SKU/Artículo: AMZ-B0DHVS9JZQ
Comprehensive Game AI Development Handbook: Creating Intelligent Games with Machine Learning (Mastering Machine Learning)
Format:
Hardcover
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.70 kg
0.70 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Key Features - Access expert insights into integrating AI within popular game engines. - Discover optimization techniques that enhance AI performance while reducing computational costs. - Explore the intersection of machine learning and traditional AI strategies for dynamic game experiences. - Real-world Python code examples accompanying every chapter to facilitate hands-on learning. Book Description Dive deep into the practical and theoretical aspects of AI as it revolutionizes the gaming industry. From advanced pathfinding algorithms and behavior trees to reinforcement learning and sentiment analysis, this handbook lays out an extensive range of topics crucial for developing smart and engaging games. Engage with a range of AI techniques, each elucidated through precise mathematical formulations and applied with Python implementations. Whether you're new to AI or a seasoned developer, this volume equips you with the knowledge and skills to craft compelling AI-driven gaming experiences. What You Will Learn - Master advanced pathfinding algorithms to optimize AI navigation. - Implement hierarchical finite state machines for complex character behavior management. - Design sophisticated AI decision-making processes with advanced behavior trees. - Utilize utility AI systems to model adaptive behaviors with utility-based calculations. - Apply reinforcement learning techniques to build autonomous gaming agents. - Explore Deep Q-Networks for AI strategy optimization. - Harness policy gradient methods to develop AI that learns strategies directly. - Delve into advanced neural network architectures for enhanced AI performance. - Process spatial data in games using convolutional neural networks. - Analyze sequential data with recurrent neural networks. - Utilize LSTM networks for effective sequence predictions. - Generate game assets through adversarial training with GANs. - Apply variational autoencoders to explore creative content generation. - Implement genetic algorithms for evolving AI behaviors. - Merge neural networks with evolutionary algorithms in neuroevolution. - Optimize AI behaviors with particle swarm techniques. - Model uncertainty in AI decisions using fuzzy logic systems. - Facilitate reasoning under uncertainty through Bayesian networks. - Model stochastic decision-making with Markov Decision Processes. - Tackle incomplete information scenarios with Partially Observable MDPs. - Employ Monte Carlo tree search for strategic AI planning. - Enhance adversarial AI with alpha-beta pruning in minimax algorithms. - Navigate multiplayer scenarios with game theory models. - Coordinate multi-agent AI systems for collective task completion. - Replicate natural swarm behaviors for collaborative AI strategies. - Implement emotion and personality modeling in non-player characters. - Develop compelling dialogue systems with natural language processing. - Integration of deep learning for intelligent in-game chatbots. - Use sentiment analysis to interpret player interactions. - Recognize player emotions via diverse input modalities for adaptive responses. - Alter game graphics dynamically with neural style transfer techniques. - Develop AI perception capabilities with computer vision algorithms. - Create seamless AI-Physics engine interactions for realism in game worlds. - Optimize AI computations using state-of-the-art hardware acceleration.
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AR$218.452
44% OFF
AR$121.360
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