Evaluating Go Game Records for Prediction of Player Attributes

We propose a way of extracting and aggregating per-move evaluations from sets of Go game records. The evaluations capture different aspects of the games such as played patterns or statistic of sente/gote sequences. Using machine learning algorithms, the evaluations can be utilized to predict different relevant target variables. We apply this methodology to predict the strength and playing style of the player (e.g. territoriality or aggressivity) with good accuracy. We propose a number of possible applications including aiding in Go study, seeding real-work ranks of internet players or tuning of Go-playing programs

Using Scratch to Teach Undergraduate Students' Skills on Artificial Intelligence

This paper presents a educational workshop in Scratch that is proposed for the active participation of undergraduate students in contexts of Artificial Intelligence. The main objective of the activity is to demystify the complexity of Artificial Intelligence and its algorithms. For this purpose, students must realize simple exercises of clustering and two neural networks, in Scratch. The detailed methodology to get that is presented in the article.Comment: 6 pages, 7 figures, workshop presentatio

Profiles of social networking sites users in the Netherlands

Online social networking has become a reality and integral part of the daily personal, social and business life. The extraordinary increase of the user numbers of Social Networking Sites (SNS) and the rampant creation of online communities presents businesses with many challenges and opportunities. From the commercial perspective, the SNS are an interesting and promising field: online social networks are important sources of market intelligence and also offer interesting options for co-operation, networking and marketing. For SMEs especially the Social Networking Sites represent a simple and low cost solution for listening the customer’s voice, reaching potential customers and creating extensive business networks. This paper presents the results of a national survey mapping the demographic, social and behavioral characteristics of the Dutch users of SNS. The study identifies four different user profiles and proposes a segmentation framework as basis for better understanding the nature and behavior of the participants in online communities. The findings present new insights to marketing strategists eager to use the communication potential of such communities; the findings are also interesting for businesses willing to explore the potential of online networking as a low cost yet very efficient alternative to physical, traditional networking

Evolving Neural Networks for the Capture Game

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