Skill Settings in MagePowerCraft
A.2 Ice Wizard
Skill Description
Freezing Sword Skill Power: 214%, Extra Skill Point: 556 Cost MP: Basic MP× 2.3%
Target: Multiple, AOE Type III: Front-Rectangular Attack Range: 0∼ 2.35 m (Near ∼ Far), Width: 0.6 m Attacking Skill Max Damage Hit: 3 Hit
Cast Time: 0.1 sec CD Time: 15 sec
Special Effect
Frostbiting: M ovingSpeed deduct× 50% and last for 2 sec.
Stunning: Last for 3.5 sec.
Freezing Field Skill Power: 279%, Extra Skill Point: 462 Cost MP: Basic MP× 3.1%
Target: Multiple, AOE Type II: Front-Field Emit Distance: 4 m, Radius: 1.41 m, Angle: 360◦ Attacking Skill Max Damage Hit: 1 Hit
Cast Time: 1.1 sec CD Time: 28 sec
Special Effect
Frostbiting: M ovingSpeed deduct× 50% and last for 8 sec.
Blizzard Skill Power: 210%, Extra Skill Point: 334 Cost MP: Basic MP× 2.3%
Target: Multiple, AOE Type II: Front-Field Emit Distance: 1.6 m, Radius: 1 m, Angle: 360◦ Attacking Skill Max Damage Hit: 2 Hit
Cast Time: 0.9 sec CD Time: 15 sec
Special Effect
Frostbiting: M ovingSpeed deduct× 50% and last for 8 sec.
Stunning: Last for 0.5 sec.
Instant Freeze Skill Power: 226%, Extra Skill Point: 270 Cost MP: Basic MP× 2.3%
Target: Multiple, AOE Type I: Self-Centered
Attack Range: 0∼ 1.88 m (Near ∼ Far), Angle: 360◦ Attacking Skill Max Damage Hit: 1 Hit
Cast Time: 0.5 sec
Chain Lightning Skill Power: 220%, Extra Skill Point: 85 Cost MP: Basic MP× 2.5%
Target: Multiple, AOE Type I: Self-Centered
Attack Range: 0∼ 5.89 m (Near ∼ Far), Angle: 360◦ Attacking Skill Max Damage Hit: 3 Hit
Cast Time: 0.8 sec CD Time: 20 sec
Special Effect
Electrocuting: Breaks action every 5 sec and last for 12 sec.
Grand Cross Skill Power: 169%, Extra Skill Point: 262 Cost MP: Basic MP× 3.2%
Target: Multiple, AOE Type III: Front-Rectangular Attack Range: 0.5∼ 11.29 m (Near ∼ Far), Width: 1.8 m Attacking Skill Max Damage Hit: 6 Hit
Cast Time: 2.6 sec CD Time: 28 sec
Special Effect Stunning: Last for 1 sec.
Heal Skill Power: 8%, Extra Skill Point: 324 Cost MP: Basic MP× 3%
Target: Multiple, AOE Type I: Self-Centered Effect Range: 0∼ 4 m (Near ∼ Far), Angle: 360◦ Aiding Skill Max Effect Hit: 1 Hit
Cast Time: 0.5 sec CD Time: 60 sec
Lightning Volt Skill Power: 181%, Extra Skill Point: 294 Cost MP: Basic MP× 2.7%
Target: Multiple, AOE Type III: Front-Rectangular Attack Range: 0∼ 4.94 m (Near ∼ Far), Width: 0.6 m Attacking Skill Max Damage Hit: 5 Hit
Cast Time: 0.5 sec CD Time: 18 sec
Special Effect
60% Electrocuting: Breaks action every 5 sec and last for 13 sec.
A.4 Shaman
Skill Description
Power Up Skill Power: 10%, Extra Skill Point: 0 Cost MP: Basic MP× 3%
Target: Multiple, AOE Type I: Self-Centered Effect Range: 0∼ 2.5 m (Near ∼ Far), Angle: 360◦ Aiding Skill Max Effect Hit: 1 Hit
Cast Time: 0.5 sec CD Time: 40 sec
Special Effect
Powering Up: Magical Attack Power enhanced by 10% for 15 sec.
Poison Spray Skill Power: 196%, Extra Skill Point: 262 Cost MP: Basic MP× 2.7%
Target: Multiple, AOE Type I: Self-Centered
Attack Range: 0∼ 3.12 m (Near ∼ Far), Angle: 60◦ Attacking Skill Max Damage Hit: 3 Hit
Cast Time: 0.4 sec CD Time: 22 sec
Special Effect
Poisoning: Takes 20% of last hit damage point every 2.5 sec and last for 11 sec.
Life Charge Skill Power: 3.5%, Extra Skill Point: 436 Cost MP: Basic MP× 4%
Target: Multiple, AOE Type I: Self-Centered Effect Range: 0∼ 4.2 m (Near ∼ Far), Angle: 360◦ Aiding Skill Max Effect Hit: 1 Hit
Cast Time: 1 sec CD Time: 60 sec
Special Effect
Regenerated: Regenerate 3.5% of Maximum HP every 2 sec and last for 9 sec.
Soul Blast Skill Power: 171%, Extra Skill Point: 274 Cost MP: Basic MP× 2.9%
Target: Multiple, AOE Type I: Self-Centered
Attack Range: 0.5∼ 5.02 m (Near ∼ Far), Angle: 360◦ Attacking Skill Max Damage Hit: 5 Hit
Cast Time: 1.5 sec CD Time: 21 sec
Bibliography
[Ang98] P. Angeline. Evolutionary optimization versus particle swarm optimiza-tion: Philosophy and performance differences. In Evolutionary Program-ming VII, pages 601--610, 1998.
[Bra95] J. Branke. Evolutionary algorithms for neural network design and training.
In Nordic Workshop on Genetic Algorithms and its Applications, 1995.
[CLL09] L. Cardamone, D. Loiacono, and P.L. Lanzi. Evolving competitive car controllers for racing games with neuroevolution. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pages 1179--1186, 2009.
[CLM04] N. Cole, S. Louis, and C. Miles. Using a genetic algorithm to tune first-person shooter bots. In Proceedings of the International Congress on Evo-lutionary Computation, volume 1, pages 139--145, 2004.
[CP98] E. Cant´u-Paz. A survey of parallel genetic algorithms. Calculateurs Par-alleles, Reseaux et Systems Repartis, 10(2):141--171, 1998.
[ES98] R. Eberhart and Y. Shi. Comparison between genetic algorithms and par-ticle swarm optimization. In Evolutionary Programming VII, pages 611--616, 1998.
[ES01] R.C. Eberhart and Y. Shi. Particle swarm optimization: developments, applications and resources. In Evolutionary computation, volume 1, pages 81--86, 2001.
[FMD02] K.D. Forbus, J.V. Mahoney, and K. Dill. How qualitative spatial reasoning can improve strategy game AIs. IEEE Intelligent Systems, 17(4):25--30, 2002.
[FP10] J. F¨urnkranz and A. Pfeifer. Creating adaptive game ai in a real time con-tinuous environment using neural networks. 2010.
[G+89] D.E. Goldberg et al. Genetic algorithms in search, optimization, and ma-chine learning. 1989.
[GMS03] R. Graham, H. McCabe, and S. Sheridan. Pathfinding in computer games.
ITB Journal, 8, 2003.
[HC04] R. Hunicke and V. Chapman. AI for dynamic difficulty adjustment in games. In Challenges in Game Artificial Intelligence AAAI Workshop, pages 91--96, 2004.
[HCDWV05] R. Hassan, B. Cohanim, O. De Weck, and G. Venter. A comparison of particle swarm optimization and the genetic algorithm. In Proceedings of the 1st AIAA Multidisciplinary Design Optimization Specialist Conference, 2005.
[Hol92] J.H. Holland. Genetic algorithms. Scientific American, 267(1):66--72, 1992.
[KE95] J. Kennedy and R. Eberhart. Particle swarm optimization. In Neural Net-works, volume 4, pages 1942--1948, 1995.
[Ken97] J. Kennedy. The particle swarm: social adaptation of knowledge. In Evo-lutionary Computation, pages 303--308, 1997.
[LSL08] R. Leigh, J. Schonfeld, and S.J. Louis. Using coevolution to understand and validate game balance in continuous games. In Genetic and evolutionary computation, pages 1563--1570, 2008.
[MN98] M. Matsumoto and T. Nishimura. Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. on Modeling and Computer Simulation, 8(1):3--30, 1998.
[OGR11] OGRE3D. Ogre, October 2011. http://www.ogre3d.org/.
[OYH08] J.K. Olesen, G.N. Yannakakis, and J. Hallam. Real-time challenge balance in an rts game using rtneat. In Computational Intelligence and Games, pages 87--94, 2008.
[Rie01] T. Riechmann. Genetic algorithm learning and evolutionary games. Jour-nal of Economic Dynamics and Control, 25(6-7):1019--1037, 2001.
[RM02] T.E. Revello and R. McCartney. Generating war game strategies using a genetic algorithm. In Proceedings of the 2002 Congress on Evolutionary Computation, volume 2, pages 1086--1091, 2002.
[RN04] M. Rocha and J. Neves. Preventing premature convergence to local op-tima in genetic algorithms via random offspring generation. Multiple Ap-proaches to Intelligent Systems, pages 127--136, 2004.
[SBM05] K.O. Stanley, B.D. Bryant, and R. Miikkulainen. Evolving neural network agents in the NERO video game. InProceedings of the IEEE symposium on computational intelligence and games, pages 182--189, 2005.
[SE98] Y. Shi and R. Eberhart. Parameter selection in particle swarm optimization.
In Evolutionary Programming VII, pages 591--600, 1998.
[TBS04] C. Thurau, C. Bauckhage, and G. Sagerer. Imitation learning at all levels of game-AI. In Proceedings of the international conference on computer games, artificial intelligence, design and education, pages 402--408, 2004.
[Thr95] S. Thrun. Learning to play the game of chess. Advances in Neural Infor-mation Processing Systems, pages 1069--1076, 1995.
[Ven02] G. Venter. Particle swarm optimization. In AIAA journal, 2002.
[Won08] K. Wong. Adaptive computer game system using artificial neural networks.
In Neural Information Processing, pages 675--682, 2008.