Computer Science
Grade 11
20 min
Creating Interactive Games
Creating Interactive Games
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1
Introduction & Learning Objectives
Learning Objectives
Implement a Finite State Machine (FSM) to manage character or game states.
Apply the A* pathfinding algorithm to navigate an agent through a 2D grid with obstacles.
Design and implement a Quadtree to optimize collision detection in a 2D space.
By the end of a this lesson, students will be able to analyze the time complexity (Big O notation) of core game algorithms like collision detection and pathfinding.
Refactor a frame-rate dependent game loop to use delta time for consistent game logic speed.
Explain the trade-offs between different spatial partitioning techniques for performance optimization.
Ever wonder how thousands of units in a strategy game navigate complex terrain without crashing into each other or freezing your computer? 🤖 Let's dive in...
2
Key Concepts & Vocabulary
TermDefinitionExample
Finite State Machine (FSM)A computational model consisting of a finite number of states. In games, it's used to define the behavior of an entity (like an AI character) by transitioning between states (e.g., 'patrolling', 'chasing', 'attacking') based on specific inputs or conditions.An enemy guard's FSM: State 1: 'Patrol'. If player is spotted, transition to State 2: 'Chase'. If player is in range, transition to State 3: 'Attack'. If player is lost, transition back to State 1.
PathfindingThe process of finding the shortest or optimal route between two points, typically on a graph or grid, while avoiding obstacles. The A* (A-star) algorithm is a widely used pathfinding algorithm in games.In a tower...
3
Core Syntax & Patterns
A* (A-Star) Algorithm Formula
f(n) = g(n) + h(n)
Used to determine the priority of nodes to explore in a pathfinding search. 'n' is the current node. 'g(n)' is the actual cost of the path from the start node to 'n'. 'h(n)' is the heuristic (estimated) cost from 'n' to the goal node. The algorithm always explores the node with the lowest 'f(n)' score.
Game Loop with Delta Time
lastTime = now();
while(running) {
now = time();
deltaTime = now - lastTime;
update(deltaTime);
render();
lastTime = now;
}
This pattern ensures game logic is decoupled from the hardware's rendering speed. All updates involving time (like movement or animations) should be multiplied by `deltaTime` to ensure they are consistent ac...
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Challenging
Consider the A* formula `f(n) = g(n) + h(n)`. If the heuristic function `h(n)` was set to always return 0 for any node `n`, what algorithm would A* effectively become?
A.Breadth-First Search (BFS)
B.Dijkstra's Algorithm
C.Depth-First Search (DFS)
D.Greedy Best-First Search
Challenging
You are designing an FSM for an enemy AI. The requirements are: it should wander randomly ('Wandering'), chase the player if they get close ('Chasing'), attack if the player is within range ('Attacking'), and run away to heal if its health is low ('Fleeing'). Which state transition is the most critical to have the highest priority?
A.From 'Wandering' to 'Chasing' when the player is seen.
B.From 'Chasing' to 'Attacking' when the player is in range.
C.From any state ('Wandering', 'Chasing', 'Attacking') to 'Fleeing' when health is low.
D.From 'Attacking' to 'Chasing' when the player moves out of attack range.
Challenging
A game developer is choosing a spatial partitioning technique for a 2D game with many static obstacles but only a few moving characters. Which technique would likely be most efficient and why?
A.Quadtree, because it dynamically adapts to the distribution of all objects.
B.simple uniform grid, because the static objects can be pre-calculated into grid cells, making lookups for the few moving objects very fast.
C.No spatial partitioning, because with only a few moving characters, the overhead is not worth it.
D.Binary Space Partitioning (BSP) tree, as it is the most memory-efficient structure for static geometry.
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