If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The process of play testing a game is subjective, expensive and incomplete. In this paper, we present a play-testing approach that explores the game space with automated agents and collects data to answer questions posed by the designers. Rather than have agents interacting with an actual game client, this approach recreates the bare bone mechanics of the game as a separate system. Our agent is able to play in minutes what would take testers days of organic gameplay. The analysis of thousands of game simulations exposed imbalances in game actions, identified inconsequential rewards and evaluated the effectiveness of optional strategic choices. Our test case game, The Sims Mobile, was recently released and the findings shown here influenced design changes that resulted in improved player experience.
In this paper we present an approach to using sequence analysis to model player behavior. This approach is designed to work in game development contexts, integrating production teams and delivering profiles that inform game design. We demonstrate the method via a case study of the game T om Clancy’s The Division, which with its 20 million players represents a major current commercial title. The approach presented provides a mixed-methods framework, combining qualitative knowledge elicitation and workshops with large-scale telemetry analysis, using sequence mining and clustering to develop detailed player profiles showing the core game-play loops of The Division’s players.
Canaan, Rodrigo (New York University)
Many modern creative industrial processes rely on the collaboration between multiple humans, assisted by one or more computational systems, in a complex environment. However, most traditional systems lack the adaptability required to contribute in a flexible, co-creative manner, instead executing a fixed set of tasks in a preset time schedule. We believe games, especially cooperative games offer an ideal platform to conduct research in co-creativity. We present our motivation, preliminary work and future goals to study, build and measure game-inspired co-creative AI systems.
Although previous work has shown that member and structural features are important to the future popularity of groups in EBSN, it is not yet clear how different member roles and the interplay between them contribute to group popularity. In this paper, we study a real-world dataset from Meetup --- a popular EBSN platform --- and propose a deep neural network based method to predict the popularity of new Meetup groups. Our method uses group-level features specific to event-based social networks, such as time and location of events in a group, as well as the structural features internal to a group, such as the inferred member roles in a group and social substructures among members. Empirically, our approach reduces the RMSE of the popularity prediction (measured in RSVPs) of a group's future events by up to 12%, against the state-of-the-art baselines.
Khatibi, Amir (Federal University of Minas Gerais) | Belem, Fabiano (Federal University of Minas Gerais) | Silva, Ana P. (Federal University of Minas Gerais) | Shasha, Dennis (New York University) | Almeida, Jussara Federal University of Minas Gerais (Federal University of Minas Gerais) | Goncalves, Marcos A. (Federal University of Minas Gerais)
Accurate predictions about future events is essential in many areas, one of them being the Tourism Industry. Usually, countries and cities invest a huge amount of money in planning and preparation in order to welcome (and profit from) tourists. An accurate prediction of the number of visits in the following days or months could help both the economy and tourists. Prior studies in this domain explore forecasting for a whole country rather than for fine-grained areas within a country (e.g., specific touristic attractions). In this work, we suggest that accessible data from online social networks and travel websites, in addition to climate data, can be used to support the inference of visitation count for many touristic attractions. To test our hypothesis we analyze visitation, climate and social media data in more than 70 National Parks in U.S during the last 3 years. The experimental results reveal a high correlation between social media data and tourism demands; in fact, in over 80\% of the parks, social media reviews and visitation counts are correlated by more than 50\%. Moreover, we assess the effectiveness of employing various prediction techniques, finding that even a simple linear regression model, when fed with social media and climate data as input features, can attain a prediction accuracy of over 80\% while a more robust algorithm, such as Support Vector Regression, reaches up to 94\% accuracy.
Online communities provide a unique way for individuals to access information from those in similar circumstances, which can be critical for health conditions that require daily and personalized management. As these groups and topics often arise organically, identifying the types of topics discussed is necessary to understand their needs. As well, these communities and people in them can be quite diverse, and existing community detection methods have not been extended towards evaluating these heterogeneities. This has been limited as community detection methodologies have not focused on community detection based on semantic relations between textual features of the user-generated content. Thus here we develop an approach, NeuroCom, that optimally finds dense groups of users as communities in a latent space inferred by neural representation of published contents of users. By embedding of words and messages, we show that NeuroCom demonstrates improved clustering and identifies more nuanced discussion topics in contrast to other common unsupervised learning approaches.
Event Extraction (EE) is a challenging Information Extraction task which aims to discover event triggers of specific types along with their arguments. Most recent research on Event Extraction relies on pattern-based or feature-based approaches, trained on annotated corpora, to recognize combi- nations of event triggers, arguments, and other contextual in- formation. However, as the event instances in the ACE corpus are not evenly distributed, some frequent expressions involving ACE event triggers do not appear in the training data, adversely affecting the performance. In this paper, we demon- strate the effectiveness of systematically importing expert-level patterns from TABARI to boost EE performance. The experimental results demonstrate that our pattern-based sys- tem with the expanded patterns can achieve 69.8% (with 1.9% absolute improvement) F-measure over the baseline, an advance over current state-of-the-art systems.
Marketing academics and practitioners recognize the importance of monitoring consumer online conversations about brands. The focus so far has been on text content. However, images are on their way to surpassing text as the medium of choice for social conversations. In these images, consumers often tag brands. We propose a ``visual listening in" approach to measuring how brands are portrayed on social media (Instagram) by mining visual content posted by users, and show what insights brand managers can gather from social media by using this approach. We first use two supervised machine learning methods, traditional support vector machine classifiers and deep convolutional neural networks, to measure brand attributes (glamorous, rugged, healthy, fun) from images. We then apply the classifiers to brand-related images posted on social media. We study 56 brands in the apparel and beverages categories, and compare their portrayal in consumer-created images with images on the firm's official Instagram account, as well as with consumer brand perceptions measured in a national brand survey. Although the three measures exhibit convergent validity, we find key differences between how consumers and firms portray the brands on visual social media, and how the average consumer perceives the brands.
The current neural network models for event detection have only considered the sequential representation of sentences. Syntactic representations have not been explored in this area although they provide an effective mechanism to directly link words to their informative context for event detection in the sentences. In this work, we investigate a convolutional neural network based on dependency trees to perform event detection. We propose a novel pooling method that relies on entity mentions to aggregate the convolution vectors. The extensive experiments demonstrate the benefits of the dependency-based convolutional neural networks and the entity mention-based pooling method for event detection. We achieve the state-of-the-art performance on widely used datasets with both perfect and predicted entity mentions.
In this paper, we extend an attention-based neural machine translation (NMT) model by allowing it to access an entire training set of parallel sentence pairs even after training. The proposed approach consists of two stages. In the first stage –retrieval stage–, an off-the-shelf, black-box search engine is used to retrieve a small subset of sentence pairs from a training set given a source sentence. These pairs are further filtered based on a fuzzy matching score based on edit distance. In the second stage–translation stage–, a novel translation model, called search engine guided NMT (SEG-NMT), seamlessly uses both the source sentence and a set of retrieved sentence pairs to perform the translation. Empirical evaluation on three language pairs (En-Fr, En-De, and En-Es) shows that the proposed approach significantly outperforms the baseline approach and the improvement is more significant when more relevant sentence pairs were retrieved.