") five times more often when they are embedded in a mixed Arab/non-Arab (mention) network. Among Arabs living in the West, we find a great variety of responses, not altogether associated with the size of their expatriate community, suggesting other variables to be at play.Comment: International AAAI Conference on Web and Social Media (ICWSM), 201

Models of Cooperation, Learning and Catastrophic Risk

Our world presents us with dangers and opportunities. Some of these dangers and opportunities are easier to handle if two or more individuals learn to cooperate. This thesis contributes five papers about cooperation, learning and catastrophic risk. In papers I-II, we consider the Finitely Repeated Prisoners' Dilemma, a model for where cooperation between two players is particularly hard to achieve. We introduce and model strategies that attempt to convince others to cooperate when backward induction can be used to eliminate cooperation for a number of steps from the end. We find that in a population with these strategies, cooperation can become recurrent, and we examine the conditions for this. Recurrent cooperation is possible in an evolutionary model (paper I) as well as in a population of players that are near-perfect Bayesian expected utility-maximizers (paper II). In paper III, we consider a bargaining model of climate negotiations where players negotiate emissions and sudden catastrophic damage occurs if emissions exceed a threshold amount. We introduce and model a mechanism of strategic reasoning, where players predict the emission bids of others, and consider how this affects the possibility of reaching agreements preventing catastrophic damage. We find that the effect of higher levels of strategic reasoning makes it harder to reach agreements in the model. This effect can be partially mitigated by restricting the range of initial bids in the bargaining process. In paper IV, we consider the arguments by Hanson and Bostrom about the Great Filter as an attempt to explain the Fermi Paradox. According to these arguments, finding extraterrestrial life on one planet should lower our expectations for humanity's prospects to progress far beyond our current technological capabilities. We model this claim as a Bayesian learning problem and examine the effect a single observation of life has in the model. We find that the conclusion of the argument depends critically on the choice of prior distribution. In paper V, we consider a model of agricultural markets and land-use competition between food and bioenergy crops. Agents in the model represent farmers who decide which crop to grow depending on predictors that give future price expectations. We model agents who can switch among predictors to make their decisions. We find that some predictor types can be concentrated on key parcels of land, which reduces volatility in crop prices for the system. We also examine several mechanisms that can bring price fluctuations in the system down and closer to a stable state

AI Researchers, Video Games Are Your Friends!

If you are an artificial intelligence researcher, you should look to video games as ideal testbeds for the work you do. If you are a video game developer, you should look to AI for the technology that makes completely new types of games possible. This chapter lays out the case for both of these propositions. It asks the question "what can video games do for AI", and discusses how in particular general video game playing is the ideal testbed for artificial general intelligence research. It then asks the question "what can AI do for video games", and lays out a vision for what video games might look like if we had significantly more advanced AI at our disposal. The chapter is based on my keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad audience.Comment: in Studies in Computational Intelligence Studies in Computational Intelligence, Volume 669 2017. Springe