Using camera motion to identify different types of American football plays

This paper presents a method that uses camera motion parameters to recognise 7 types of American football plays. The approach is based on the motion information extracted from the video and it can identify short and long pass plays, short and long running plays, quarterback sacks, punt plays and kickoff plays. This method has the advantage that it is fast and it does not require player or ball tracking. The system was trained and tested using 782 plays and the results show that the system has an overall classification accuracy of 68%.<br /

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

An Experiential Comparative Tool for Board Games

In the field of game studies, contemporary board games have until now remained relatively unexplored. The recent years have allowed us to witness the emergence of the occasional academic texts focusing on board games – such as Eurogames (Woods, 2012), Characteristics of Games (Elias et al. 2013), and most recently Game Play: Paratextuality in Contemporary Board Games (Booth, 2015). The mentioned authors all explore board games from diverse viewpoints but none of these authors present a viable and practical analytical tool to allow us to examine and differentiate one board game from another. In this vein, this paper seeks to present an analytical comparative tool intended specifically for board games. The tool builds upon previous works (Aarseth et al. 2003; Elias et al. 2012; and Woods 2012) to show how four categories – rules, luck, interaction and theme – can interact on different levels to generate diverse gameplay experiences. Such a tool allows to score games objectively and separately in each of the categories to create a combined gameplay experience profile for each board game. Following this, the paper proceeds to present numerous practical examples of contemporary board games and how it can be used from a design perspective and an analytical perspective alike

Analysing the behaviour of robot teams through relational sequential pattern mining

This report outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to verify the effective collaboration among the members of a team and comparing the different multi-agent behaviours. Using external observations of a Multi-Agent System to analyse, model, recognize agent behaviour could be very useful to direct team actions. In particular, this report focuses on the challenge of autonomous unsupervised sequential learning of the team's behaviour from observations. Our approach allows to learn a symbolic sequence (a relational representation) to translate raw multi-agent, multi-variate observations of a dynamic, complex environment, into a set of sequential behaviours that are characteristic of the team in question, represented by a set of sequences expressed in first-order logic atoms. We propose to use a relational learning algorithm to mine meaningful frequent patterns among the relational sequences to characterise team behaviours. We compared the performance of two teams in the RoboCup four-legged league environment, that have a very different approach to the game. One uses a Case Based Reasoning approach, the other uses a pure reactive behaviour.Comment: 25 page

Deciding the Borel complexity of regular tree languages

We show that it is decidable whether a given a regular tree language belongs to the class ${\bf \Delta^0_2}$ of the Borel hierarchy, or equivalently whether the Wadge degree of a regular tree language is countable.Comment: 15 pages, 2 figure

Tailoring persuasive health games to gamer type

Persuasive games are an effective approach for motivating health behavior, and recent years have seen an increase in games designed for changing human behaviors or attitudes. However, these games are limited in two major ways: first, they are not based on theories of what motivates healthy behavior change. This makes it difficult to evaluate why a persuasive approach works. Second, most persuasive games treat players as a monolithic group. As an attempt to resolve these weaknesses, we conducted a large-scale survey of 642 gamers' eating habits and their associated determinants of healthy behavior to understand how health behavior relates to gamer type. We developed seven different models of healthy eating behavior for the gamer types identified by BrainHex. We then explored the differences between the models and created two approaches for effective persuasive game design based on our results. The first is a one-size-fits-all approach that will motivate the majority of the population, while not demotivating any players. The second is a personalized approach that will best motivate a particular type of gamer. Finally, to make our approaches actionable in persuasive game design, we map common game mechanics to the determinants of healthy behavior

Social Psychology And Marketing: The Consumption Game. Understanding Marketing And Consumer Behavior Through Game Theory

Consumer psychology provides enough evidence that consumer behavior is not just one side of our existence, but, as a matter of fact, it is a central dimension of our everyday lives, engaging us into changing and defining our identity, beliefs, attitudes and practices. In relation to this, commodification has reached us on all levels: everything that people created, produced and developed over the years, during the post-industrial era, can be commodified and sold to a specific market. Commodification and increased consumption are crossing the line between values and needs, production and creation, identity and capital accumulation, thus making people constantly expecting a payoff while engaging in social, cultural and economic transactions. In this article we argue that we can use the models of game theory to understand socio-economic phenomena such as consumption, B2C marketing and market dynamics.Game Theory, consumer behaviour, commodification, decision theory, marketing