27,844 research outputs found

    Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge

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    This paper summarizes the AAIA'17 Data Mining Challenge: Helping AI to Play Hearthstone which was held between March 23, and May 15, 2017 at the Knowledge Pit platform. We briefly describe the scope and background of this competition in the context of a more general project related to the development of an AI engine for video games, called Grail. We also discuss the outcomes of this challenge and demonstrate how predictive models for the assessment of player's winning chances can be utilized in a construction of an intelligent agent for playing Hearthstone. Finally, we show a few selected machine learning approaches for modeling state and action values in Hearthstone. We provide evaluation for a few promising solutions that may be used to create more advanced types of agents, especially in conjunction with Monte Carlo Tree Search algorithms.Comment: Federated Conference on Computer Science and Information Systems, Prague (FedCSIS-2017) (Prague, Czech Republic

    M-Commerce Implementation in Nigeria: Trends and Issues

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    Nigeria was described as the fastest growing telecoms nation in Africa and the third in the World. The country had experienced a phenomenal growth from a teledensity of 0.49 in 2000 to 25.22 in 2007. This trend has brought about a monumental development in the major sectors of the economy, such as banking, telecoms and commerce in general. This paper presents the level of adoption of ICT in the banking sector and investigates the prospects of m-Commerce in Nigeria based on strengths, weaknesses, opportunities and threats (SWOT) analysis. Findings revealed that all banks in Nigeria offer e-Banking services and about 52% of the offer some forms of m-Banking services. The banks and the telecoms operators have enormous potentials and opportunities for m-Commerce but the level of patronage, quality of cell phones, lack of basic infrastructure and security issues pose a major threat to its wide scale implementation

    Scaling of city attractiveness for foreign visitors through big data of human economical and social media activity

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    Scientific studies investigating laws and regularities of human behavior are nowadays increasingly relying on the wealth of widely available digital information produced by human social activity. In this paper we leverage big data created by three different aspects of human activity (i.e., bank card transactions, geotagged photographs and tweets) in Spain for quantifying city attractiveness for the foreign visitors. An important finding of this papers is a strong superlinear scaling of city attractiveness with its population size. The observed scaling exponent stays nearly the same for different ways of defining cities and for different data sources, emphasizing the robustness of our finding. Temporal variation of the scaling exponent is also considered in order to reveal seasonal patterns in the attractivenessComment: 8 pages, 3 figures, 1 tabl
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