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AI-and-Pathfinding.-.md
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AI-and-Pathfinding.-.md
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# AI and Pathfinding
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Field of view (FOV), pathfinding, and AI systems for creating intelligent game entities.
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## Quick Reference
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**Systems:** [[Grid-System]], [[Entity-Management]]
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**Key Features:**
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- A* pathfinding (`grid.find_path()` returns `AStarPath` object)
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- Dijkstra maps (`grid.get_dijkstra_map()` returns `DijkstraMap` object)
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- Field of view (`grid.compute_fov()` / `grid.is_in_fov()`)
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- Per-entity perspective rendering
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**TCOD Integration:** `src/UIGrid.cpp` libtcod bindings
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---
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## Field of View (FOV)
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### Basic FOV
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```python
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import mcrfpy
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# Setup grid with transparency
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grid = mcrfpy.Grid(grid_size=(50, 50), pos=(0, 0), size=(400, 400))
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# Set tile properties (walkable + transparent)
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for x in range(50):
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for y in range(50):
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grid.at(x, y).transparent = True
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grid.at(x, y).walkable = True
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# Add walls (opaque and unwalkable)
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grid.at(10, 10).transparent = False
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grid.at(10, 10).walkable = False
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# Compute FOV from position with radius
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grid.compute_fov((25, 25), radius=10)
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# Query visibility
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if grid.is_in_fov((25, 25)):
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print("Origin is visible")
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if not grid.is_in_fov((0, 0)):
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print("Far corner not visible")
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```
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### Per-Entity Perspective
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Each entity can have its own view of the map:
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```python
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player = mcrfpy.Entity(grid_pos=(25, 25), sprite_index=0)
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grid.entities.append(player)
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# Set which entity's perspective to render
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grid.perspective = player
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# This affects rendering:
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# - Unexplored tiles: Black (never seen)
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# - Explored tiles: Dark (seen before, not visible now)
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# - Visible tiles: Normal (currently in FOV)
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# Clear perspective (show everything)
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grid.perspective = None
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```
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### FOV + Fog of War Pattern
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Use a ColorLayer to implement fog of war:
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```python
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# Create grid with fog layer
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grid = mcrfpy.Grid(grid_size=(50, 50), pos=(0, 0), size=(400, 400), layers=[])
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fog = mcrfpy.ColorLayer(name="fog", z_index=1)
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grid.add_layer(fog)
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# Start fully fogged
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fog.fill(mcrfpy.Color(0, 0, 0, 255))
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# Setup transparency
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for x in range(50):
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for y in range(50):
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grid.at(x, y).transparent = True
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grid.at(x, y).walkable = True
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player = mcrfpy.Entity(grid_pos=(25, 25), sprite_index=0)
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grid.entities.append(player)
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def update_fov():
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"""Recompute FOV and update fog layer."""
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px, py = int(player.grid_x), int(player.grid_y)
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grid.compute_fov((px, py), radius=10)
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for x in range(50):
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for y in range(50):
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if grid.is_in_fov((x, y)):
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fog.set((x, y), mcrfpy.Color(0, 0, 0, 0)) # Visible
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# Explored but not visible: keep at partial opacity
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# (track explored state separately if needed)
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# Initial FOV
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update_fov()
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# Update on movement
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def on_player_move(dx, dy):
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new_x = int(player.grid_x + dx)
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new_y = int(player.grid_y + dy)
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point = grid.at(new_x, new_y)
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if point and point.walkable:
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player.grid_x = new_x
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player.grid_y = new_y
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update_fov()
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```
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### Movement + FOV Input Handling
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```python
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scene = mcrfpy.Scene("game")
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scene.children.append(grid)
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mcrfpy.current_scene = scene
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move_map = {
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mcrfpy.Key.W: (0, -1),
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mcrfpy.Key.A: (-1, 0),
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mcrfpy.Key.S: (0, 1),
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mcrfpy.Key.D: (1, 0),
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}
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def handle_key(key, action):
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if action != mcrfpy.InputState.PRESSED:
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return
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if key in move_map:
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dx, dy = move_map[key]
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on_player_move(dx, dy)
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scene.on_key = handle_key
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```
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---
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## Pathfinding
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### A* Pathfinding
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`grid.find_path()` returns an `AStarPath` object:
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```python
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grid = mcrfpy.Grid(grid_size=(30, 30), pos=(0, 0), size=(400, 400))
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for x in range(30):
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for y in range(30):
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grid.at(x, y).walkable = True
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# Find path between two points
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path = grid.find_path((10, 10), (20, 20))
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if path and len(path) > 0:
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print(f"Path has {path.remaining} steps")
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print(f"From: ({path.origin.x}, {path.origin.y})")
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print(f"To: ({path.destination.x}, {path.destination.y})")
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# Walk the path step by step
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next_step = path.walk() # Consume and return next step
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print(f"Next: ({next_step.x}, {next_step.y})")
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# Peek without consuming
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upcoming = path.peek() # Look at next step without consuming
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```
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**AStarPath API:**
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| Method/Property | Description |
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|----------------|-------------|
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| `path.walk()` | Consume and return next step (Vector) |
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| `path.peek()` | Look at next step without consuming |
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| `path.remaining` | Number of steps left |
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| `len(path)` | Same as `remaining` |
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| `path.origin` | Start position (Vector) |
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| `path.destination` | End position (Vector) |
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### Dijkstra Maps
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Multi-source distance maps for efficient AI:
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```python
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# Create Dijkstra map from a single source
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dm = grid.get_dijkstra_map((15, 15))
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# Query distance from any cell to source
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d = dm.distance((0, 0))
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print(f"Distance from (0,0) to (15,15): {d}")
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# Get full path from a position to the source
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path_points = dm.path_from((0, 0))
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print(f"Path length: {len(path_points)}")
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# Get single next step toward source
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next_step = dm.step_from((0, 0))
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print(f"Next step: ({next_step.x}, {next_step.y})")
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```
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**DijkstraMap API:**
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| Method | Description |
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|--------|-------------|
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| `dm.distance((x, y))` | Distance from cell to source |
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| `dm.path_from((x, y))` | Full path from cell to source |
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| `dm.step_from((x, y))` | Single step toward source |
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| `dm.to_heightmap()` | Convert to HeightMap for visualization |
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---
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## AI Patterns
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### Pattern 1: Chase AI (Dijkstra)
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Enemies chase player using a shared Dijkstra map:
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```python
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def update_enemies(grid, player, enemies):
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# Compute Dijkstra map with player as source
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dm = grid.get_dijkstra_map((int(player.grid_x), int(player.grid_y)))
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for enemy in enemies:
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ex, ey = int(enemy.grid_x), int(enemy.grid_y)
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next_step = dm.step_from((ex, ey))
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if next_step:
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nx, ny = int(next_step.x), int(next_step.y)
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if grid.at(nx, ny).walkable:
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enemy.grid_x = nx
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enemy.grid_y = ny
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```
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**Advantage:** One Dijkstra map serves all enemies (O(n) compute, O(1) per enemy query).
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### Pattern 2: Flee AI (Inverted Dijkstra)
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Enemies flee from danger by moving to higher-distance cells:
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```python
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def flee_from_player(grid, player, scared_npcs):
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dm = grid.get_dijkstra_map((int(player.grid_x), int(player.grid_y)))
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for npc in scared_npcs:
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nx, ny = int(npc.grid_x), int(npc.grid_y)
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best_pos = None
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best_distance = -1
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for dx in [-1, 0, 1]:
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for dy in [-1, 0, 1]:
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if dx == 0 and dy == 0:
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continue
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cx, cy = nx + dx, ny + dy
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if grid.at(cx, cy).walkable:
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d = dm.distance((cx, cy))
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if d > best_distance:
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best_distance = d
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best_pos = (cx, cy)
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if best_pos:
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npc.grid_x = best_pos[0]
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npc.grid_y = best_pos[1]
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```
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### Pattern 3: Aggro Range with Spatial Queries
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Use `entities_in_radius()` for aggro detection:
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```python
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class Enemy:
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def __init__(self, grid, pos, aggro_range=10):
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self.entity = mcrfpy.Entity(grid_pos=pos, sprite_index=1, name="enemy")
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self.aggro_range = aggro_range
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grid.entities.append(self.entity)
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def update(self, grid):
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ex, ey = int(self.entity.grid_x), int(self.entity.grid_y)
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nearby = grid.entities_in_radius((ex, ey), self.aggro_range)
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# Filter for player entities
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for entity in nearby:
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if entity.name == "player":
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self.chase(grid, entity)
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return
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def chase(self, grid, target):
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path = grid.find_path(
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(int(self.entity.grid_x), int(self.entity.grid_y)),
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(int(target.grid_x), int(target.grid_y))
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)
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if path and len(path) > 0:
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step = path.walk()
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self.entity.grid_x = int(step.x)
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self.entity.grid_y = int(step.y)
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```
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### Pattern 4: State Machine AI
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Complex behaviors using states:
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```python
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class StateMachineAI:
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def __init__(self, grid, pos):
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self.entity = mcrfpy.Entity(grid_pos=pos, sprite_index=3, name="ai")
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self.state = "patrol"
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self.health = 100
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self.aggro_range = 10
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self.flee_threshold = 30
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grid.entities.append(self.entity)
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def update(self, grid, player):
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if self.state == "patrol":
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# Check for player in range
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nearby = grid.entities_in_radius(
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(int(self.entity.grid_x), int(self.entity.grid_y)),
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self.aggro_range
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)
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for e in nearby:
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if e.name == "player":
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self.state = "chase"
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return
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elif self.state == "chase":
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if self.health < self.flee_threshold:
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self.state = "flee"
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return
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# Chase with A*
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path = grid.find_path(
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(int(self.entity.grid_x), int(self.entity.grid_y)),
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(int(player.grid_x), int(player.grid_y))
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)
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if path and len(path) > 0:
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step = path.walk()
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self.entity.grid_x = int(step.x)
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self.entity.grid_y = int(step.y)
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elif self.state == "flee":
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dm = grid.get_dijkstra_map(
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(int(player.grid_x), int(player.grid_y))
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)
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# Move away from player (maximize distance)
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# ... (see Flee AI pattern above)
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```
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---
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## Performance Considerations
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### FOV: Only Recompute on Move
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```python
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# Don't recompute every frame
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def on_player_move(dx, dy):
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# ... move player ...
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grid.compute_fov((new_x, new_y), radius=10) # Only on move
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```
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### Pathfinding: Cache Dijkstra Maps
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```python
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# Compute once per turn, query many times
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dm = grid.get_dijkstra_map((int(player.grid_x), int(player.grid_y)))
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for enemy in enemies:
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step = dm.step_from((int(enemy.grid_x), int(enemy.grid_y)))
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# O(1) per enemy vs O(n log n) for individual A*
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```
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### A* vs Dijkstra Trade-offs
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| Method | Best For | Cost |
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|--------|----------|------|
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| `find_path()` (A*) | Single entity, single target | O(n log n) per call |
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| `get_dijkstra_map()` | Many entities, one target | O(n) compute, O(1) per query |
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---
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## Related Documentation
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- [[Grid-System]] - Grid fundamentals, cell properties
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- [[Entity-Management]] - Entity creation and movement
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- [[Animation-System]] - Smooth entity movement animations
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- [[Input-and-Events]] - Keyboard handler for movement
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---
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*Last updated: 2026-02-07*
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@ -1,466 +0,0 @@
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# AI and Pathfinding
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Field of view (FOV), pathfinding, and AI systems for creating intelligent game entities.
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## Quick Reference
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**Systems:** [[Grid-System]], [[Entity-Management]]
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**Key Features:**
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- A* pathfinding
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- Dijkstra maps
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- Field of view (FOV)
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- Per-entity perspective/knowledge
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**TCOD Integration:** `src/UIGrid.cpp` libtcod bindings
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---
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## Field of View (FOV)
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### Basic FOV
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```python
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import mcrfpy
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# Setup grid with transparency
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grid = mcrfpy.Grid(50, 50, 16, 16)
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# Set tile transparency (can entity see through this tile?)
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for x in range(50):
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for y in range(50):
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# Walls are opaque, floor is transparent
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is_wall = grid.at((x, y)).tilesprite == WALL_TILE
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grid.transparent((x, y), not is_wall)
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# Compute FOV from entity position
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player = mcrfpy.Entity(25, 25, 0)
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grid.compute_fov(player.x, player.y, radius=10)
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# FOV is now computed - use grid.perspective for rendering
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```
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**Implementation:** `src/UIGrid.cpp::compute_fov()` - Uses libtcod's FOV algorithm
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### Per-Entity Perspective
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Each entity can have its own knowledge of the map:
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```python
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# Set which entity's perspective to render
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grid.perspective = player
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# This affects rendering:
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# - Unexplored tiles: Black (never seen)
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# - Explored tiles: Dark (seen before, not visible now)
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# - Visible tiles: Normal (currently in FOV)
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```
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**Three rendering states:**
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1. **Unexplored** - Never seen by this entity
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2. **Explored** - Seen before, not currently visible
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3. **Visible** - Currently in field of view
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**Implementation:**
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- `src/UIGrid.cpp::perspective` property
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- `src/UIGridPointState.h` - Per-entity tile knowledge
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### FOV Example: Fog of War
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```python
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import mcrfpy
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# Create game
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mcrfpy.createScene("game")
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grid = mcrfpy.Grid(50, 50, 16, 16)
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grid.texture = mcrfpy.createTexture("tiles.png")
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# Create player
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player = mcrfpy.Entity(25, 25, 0)
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grid.entities.append(player)
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# Set grid perspective to player
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grid.perspective = player
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# Update FOV when player moves
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def on_player_move(dx, dy):
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new_x = player.x + dx
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new_y = player.y + dy
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if grid.walkable((new_x, new_y)):
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player.pos = (new_x, new_y)
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# Recompute FOV from new position
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grid.compute_fov(player.x, player.y, radius=10)
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# Input handling
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def on_keypress(key, pressed):
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if pressed:
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if key == mcrfpy.Key.Up:
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on_player_move(0, -1)
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elif key == mcrfpy.Key.Down:
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on_player_move(0, 1)
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# ... etc
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```
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---
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## Pathfinding
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### A* Pathfinding
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Finds shortest path from entity to target:
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```python
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# Entity pathfinding
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player = mcrfpy.Entity(10, 10, 0)
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||||
grid.entities.append(player)
|
||||
|
||||
# Find path to target position
|
||||
path = player.path_to(30, 25)
|
||||
|
||||
# path is list of (x, y) tuples
|
||||
if path:
|
||||
print(f"Path length: {len(path)}")
|
||||
for x, y in path:
|
||||
print(f" Step: ({x}, {y})")
|
||||
|
||||
# Move along path
|
||||
next_step = path[0]
|
||||
player.pos = next_step
|
||||
```
|
||||
|
||||
**Caching:** Paths are automatically cached in entity for performance
|
||||
|
||||
**Implementation:**
|
||||
- `src/UIEntity.cpp::path_to()` - A* pathfinding
|
||||
- Uses libtcod's A* implementation
|
||||
- Respects grid walkability
|
||||
|
||||
### Dijkstra Maps
|
||||
|
||||
Multi-target pathfinding for AI:
|
||||
|
||||
```python
|
||||
# Create Dijkstra map with multiple goal points
|
||||
goals = [(10, 10), (40, 40), (25, 5)]
|
||||
grid.compute_dijkstra(goals)
|
||||
|
||||
# Get distance from any position to nearest goal
|
||||
distance = grid.get_dijkstra_distance(entity.x, entity.y)
|
||||
|
||||
# Get path from position to nearest goal
|
||||
path = grid.get_dijkstra_path(entity.x, entity.y, max_length=20)
|
||||
```
|
||||
|
||||
**Use cases:**
|
||||
- **Chase AI:** Dijkstra map with player as goal
|
||||
- **Flee AI:** Inverted Dijkstra map (high values = safe)
|
||||
- **Multi-target:** Pathfind to any of several objectives
|
||||
|
||||
**Implementation:** `src/UIGrid.cpp` - Dijkstra map functions
|
||||
|
||||
### Pathfinding Example: Smart Enemy AI
|
||||
|
||||
```python
|
||||
import mcrfpy
|
||||
|
||||
class SmartEnemy:
|
||||
def __init__(self, x, y):
|
||||
self.entity = mcrfpy.Entity(x, y, 1)
|
||||
self.state = "idle"
|
||||
self.aggro_range = 15
|
||||
self.last_player_pos = None
|
||||
|
||||
def update(self, player, grid):
|
||||
# Calculate distance to player
|
||||
dx = player.x - self.entity.x
|
||||
dy = player.y - self.entity.y
|
||||
distance = (dx*dx + dy*dy) ** 0.5
|
||||
|
||||
if distance < self.aggro_range:
|
||||
# Can we see the player?
|
||||
if self.can_see(player, grid):
|
||||
# Use pathfinding to chase
|
||||
self.chase(player)
|
||||
self.last_player_pos = (player.x, player.y)
|
||||
elif self.last_player_pos:
|
||||
# Move to last known position
|
||||
self.investigate(self.last_player_pos)
|
||||
else:
|
||||
# Lost player, go back to idle
|
||||
self.state = "idle"
|
||||
else:
|
||||
self.state = "idle"
|
||||
|
||||
def can_see(self, player, grid):
|
||||
# Check if entity can see player using FOV
|
||||
grid.compute_fov(self.entity.x, self.entity.y, radius=self.aggro_range)
|
||||
# In real implementation, check if player's tile is visible
|
||||
# For now, simple line-of-sight check
|
||||
return True # Simplified
|
||||
|
||||
def chase(self, player):
|
||||
# Use A* pathfinding
|
||||
path = self.entity.path_to(player.x, player.y)
|
||||
if path and len(path) > 0:
|
||||
next_step = path[0]
|
||||
self.entity.pos = next_step
|
||||
|
||||
def investigate(self, target_pos):
|
||||
# Move to last known position
|
||||
if (self.entity.x, self.entity.y) == target_pos:
|
||||
self.last_player_pos = None
|
||||
self.state = "idle"
|
||||
else:
|
||||
path = self.entity.path_to(*target_pos)
|
||||
if path and len(path) > 0:
|
||||
next_step = path[0]
|
||||
self.entity.pos = next_step
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## AI Patterns
|
||||
|
||||
### Pattern 1: Chase AI (Dijkstra)
|
||||
|
||||
Enemies chase player using Dijkstra map:
|
||||
|
||||
```python
|
||||
def update_enemies(grid, player, enemies):
|
||||
# Compute Dijkstra map with player as goal
|
||||
grid.compute_dijkstra([(player.x, player.y)])
|
||||
|
||||
for enemy in enemies:
|
||||
# Get path toward player
|
||||
path = grid.get_dijkstra_path(enemy.x, enemy.y, max_length=1)
|
||||
if path and len(path) > 0:
|
||||
next_x, next_y = path[0]
|
||||
if grid.walkable((next_x, next_y)):
|
||||
enemy.pos = (next_x, next_y)
|
||||
```
|
||||
|
||||
**Advantage:** Computes paths for all enemies at once - more efficient than individual A*
|
||||
|
||||
### Pattern 2: Flee AI (Inverted Dijkstra)
|
||||
|
||||
Enemies flee from danger:
|
||||
|
||||
```python
|
||||
def flee_from_player(grid, player, scared_npcs):
|
||||
# Compute danger map (player = maximum danger)
|
||||
grid.compute_dijkstra([(player.x, player.y)])
|
||||
|
||||
for npc in scared_npcs:
|
||||
# Find tile furthest from player
|
||||
best_pos = None
|
||||
best_distance = -1
|
||||
|
||||
for dx in [-1, 0, 1]:
|
||||
for dy in [-1, 0, 1]:
|
||||
if dx == 0 and dy == 0:
|
||||
continue
|
||||
|
||||
check_x = npc.x + dx
|
||||
check_y = npc.y + dy
|
||||
|
||||
if grid.walkable((check_x, check_y)):
|
||||
distance = grid.get_dijkstra_distance(check_x, check_y)
|
||||
if distance > best_distance:
|
||||
best_distance = distance
|
||||
best_pos = (check_x, check_y)
|
||||
|
||||
if best_pos:
|
||||
npc.pos = best_pos
|
||||
```
|
||||
|
||||
### Pattern 3: Guard AI (Patrol Routes)
|
||||
|
||||
Entities patrol between waypoints:
|
||||
|
||||
```python
|
||||
class Guard:
|
||||
def __init__(self, x, y, waypoints):
|
||||
self.entity = mcrfpy.Entity(x, y, 2)
|
||||
self.waypoints = waypoints
|
||||
self.current_waypoint = 0
|
||||
self.wait_time = 0
|
||||
|
||||
def update(self, grid):
|
||||
if self.wait_time > 0:
|
||||
self.wait_time -= 1
|
||||
return
|
||||
|
||||
# Get current target waypoint
|
||||
target = self.waypoints[self.current_waypoint]
|
||||
|
||||
# Are we there yet?
|
||||
if (self.entity.x, self.entity.y) == target:
|
||||
# Wait at waypoint
|
||||
self.wait_time = 30 # Wait 30 ticks
|
||||
# Move to next waypoint
|
||||
self.current_waypoint = (self.current_waypoint + 1) % len(self.waypoints)
|
||||
else:
|
||||
# Pathfind to waypoint
|
||||
path = self.entity.path_to(*target)
|
||||
if path and len(path) > 0:
|
||||
next_step = path[0]
|
||||
self.entity.pos = next_step
|
||||
|
||||
# Create guard with patrol route
|
||||
guard = Guard(10, 10, [(10, 10), (30, 10), (30, 30), (10, 30)])
|
||||
```
|
||||
|
||||
### Pattern 4: State Machine AI
|
||||
|
||||
Complex behaviors using states:
|
||||
|
||||
```python
|
||||
class ComplexAI:
|
||||
def __init__(self, x, y):
|
||||
self.entity = mcrfpy.Entity(x, y, 3)
|
||||
self.state = "patrol"
|
||||
self.health = 100
|
||||
self.aggro_range = 10
|
||||
self.flee_threshold = 30
|
||||
|
||||
def update(self, player, grid):
|
||||
if self.state == "patrol":
|
||||
self.do_patrol(grid)
|
||||
# Check for player
|
||||
if self.distance_to(player) < self.aggro_range:
|
||||
self.state = "chase"
|
||||
|
||||
elif self.state == "chase":
|
||||
self.do_chase(player)
|
||||
# Check health
|
||||
if self.health < self.flee_threshold:
|
||||
self.state = "flee"
|
||||
|
||||
elif self.state == "flee":
|
||||
self.do_flee(player, grid)
|
||||
# Check if safe
|
||||
if self.distance_to(player) > self.aggro_range * 2:
|
||||
self.state = "patrol"
|
||||
|
||||
def distance_to(self, other_entity):
|
||||
dx = self.entity.x - other_entity.x
|
||||
dy = self.entity.y - other_entity.y
|
||||
return (dx*dx + dy*dy) ** 0.5
|
||||
|
||||
def do_patrol(self, grid):
|
||||
# Patrol logic
|
||||
pass
|
||||
|
||||
def do_chase(self, player):
|
||||
# Chase with pathfinding
|
||||
path = self.entity.path_to(player.x, player.y)
|
||||
if path and len(path) > 0:
|
||||
self.entity.pos = path[0]
|
||||
|
||||
def do_flee(self, player, grid):
|
||||
# Flee using inverted Dijkstra
|
||||
# (Implementation similar to flee AI above)
|
||||
pass
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
### FOV Performance
|
||||
|
||||
**Cost:** O(cells in radius)
|
||||
- 10 tile radius: ~314 cells to check
|
||||
- 20 tile radius: ~1256 cells to check
|
||||
|
||||
**Optimization:** Only recompute when needed
|
||||
```python
|
||||
# Don't recompute every frame!
|
||||
def on_player_move():
|
||||
player.pos = new_pos
|
||||
grid.compute_fov(player.x, player.y, radius=10) # Only on move
|
||||
```
|
||||
|
||||
### Pathfinding Performance
|
||||
|
||||
**A* Cost:** O(n log n) where n = path length
|
||||
- Short paths (< 20 tiles): < 1ms
|
||||
- Long paths (> 100 tiles): Can be 5-10ms
|
||||
|
||||
**Optimization:** Use path caching
|
||||
```python
|
||||
class CachedPathfinder:
|
||||
def __init__(self, entity):
|
||||
self.entity = entity
|
||||
self.cached_path = None
|
||||
self.cached_target = None
|
||||
|
||||
def path_to(self, target_x, target_y):
|
||||
# Check if we can reuse cached path
|
||||
if self.cached_target == (target_x, target_y) and self.cached_path:
|
||||
# Remove first step if we've reached it
|
||||
if self.cached_path and self.cached_path[0] == (self.entity.x, self.entity.y):
|
||||
self.cached_path = self.cached_path[1:]
|
||||
return self.cached_path
|
||||
|
||||
# Compute new path
|
||||
self.cached_path = self.entity.path_to(target_x, target_y)
|
||||
self.cached_target = (target_x, target_y)
|
||||
return self.cached_path
|
||||
```
|
||||
|
||||
### Dijkstra Performance
|
||||
|
||||
**Cost:** O(n) where n = number of cells
|
||||
- 50x50 grid: 2500 cells
|
||||
- 100x100 grid: 10000 cells
|
||||
|
||||
**Best practice:** Compute once, query many times
|
||||
```python
|
||||
# Compute Dijkstra map once
|
||||
grid.compute_dijkstra([(player.x, player.y)])
|
||||
|
||||
# Use for all enemies (amortized O(1) per enemy)
|
||||
for enemy in enemies:
|
||||
path = grid.get_dijkstra_path(enemy.x, enemy.y, max_length=1)
|
||||
# Move enemy...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Integration with Other Systems
|
||||
|
||||
### With Grid System
|
||||
|
||||
See [[Grid-System]] for:
|
||||
- Setting walkability: `grid.walkable((x, y), True)`
|
||||
- Setting transparency: `grid.transparent((x, y), True)`
|
||||
- Grid perspective rendering
|
||||
|
||||
### With Entity Management
|
||||
|
||||
See [[Entity-Management]] for:
|
||||
- Creating entities
|
||||
- Moving entities along paths
|
||||
- Entity lifecycle
|
||||
|
||||
### With Animation
|
||||
|
||||
See [[Animation-System]] for:
|
||||
- Smoothly animating entity movement
|
||||
- Visual feedback for AI state changes
|
||||
|
||||
---
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- [[Grid-System]] - Grid fundamentals, TCOD integration
|
||||
- [[Entity-Management]] - Entity creation and movement
|
||||
- `src/UIGrid.cpp` - FOV and pathfinding implementation
|
||||
- Tutorial Part 6+ - AI and pathfinding examples
|
||||
|
||||
**Open Issues:**
|
||||
- [#64](../../issues/64) - Grid-Entity-GridPointState TCOD Updates
|
||||
- [#115](../../issues/115) - SpatialHash (improves AI spatial queries)
|
||||
Loading…
Add table
Add a link
Reference in a new issue