Games with a purpose for social networking platforms

Rafelsberger, Walter; Scharl, Arno

doi:10.1145/1557914.1557948

Cited by 35 publications

(39 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Through their social game Magpies, also on Facebook, Kirman et al [17] found that the additional sociocontextual data such as social network analysis (SNA) information can increase the frequency of social activity between players engaged in the game but does little to increase the growth of the player-base. Similar to this paper, Rafelsberger and Scharl [18] propose an application framework to develop interactive games with a purpose on top of social networking platforms, leveraging the wisdom of the crowd by engaging users in online games to complete tasks that are trivial for humans but difficult for computers. In this paper, we leverage the social aspects of OSNs both to recruit human trainers and to improve the performance of the agents they train.…”

Section: B Social Networkmentioning

confidence: 99%

Learning from human reward benefits from socio-competitive feedback

Hung

Whiteson

et al. 2014

4th International Conference on Development and Learning and on Epigenetic Robotics

View full text Add to dashboard Cite

Abstract-Learning from rewards generated by a human trainer observing an agent in action has proven to be a powerful method for non-experts in autonomous agents to teach such agents to perform challenging tasks. Since the efficacy of this approach depends critically on the reward the trainer provides, we consider how the interaction between the trainer and the agent should be designed so as to increase the efficiency of the training process. This paper investigates the influence of the agent's socio-competitive feedback on the human trainer's training behavior and the agent's learning. The results of our user study with 85 subjects suggest that the agent's sociocompetitive feedback substantially increases the engagement of the participants in the game task and improves the agents' performance, even though the participants do not directly play the game but instead train the agent to do so. Moreover, making this feedback active further induces more subjects to train the agents longer but does not further improve agent performance. Our analysis suggests that this may be because some trainers train a more complex behavior in the agent that is appropriate for a different performance metric that is sometimes associated with the target task.

show abstract

Section: B Social Networkmentioning

confidence: 99%

Learning from human reward benefits from socio-competitive feedback

Hung

Whiteson

et al. 2014

4th International Conference on Development and Learning and on Epigenetic Robotics

View full text Add to dashboard Cite

show abstract

“…Human language technology games integrated into social networking sites such as Sentiment Quiz [63] on Facebook show that social interaction within a game environment does motivate players to participate. The Sentiment Quiz asks players to select a level of sentiment (on a 5 point scale) associated with a word taken from a corpus of documents regarding the 2008 US Presidential election.…”

Section: Sentiment Analysismentioning

confidence: 99%

Using Games to Create Language Resources: Successes and Limitations of the Approach

Chamberlain

Fort

Kruschwitz

et al. 2013

The People’s Web Meets NLP

View full text Add to dashboard Cite

One of the more novel approaches to collaboratively creating language resources in recent years is to use online games to collect and validate data. The most significant challenges collaborative systems face are how to train users with the necessary expertise and how to encourage participation on a scale required to produce high quality data comparable with data produced by "traditional" experts. In this chapter we provide a brief overview of collaborative creation and the different approaches that have been used to create language resources, before analysing games used for this purpose. We discuss some key issues in using a gaming approach, including task design, player motivation and data quality, and compare the costs of each approach in terms of development, distribution and ongoing administration. In conclusion, we summarise the benefits and limitations of using a gaming approach to resource creation and suggest key considerations for evaluating its utility in different research scenarios.

show abstract

“…We will extend our game portfolio with new Facebook-based games similar to the already successfully deployed Sentiment Quiz (Rafelsberger and Scharl, 2009). The Sentiment Quiz addressed major challenges of GWAPs such as how to attract and retain players, how to ensure the generation of high quality data, and how to effectively aggregate results.…”

Section: Low-overhead Forms Of User Feedbackmentioning

confidence: 99%

Confidence Management for Learning Ontologies from Dynamic Web Sources

Wohlgenannt

Weichselbraun

Scharl

et al. 2012

Proceedings of the International Conference on Knowledge Engineering and Ontology Development

View full text Add to dashboard Cite

Abstract:Dynamic environments require effective update mechanisms for ontologies to incorporate new knowledge.In this position paper we present a dynamic framework for ontology learning which integrates automated learning methods with rapid user feedback mechanism to build and extend lightweight domain ontologies at regular intervals. Automated methods collect evidence from a variety of heterogeneous sources and generate an ontology with spreading activation techniques, while crowdsourcing in the form of Games with a Purpose validates the new ontology elements. Special data structures support dynamic confidence management in regards to three major aspects of the ontology: (i) the incoming facts collected from evidence sources, (ii) the relations that constitute the extended ontology, and (iii) the observed quality of evidence sources. Based on these data structures we propose trend detection experiments to measure not only significant changes in the domain, but also in the conceptualization suggested by user feedback.

show abstract

Games with a purpose for social networking platforms

Cited by 35 publications

References 7 publications

Learning from human reward benefits from socio-competitive feedback

Learning from human reward benefits from socio-competitive feedback

Using Games to Create Language Resources: Successes and Limitations of the Approach

Confidence Management for Learning Ontologies from Dynamic Web Sources

Contact Info

Product

Resources

About