Modelling Global Pattern Formations for Collaborative Learning Environments

Grappiolo, Corrado; Cheong, Yun-Gyung; Khaled, Rilla; Yannakakis, Georgios N.

doi:10.1109/icalt.2012.17

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…Thus, the metrics T GB, N H, GC and F I (or any other metrics which correlate well to those) can be used to detect unfair treatments which may lead to social conflicts [7,61]. Similarly, fairness metrics can be used to extract student profiles in collaborative educational games [21,61].…”

Section: Discussionmentioning

confidence: 99%

“…The fairness metrics proposed can be used in both simulated scenarios of artificial agent societies to investigate global phenomena, such as collaboration and the emergence of group structures [23,24], or in educational collaborative virtual environments, in which human-controlled avatars interact with each other [21,61].…”

Section: Discussionmentioning

confidence: 99%

“…The use of crowdsourcing and the correlation analysis we performed has been used in game artificial intelligence research, though centred on affect modelling in single player game environments [51,57,59]. Our one-to-many interaction scenario, however, provides the basis for the investigation of multi-agent and multiplayer scenarios [21,23,24].…”

Section: Related Workmentioning

confidence: 99%

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Validating Generic Metrics of Fairness in Game-Based Resource Allocation Scenarios with Crowdsourced Annotations

Grappiolo

Martínez

Yannakakis

2014

Transactions on Computational Intelligence XIII

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Being able to effectively measure the notion of fairness is of vital importance as it can provide insight into the formation and evolution of complex patterns and phenomena, such as social preferences, collaboration, group structures and social conflicts. This paper presents a comparative study for quantitatively modelling the notion of fairness in one-to-many resource allocation scenarios-i.e. one provider agent has to allocate resources to multiple receiver agents. For this purpose, we investigate the efficacy of six metrics and cross-validate them on crowdsourced human ranks of fairness annotated through a computer game implementation of the one-to-many resource allocation scenario. Four of the fairness metrics examined are well-established metrics of data dispersion, namely standard deviation, normalised entropy, the Gini coefficient and the fairness index. The fifth metric, proposed by the authors, is an ad-hoc context-based measure which is based on key aspects of distribution strategies. The sixth metric, finally, is machine learned via ranking support vector machines (SVMs) on the crowdsourced human perceptions of fairness. Results suggest that all ad-hoc designed metrics correlate well with the human notion of fairness, and the context-based metrics we propose appear to have a predictability advantage over the other ad-hoc metrics. On the other hand, the normalised entropy and fairness index metrics appear to be the most expressive and generic for measuring fairness for the scenario adopted in this study and beyond. The SVM model can automatically model fairness more accurately than any ad-hoc metric examined (with an accuracy of 81.86%) but it is limited by its expressivity and generalisability.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Validating Generic Metrics of Fairness in Game-Based Resource Allocation Scenarios with Crowdsourced Annotations

Grappiolo

Martínez

Yannakakis

2014

Transactions on Computational Intelligence XIII

Self Cite

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“…In addition, data-driven modeling of groups of NPCs and players via group structure identification [16] can offer a complementary perspective towards well-grounded human behavior models [9] that can guide personalization in social games.…”

Section: Npc Ai: Different Perspectivesmentioning

confidence: 99%

Game AI revisited

Yannakakis

2012

Proceedings of the 9th Conference on Computing Frontiers

138

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More than a decade after the early research efforts on the use of artificial intelligence (AI) in computer games and the establishment of a new AI domain the term "game AI" needs to be redefined. Traditionally, the tasks associated with game AI revolved around non player character (NPC) behavior at different levels of control, varying from navigation and pathfinding to decision making. Commercial-standard games developed over the last 15 years and current game productions, however, suggest that the traditional challenges of game AI have been well addressed via the use of sophisticated AI approaches, not necessarily following or inspired by advances in academic practices. The marginal penetration of traditional academic game AI methods in industrial productions has been mainly due to the lack of constructive communication between academia and industry in the early days of academic game AI, and the inability of academic game AI to propose methods that would significantly advance existing development processes or provide scalable solutions to real world problems. Recently, however, there has been a shift of research focus as the current plethora of AI uses in games is breaking the non-player character AI tradition. A number of those alternative AI uses have already shown a significant potential for the design of better games.This paper presents four key game AI research areas that are currently reshaping the research roadmap in the game AI field and evidently put the game AI term under a new perspective. These game AI flagship research areas include the computational modeling of player experience, the procedural generation of content, the mining of player data on massive-scale and the alternative AI research foci for enhancing NPC capabilities.

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Modelling Global Pattern Formations for Collaborative Learning Environments

Cited by 2 publications

References 13 publications

Validating Generic Metrics of Fairness in Game-Based Resource Allocation Scenarios with Crowdsourced Annotations

Validating Generic Metrics of Fairness in Game-Based Resource Allocation Scenarios with Crowdsourced Annotations

Game AI revisited

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