In this paper we introduce LingoTowns, a new gwap platform targeting language learners. LingoTowns provides a unified experience integrating games for multiple aspects of lexical and grammatical experience in a single virtual world, whilst simultaneously collecting judgements. Both LingoTowns and its constituent games are designed to provide more engagement to the players/ learners than normal gwaps. The platform also incorporates knowledge tracing methods ensuring that the players' progress in terms of understanding of grammatical concepts is tracked both at the individual game level and overall.

Coreference resolution is a key aspect of text comprehension, but the size of the available coreference corpora for Arabic is limited in comparison to the size of the corpora for other languages.In this paper we present a Game-With-A-Purpose called Stroll with a Scroll created to collect from players coreference annotations for Arabic.The key contribution of this work is the embedding of the annotation task in a virtual world setting, as opposed to the puzzle-type games used in previously proposed Games-With-A-Purpose for coreference.

This paper describes the process of developing and collecting data for analysis via a Qualtrics survey on “Tend & Befriend Theory” and the Acute Stress Response, i.e. “Fight or Flight Response”. We discuss the constraints and implications of current thinking around “Tend & Befriend”, the descriptive results of our initial study, present a methodology for categorising Tend & Befriend games, frame our results in the context of gaming experience, and outline our next research steps in addition to areas of future interest. Our study suggests that Tend & Befriend can be considered as a concrete phenomenon in games, supported by data. Our findings show some games can be considered as “archetypal” titles, making them useful references for research and discourse.

As the uses of Games-With-A-Purpose (GWAPs) broadens, the systems that incorporate its usages have expanded in complexity. The types of annotations required within the NLP paradigm set such an example, where tasks can involve varying complexity of annotations. Assigning more complex tasks to more skilled players through a progression mechanism can achieve higher accuracy in the collected data while acting as a motivating factor that rewards the more skilled players. In this paper, we present the progression technique implemented in Wormingo , an NLP GWAP that currently includes two layers of task complexity. For the experiment, we have implemented four different progression scenarios on 192 players and compared the accuracy and engagement achieved with each scenario.

What distinguishes a good AI opponent from a bad one in the eyes of players? To answer this question, hundreds of opinions were analysed, as expressed by strategy-game players in forums; from these, a grounded theory was formed. It was found that the AI’s role as an opponent in a game shapes player expectations, and that not breaking these is instrumental to players’ enjoyment. The expectations include: keeping a tension between the player and AI; maintaining a level playing field; and more subtle expectations involving closure and the ability to rectify behaviour. The nature of these expectations are explored, as well as how they might be upheld.

In this paper we present Wormingo, 1 a new Game-with-a-Purpose for anaphoric annotation. It introduces the motivation-annotation paradigm which uses linguistic puzzles and other widely known gamification techniques and word game mechanics to motivate players to carry out anaphoric annotation tasks. In a preliminary experiment, the game was tested on 270 players recruited through the Reddit platform, achieving promising results.

Games for text annotation / labelling are becoming more common, but it’s difficult to find a mechanics that fits. In this work we discuss a clicker game that can support text annotation. We believe this type of game is uniquely suited to addressing some of the challenges faced by games featuring text annotation as a core task.

In this paper we present WordClicker, a clicker game for text annotation. We believe the mechanics of 'Ville type Free-To-Play (F2P) games in general, and clicker games in particular, is particularly suited for GWAPs (Games-With-A-Purpose). WordClicker was developed as one component of a suite of GWAPs meant to cover all aspects of language interpretation, from tokenization to anaphoric interpretation. As such, WordClicker is intended to have a dual function as part of this suite of GWAPs: both for parts-of-speech annotation and for teaching players about parts of speech so that they can go on and play GWAPs for more complex syntactic annotation. Therefore, game-based language learning platforms also had a strong influence on its design.

This paper outlines the Hanabi competition, first run at CIG 2018, and returning for COG 2019. Hanabi presents a useful domain for game agents which must function in a cooperative environment. The paper presents the results of the two tracks which formed the 2018 competition and introduces the learning track, a new track for 2019 which allows the agents to collect statistics across multiple games.

In this paper, we propose a social negotiation system in which agents can communicate and interact with each other socially throughout a Sheriff of Nottingham game. We address issues with the number of options available while negotiating, particularly when bluffing is involved. Experiments are proposed that would allow us to validate how closely this framework mirrors real social interaction in the game, and the possibility of generalising multi-agent negotiation beyond this framework is raised.

We argue that the mechanics of 'Ville type Free-To-Play (F2P) games in general, and incremental games in particular, is especially suited for Games-With-A-Purpose. We demonstrate this through WordClicker, an incremental game whose mechanics is designed for text labelling. We believe the design and mechanics used are highly transferable to other games featuring annotation where game design is a challenge, such as serious games and language resourcing GWAPs. The game was tested with audiences from three popular indie gaming portals, achieving promising results both for entertainment value and learning.

In this paper we outline our game framework, Hexboard, and its possible applications for games research. We outline the architecture of the game framework and the reasons for our design choices. We present games created in the framework, both by ourselves and students enrolled on a game design course. We found that a wide variety of interesting games could be made with the Hexboard framework with relative ease. Finally, we outline future work for this framework and some possible uses for it within games research.

Agent modelling involves considering how other agents will behave, in order to influence your own actions. In this paper, we explore the use of agent modelling in the hidden-information, collaborative card game Hanabi. We implement a number of rule-based agents, both from the literature and of our own devising, in addition to an Information Set-Monte Carlo Tree Search (IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new, predictor version that uses a model of the agents with which it is paired. We observe a significant improvement in game-playing strength from this agent in comparison to IS-MCTS, resulting from its consideration of what the other agents in a game would do. In addition, we create a flawed rule-based agent to highlight the predictor's capabilities with such an agent.

This paper describes a highly configurable game-with-a-purpose (GWAP) designed to explore the effects of different game mechanics on GWAP for NLP problems with a view to improving both quality of annotation and player uptake. The details of the game are discussed along with some of the questions the game hopes to answer.

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