![]() Onrpg: What makes AdventureQuest Worlds so unique?īohn:First, thank you so much for taking the time to interview us about our first massively multiplayer game! How is it unique? Well, many of our players might point out that we are the first MMO they played where you do not start off by killing rats. Boy, were they wrong! We at Onrpg humbly congratulate them on their first multiplayer release! It is not possible in flash.” Followed by a giant string of people agreeing.” said Adam Bohn. You will even discover their inspiration in the creation of the game: “One of the players on the forums said flat out, “There is no way AE can make a MMO. You will learn about what updates are to come in the game, the benefits of paying to play or remaining a free account player, and so much more. This interview focuses on their newest release, AdventureQuest Worlds. ![]() Onrpg was glad to have interviewed Adam Bohn, founder and CEO of Artix Entertainment (“better known as Artix Krieger, the pun spewing, undead slaying, lactose intolerant, wasabi loving Paladin”). The right strategies for applying benefits and avoiding pitfalls of 3D computational modeling-based ML prediction are pursuing high quality data, applying a capable ML algorithm, reinforcing field geology-based investigation and ratiocination, and keeping full feedback coupling with exploration processes to ensure correct decision-making and timely updating of data.Answered by Adam Bohn, founder anda CEO of Artix Entertainment But on the other hand, they have some hidden pitfalls to impede or misguide mineral exploration, such as over extrapolation producing false model, over interpolation creating false big data, overfitting generating false knowledge and over simplification causing false prediction. On the one hand, they can facilitate mineral exploration by characterizing the mineralization system in 3D visual and quantitative prediction of ore potentials. Such inconsistent predictions of different algorithms and the unsuccessful exploration stories in FHS and AQ mines demonstrate that the 3D computational modeling-based ML technologies are a two edged sword. ![]() The drills of the AQ Mine Outer Drilling Program (AQMODP) that failed in ore discovery are all at the locations with low RF PP, but with high ANN PP, suggesting that the RF prediction is more reliable than that of ANN. ![]() The RF prediction indicates that there is no significant potential there down to −1520 m, whilst the SVM prediction shows a few small high potentials there, and ANN prediction shows prominent high potentials there. The ANN, SVM and RF models of AQ mine gave different predictions, although the same data were imputed. The drills of the FHS Deep Drilling Program (FHSDDP) are all at the locations with low RF prediction probability (PP) of mineralization, which can explain why the FHSDDP had not discovered any orebodies and verifies the reliability of the RF prediction. The RF prediction indicates that there is no significant potential in the FHS mine down to depth of 1400 m. Based on the results of 3D geometric and geodynamic modeling, we conducted the random forest (RF) ML model in the FHS mine and the ML models respectively of artificial neural network (ANN), support vector machine (SVM) and RF in the AQ mine to predict mineral potentials. The dynamic simulations of the intrusions’ cooling processes suggest that the dilatant deformation produced by the coupled mechano-thermo-hydrological (MTH) processes is favor for mineralization. The 3D variation of resistivity can provide some ambiguous evidences for inferring contact zone and orebodies. The 3D geological models of the FHS and AQ orefields show that the topography and attitudes of the intrusions’ contact zones have spatial constraints on orebodies. This paper presents our studies on its application in the Fenghuangshan (FHS) and Anqing (AQ) mines, its benefits and pitfalls and the strategies for applying benefits and avoiding pitfalls. The 3D computational modeling-based machine learning (ML) prediction is an innovative methodology for exploration-targeting.
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