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John McCardle 2026-02-07 22:23:25 +00:00
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# AI and Pathfinding
Field of view (FOV), pathfinding, and AI systems for creating intelligent game entities.
## Quick Reference
**Systems:** [[Grid-System]], [[Entity-Management]]
**Key Features:**
- A* pathfinding (`grid.find_path()` returns `AStarPath` object)
- Dijkstra maps (`grid.get_dijkstra_map()` returns `DijkstraMap` object)
- Field of view (`grid.compute_fov()` / `grid.is_in_fov()`)
- Per-entity perspective rendering
**TCOD Integration:** `src/UIGrid.cpp` libtcod bindings
---
## Field of View (FOV)
### Basic FOV
```python
import mcrfpy
# Setup grid with transparency
grid = mcrfpy.Grid(grid_size=(50, 50), pos=(0, 0), size=(400, 400))
# Set tile properties (walkable + transparent)
for x in range(50):
for y in range(50):
grid.at(x, y).transparent = True
grid.at(x, y).walkable = True
# Add walls (opaque and unwalkable)
grid.at(10, 10).transparent = False
grid.at(10, 10).walkable = False
# Compute FOV from position with radius
grid.compute_fov((25, 25), radius=10)
# Query visibility
if grid.is_in_fov((25, 25)):
print("Origin is visible")
if not grid.is_in_fov((0, 0)):
print("Far corner not visible")
```
### Per-Entity Perspective
Each entity can have its own view of the map:
```python
player = mcrfpy.Entity(grid_pos=(25, 25), sprite_index=0)
grid.entities.append(player)
# Set which entity's perspective to render
grid.perspective = player
# This affects rendering:
# - Unexplored tiles: Black (never seen)
# - Explored tiles: Dark (seen before, not visible now)
# - Visible tiles: Normal (currently in FOV)
# Clear perspective (show everything)
grid.perspective = None
```
### FOV + Fog of War Pattern
Use a ColorLayer to implement fog of war:
```python
# Create grid with fog layer
grid = mcrfpy.Grid(grid_size=(50, 50), pos=(0, 0), size=(400, 400), layers=[])
fog = mcrfpy.ColorLayer(name="fog", z_index=1)
grid.add_layer(fog)
# Start fully fogged
fog.fill(mcrfpy.Color(0, 0, 0, 255))
# Setup transparency
for x in range(50):
for y in range(50):
grid.at(x, y).transparent = True
grid.at(x, y).walkable = True
player = mcrfpy.Entity(grid_pos=(25, 25), sprite_index=0)
grid.entities.append(player)
def update_fov():
"""Recompute FOV and update fog layer."""
px, py = int(player.grid_x), int(player.grid_y)
grid.compute_fov((px, py), radius=10)
for x in range(50):
for y in range(50):
if grid.is_in_fov((x, y)):
fog.set((x, y), mcrfpy.Color(0, 0, 0, 0)) # Visible
# Explored but not visible: keep at partial opacity
# (track explored state separately if needed)
# Initial FOV
update_fov()
# Update on movement
def on_player_move(dx, dy):
new_x = int(player.grid_x + dx)
new_y = int(player.grid_y + dy)
point = grid.at(new_x, new_y)
if point and point.walkable:
player.grid_x = new_x
player.grid_y = new_y
update_fov()
```
### Movement + FOV Input Handling
```python
scene = mcrfpy.Scene("game")
scene.children.append(grid)
mcrfpy.current_scene = scene
move_map = {
mcrfpy.Key.W: (0, -1),
mcrfpy.Key.A: (-1, 0),
mcrfpy.Key.S: (0, 1),
mcrfpy.Key.D: (1, 0),
}
def handle_key(key, action):
if action != mcrfpy.InputState.PRESSED:
return
if key in move_map:
dx, dy = move_map[key]
on_player_move(dx, dy)
scene.on_key = handle_key
```
---
## Pathfinding
### A* Pathfinding
`grid.find_path()` returns an `AStarPath` object:
```python
grid = mcrfpy.Grid(grid_size=(30, 30), pos=(0, 0), size=(400, 400))
for x in range(30):
for y in range(30):
grid.at(x, y).walkable = True
# Find path between two points
path = grid.find_path((10, 10), (20, 20))
if path and len(path) > 0:
print(f"Path has {path.remaining} steps")
print(f"From: ({path.origin.x}, {path.origin.y})")
print(f"To: ({path.destination.x}, {path.destination.y})")
# Walk the path step by step
next_step = path.walk() # Consume and return next step
print(f"Next: ({next_step.x}, {next_step.y})")
# Peek without consuming
upcoming = path.peek() # Look at next step without consuming
```
**AStarPath API:**
| Method/Property | Description |
|----------------|-------------|
| `path.walk()` | Consume and return next step (Vector) |
| `path.peek()` | Look at next step without consuming |
| `path.remaining` | Number of steps left |
| `len(path)` | Same as `remaining` |
| `path.origin` | Start position (Vector) |
| `path.destination` | End position (Vector) |
### Dijkstra Maps
Multi-source distance maps for efficient AI:
```python
# Create Dijkstra map from a single source
dm = grid.get_dijkstra_map((15, 15))
# Query distance from any cell to source
d = dm.distance((0, 0))
print(f"Distance from (0,0) to (15,15): {d}")
# Get full path from a position to the source
path_points = dm.path_from((0, 0))
print(f"Path length: {len(path_points)}")
# Get single next step toward source
next_step = dm.step_from((0, 0))
print(f"Next step: ({next_step.x}, {next_step.y})")
```
**DijkstraMap API:**
| Method | Description |
|--------|-------------|
| `dm.distance((x, y))` | Distance from cell to source |
| `dm.path_from((x, y))` | Full path from cell to source |
| `dm.step_from((x, y))` | Single step toward source |
| `dm.to_heightmap()` | Convert to HeightMap for visualization |
---
## AI Patterns
### Pattern 1: Chase AI (Dijkstra)
Enemies chase player using a shared Dijkstra map:
```python
def update_enemies(grid, player, enemies):
# Compute Dijkstra map with player as source
dm = grid.get_dijkstra_map((int(player.grid_x), int(player.grid_y)))
for enemy in enemies:
ex, ey = int(enemy.grid_x), int(enemy.grid_y)
next_step = dm.step_from((ex, ey))
if next_step:
nx, ny = int(next_step.x), int(next_step.y)
if grid.at(nx, ny).walkable:
enemy.grid_x = nx
enemy.grid_y = ny
```
**Advantage:** One Dijkstra map serves all enemies (O(n) compute, O(1) per enemy query).
### Pattern 2: Flee AI (Inverted Dijkstra)
Enemies flee from danger by moving to higher-distance cells:
```python
def flee_from_player(grid, player, scared_npcs):
dm = grid.get_dijkstra_map((int(player.grid_x), int(player.grid_y)))
for npc in scared_npcs:
nx, ny = int(npc.grid_x), int(npc.grid_y)
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
cx, cy = nx + dx, ny + dy
if grid.at(cx, cy).walkable:
d = dm.distance((cx, cy))
if d > best_distance:
best_distance = d
best_pos = (cx, cy)
if best_pos:
npc.grid_x = best_pos[0]
npc.grid_y = best_pos[1]
```
### Pattern 3: Aggro Range with Spatial Queries
Use `entities_in_radius()` for aggro detection:
```python
class Enemy:
def __init__(self, grid, pos, aggro_range=10):
self.entity = mcrfpy.Entity(grid_pos=pos, sprite_index=1, name="enemy")
self.aggro_range = aggro_range
grid.entities.append(self.entity)
def update(self, grid):
ex, ey = int(self.entity.grid_x), int(self.entity.grid_y)
nearby = grid.entities_in_radius((ex, ey), self.aggro_range)
# Filter for player entities
for entity in nearby:
if entity.name == "player":
self.chase(grid, entity)
return
def chase(self, grid, target):
path = grid.find_path(
(int(self.entity.grid_x), int(self.entity.grid_y)),
(int(target.grid_x), int(target.grid_y))
)
if path and len(path) > 0:
step = path.walk()
self.entity.grid_x = int(step.x)
self.entity.grid_y = int(step.y)
```
### Pattern 4: State Machine AI
Complex behaviors using states:
```python
class StateMachineAI:
def __init__(self, grid, pos):
self.entity = mcrfpy.Entity(grid_pos=pos, sprite_index=3, name="ai")
self.state = "patrol"
self.health = 100
self.aggro_range = 10
self.flee_threshold = 30
grid.entities.append(self.entity)
def update(self, grid, player):
if self.state == "patrol":
# Check for player in range
nearby = grid.entities_in_radius(
(int(self.entity.grid_x), int(self.entity.grid_y)),
self.aggro_range
)
for e in nearby:
if e.name == "player":
self.state = "chase"
return
elif self.state == "chase":
if self.health < self.flee_threshold:
self.state = "flee"
return
# Chase with A*
path = grid.find_path(
(int(self.entity.grid_x), int(self.entity.grid_y)),
(int(player.grid_x), int(player.grid_y))
)
if path and len(path) > 0:
step = path.walk()
self.entity.grid_x = int(step.x)
self.entity.grid_y = int(step.y)
elif self.state == "flee":
dm = grid.get_dijkstra_map(
(int(player.grid_x), int(player.grid_y))
)
# Move away from player (maximize distance)
# ... (see Flee AI pattern above)
```
---
## Performance Considerations
### FOV: Only Recompute on Move
```python
# Don't recompute every frame
def on_player_move(dx, dy):
# ... move player ...
grid.compute_fov((new_x, new_y), radius=10) # Only on move
```
### Pathfinding: Cache Dijkstra Maps
```python
# Compute once per turn, query many times
dm = grid.get_dijkstra_map((int(player.grid_x), int(player.grid_y)))
for enemy in enemies:
step = dm.step_from((int(enemy.grid_x), int(enemy.grid_y)))
# O(1) per enemy vs O(n log n) for individual A*
```
### A* vs Dijkstra Trade-offs
| Method | Best For | Cost |
|--------|----------|------|
| `find_path()` (A*) | Single entity, single target | O(n log n) per call |
| `get_dijkstra_map()` | Many entities, one target | O(n) compute, O(1) per query |
---
## Related Documentation
- [[Grid-System]] - Grid fundamentals, cell properties
- [[Entity-Management]] - Entity creation and movement
- [[Animation-System]] - Smooth entity movement animations
- [[Input-and-Events]] - Keyboard handler for movement
---
*Last updated: 2026-02-07*

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# AI and Pathfinding
Field of view (FOV), pathfinding, and AI systems for creating intelligent game entities.
## Quick Reference
**Systems:** [[Grid-System]], [[Entity-Management]]
**Key Features:**
- A* pathfinding
- Dijkstra maps
- Field of view (FOV)
- Per-entity perspective/knowledge
**TCOD Integration:** `src/UIGrid.cpp` libtcod bindings
---
## Field of View (FOV)
### Basic FOV
```python
import mcrfpy
# Setup grid with transparency
grid = mcrfpy.Grid(50, 50, 16, 16)
# Set tile transparency (can entity see through this tile?)
for x in range(50):
for y in range(50):
# Walls are opaque, floor is transparent
is_wall = grid.at((x, y)).tilesprite == WALL_TILE
grid.transparent((x, y), not is_wall)
# Compute FOV from entity position
player = mcrfpy.Entity(25, 25, 0)
grid.compute_fov(player.x, player.y, radius=10)
# FOV is now computed - use grid.perspective for rendering
```
**Implementation:** `src/UIGrid.cpp::compute_fov()` - Uses libtcod's FOV algorithm
### Per-Entity Perspective
Each entity can have its own knowledge of the map:
```python
# Set which entity's perspective to render
grid.perspective = player
# This affects rendering:
# - Unexplored tiles: Black (never seen)
# - Explored tiles: Dark (seen before, not visible now)
# - Visible tiles: Normal (currently in FOV)
```
**Three rendering states:**
1. **Unexplored** - Never seen by this entity
2. **Explored** - Seen before, not currently visible
3. **Visible** - Currently in field of view
**Implementation:**
- `src/UIGrid.cpp::perspective` property
- `src/UIGridPointState.h` - Per-entity tile knowledge
### FOV Example: Fog of War
```python
import mcrfpy
# Create game
mcrfpy.createScene("game")
grid = mcrfpy.Grid(50, 50, 16, 16)
grid.texture = mcrfpy.createTexture("tiles.png")
# Create player
player = mcrfpy.Entity(25, 25, 0)
grid.entities.append(player)
# Set grid perspective to player
grid.perspective = player
# Update FOV when player moves
def on_player_move(dx, dy):
new_x = player.x + dx
new_y = player.y + dy
if grid.walkable((new_x, new_y)):
player.pos = (new_x, new_y)
# Recompute FOV from new position
grid.compute_fov(player.x, player.y, radius=10)
# Input handling
def on_keypress(key, pressed):
if pressed:
if key == mcrfpy.Key.Up:
on_player_move(0, -1)
elif key == mcrfpy.Key.Down:
on_player_move(0, 1)
# ... etc
```
---
## Pathfinding
### A* Pathfinding
Finds shortest path from entity to target:
```python
# Entity pathfinding
player = mcrfpy.Entity(10, 10, 0)
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)