A Framework for Exploring and Evaluating Mechanics in Human Computation Games

Human computation games (HCGs) are a crowdsourcing approach to solving computationally-intractable tasks using games. In this paper, we describe the need for generalizable HCG design knowledge that accommodates the needs of both players and tasks. We propose a formal representation of the mechanics in HCGs, providing a structural breakdown to visualize, compare, and explore the space of HCG mechanics. We present a methodology based on small-scale design experiments using fixed tasks while varying game elements to observe effects on both the player experience and the human computation task completion. Finally we discuss applications of our framework using comparisons of prior HCGs and recent design experiments. Ultimately, we wish to enable easier exploration and development of HCGs, helping these games provide meaningful player experiences while solving difficult problems.Comment: 11 pages, 5 figure

Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs

To make machines better understand sentiments, research needs to move from polarity identification to understanding the reasons that underlie the expression of sentiment. Categorizing the goals or needs of humans is one way to explain the expression of sentiment in text. Humans are good at understanding situations described in natural language and can easily connect them to the character's psychological needs using commonsense knowledge. We present a novel method to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. We efficiently integrate the acquired knowledge paths in a neural model that interfaces context representations with knowledge using a gated attention mechanism. We assess the model's performance on a recently published dataset for categorizing human needs. Selectively integrating knowledge paths boosts performance and establishes a new state-of-the-art. Our model offers interpretability through the learned attention map over commonsense knowledge paths. Human evaluation highlights the relevance of the encoded knowledge