Optimal Pathfinding with A-Star Algorithms (eBook, ePUB)
Definitive Reference for Developers and Engineers
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"Optimal Pathfinding with A-Star Algorithms" "Optimal Pathfinding with A-Star Algorithms" delivers a comprehensive and rigorous exploration of modern pathfinding theory and practice, centering on the powerful A* family of search algorithms. The book systematically builds foundational knowledge, covering problem formulation, essential graph theory, complexity analysis, and the principles of both uninformed and informed search. Through clear explanations and detailed mathematical formalism, it lays the groundwork necessary for understanding what distinguishes A*-based approaches from other searc...
"Optimal Pathfinding with A-Star Algorithms"
"Optimal Pathfinding with A-Star Algorithms" delivers a comprehensive and rigorous exploration of modern pathfinding theory and practice, centering on the powerful A* family of search algorithms. The book systematically builds foundational knowledge, covering problem formulation, essential graph theory, complexity analysis, and the principles of both uninformed and informed search. Through clear explanations and detailed mathematical formalism, it lays the groundwork necessary for understanding what distinguishes A*-based approaches from other search strategies, making it a valuable resource for researchers, practitioners, and advanced students alike.
Moving far beyond the canonical A* algorithm, the text delves into advanced heuristic construction, algorithmic optimizations, and scalable deployment in distributed or resource-constrained settings. Readers are guided through topics such as memory-efficient heuristic design, dynamic replanning, hierarchical and parallel implementations, and the intricacies of multi-agent and multi-objective pathfinding. This breadth ensures relevance to a wide spectrum of real-world applications-from robotics and autonomous vehicles to intelligent game agents and logistics systems-while maintaining a deep focus on optimality, completeness, and computational efficiency.
In its final chapters, the book turns to pressing challenges at the forefront of the field, including security, robustness in adversarial environments, experimental benchmarking, and the integration of learning-based techniques. Emerging directions like quantum and probabilistic computing, energy-efficient planning, and human-in-the-loop systems are thoroughly surveyed, reflecting the ongoing evolution and multidisciplinary reach of A*-driven research. With its combination of theoretical rigor and practical insight, "Optimal Pathfinding with A-Star Algorithms" stands as an indispensable guide for those seeking mastery over the science and engineering of intelligent search.
"Optimal Pathfinding with A-Star Algorithms" delivers a comprehensive and rigorous exploration of modern pathfinding theory and practice, centering on the powerful A* family of search algorithms. The book systematically builds foundational knowledge, covering problem formulation, essential graph theory, complexity analysis, and the principles of both uninformed and informed search. Through clear explanations and detailed mathematical formalism, it lays the groundwork necessary for understanding what distinguishes A*-based approaches from other search strategies, making it a valuable resource for researchers, practitioners, and advanced students alike.
Moving far beyond the canonical A* algorithm, the text delves into advanced heuristic construction, algorithmic optimizations, and scalable deployment in distributed or resource-constrained settings. Readers are guided through topics such as memory-efficient heuristic design, dynamic replanning, hierarchical and parallel implementations, and the intricacies of multi-agent and multi-objective pathfinding. This breadth ensures relevance to a wide spectrum of real-world applications-from robotics and autonomous vehicles to intelligent game agents and logistics systems-while maintaining a deep focus on optimality, completeness, and computational efficiency.
In its final chapters, the book turns to pressing challenges at the forefront of the field, including security, robustness in adversarial environments, experimental benchmarking, and the integration of learning-based techniques. Emerging directions like quantum and probabilistic computing, energy-efficient planning, and human-in-the-loop systems are thoroughly surveyed, reflecting the ongoing evolution and multidisciplinary reach of A*-driven research. With its combination of theoretical rigor and practical insight, "Optimal Pathfinding with A-Star Algorithms" stands as an indispensable guide for those seeking mastery over the science and engineering of intelligent search.
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