Akademik Dersler - Tanıtım

Last Updated: 26/06/2026

Code - Course Title	Course Type	Laboratory + Practice	ECTS
OGP227 - Data Structures and Algorithms in Games	III. SEMESTER	2 +1	3.0

Language of Instruction	Türkçe
Course Type	Area Elective Courses
Course Instructor(s)	ÖĞR. GÖR. GÜVENÇ USANMAZ
Mode of Delivery	This course is delivered through face-to-face instruction. Theoretical lectures and laboratory sessions are integrated throughout the semester. Practical work is conducted by default with C#/Unity; the instructor may prefer an equivalent language/engine.
Place of Delivery
Used Educational Platforms
Prerequisites	There are no prerequisites or co-requisites for this course.
Courses Recomended	It is recommended to have completed OGP104 Programming II before taking this course. Having completed OGP106 Computer Graphics and XXX7960 Lightweight Game Frameworks may help situate algorithms in a game context.
Recommended Resources	Red Blob Games — Pathfinding for Games: https://www.redblobgames.com/pathfinding/a-star/introduction.htmlUnity. NavMesh and AI documentation: https://docs.unity3d.com/Manual/nav-BuildingNavMesh.html
Work Placement	This course has no internship or placement requirement.
Aims of the Course

Weeks	Learning Outcomes	Topics	Teaching Methods
Week - 1		DS&A in games: frame budget, Big-O, hot path; map of most common industry structures.
Week - 2		Object pool and command pattern; bullet/particle spawn, undo and command queue.
Week - 3		Inventory systems: grid inventory, slot bag, stacking; loot table and weighted random selection.
Week - 4		Event queue and ring buffer; input events, fixed timestep friendly structures.
Week - 5		Spatial partitioning: uniform grid, spatial hash, quadtree; broad-phase collision and culling.
Week - 6		Broad-phase / narrow-phase separation; which structure fits which game type.
Week - 7		Graph-based game systems: waypoint network, quest chain, map connectivity; directed/undirected graph.
Week - 8		MST and procedural map connectivity; dungeon/level connection graph.
Week - 9		Pathfinding I: grid A*, heuristic tuning, path smoothing, intro to JPS concept.
Week - 10		Pathfinding II: navmesh, flow field idea, steering behaviors (seek, flee, obstacle avoidance).
Week - 11		Game AI structures: FSM, behavior tree; action/turn selection with priority queue.
Week - 12		Decision algorithms: minimax/alpha-beta or utility scoring; influence map concept.
Week - 13		Unity NavMesh / profiling; choosing the right algorithm and performance measurement.
Week - 14		End-of-term two-module presentation; systems + pathfinding/AI integration and rationale analysis.

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Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
Toplam (%)