The transfer of cognitive skill
The transfer of cognitive skill
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
IEEE Intelligent Systems
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Improving Learning Transfer in Organizations
Improving Learning Transfer in Organizations
Graph-based relational learning: current and future directions
ACM SIGKDD Explorations Newsletter
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Behavior transfer for value-function-based reinforcement learning
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
An experts algorithm for transfer learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Optimal information placement in an interactive 3D environment
Proceedings of the 2007 ACM SIGGRAPH symposium on Video games
Discovering 3D surface information values from gameplayers
IEEE Computer Graphics and Applications - Special issue title on demystifying visual analytics impaired driving in virtual spaces
Neural networks training for weapon selection in first-person shooter games
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
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The ability to transfer knowledge learned in one environment in order to improve performance in a different environment is one of the hallmarks of human intelligence. Insights into human transfer learning help us to design computer-based agents that can better adapt to new environments without the need for substantial reprogramming. In this paper we study the transfer of knowledge by humans playing various scenarios in a graphically realistic urban setting which are specifically designed to test various levels of transfer. We determine the amount and type of transfer that is being performed based on the performance of human trained and untrained players. In addition, we use a graph-based relational learning algorithm to extract patterns from player graphs. These analyses reveal that indeed humans are transferring knowledge from one set of games to another and the amount and type of transfer varies according to player experience and scenario complexity. The results of this analysis help us understand the nature of human transfer in such environments and shed light on how we might endow computer-based agents with similar capabilities. The game simulator and human data collection also represent a significant testbed in which other AI capabilities can be tested and compared to human performance.