Castle Anthrax: Dungeon Generation Techniques by James King

Transcript

1. James King @agentultra james@agentultra.com
2. Dungeon Generation Techniques
3. http://goo.gl/INm3W9
4. [[0 for x in range(5)] for y in range(5)]
5. [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
6. [0 for _ in range(5 * 5)]
7. [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
8. Row Major Order offset = (row * num_cols) + column
9. g = Grid(5, 5) g[1, 2] = 3 g[1, 2] # 3
10. Mazes
12. def generate_maze(): grid = Grid(MAZE_ROWS, MAZE_COLS, value=default_cell) stack = [] current_cell = random.choice(grid.coordinates) grid[current_cell]['visited'] = True stack.append(current_cell) ! while stack: ns = unvisited_neighbours(grid, current_cell) if ns: n = random.choice(ns) remove_wall_between(current_cell, n, grid) stack.append(current_cell) current_cell = n grid[current_cell]['visited'] = True else: current_cell = stack.pop() ! return grid
14. def generate_maze(): grid = Grid(MAZE_ROWS, MAZE_COLS, default_cell) frontier = [] start_cell = random.choice(grid.coordinates) grid[start_cell]['set'] = INTERIOR ns = neighbours(grid, start_cell, is_unvisited) for n in ns: grid[n]['set'] = FRONTIER frontier.extend(ns) ! while frontier: frontier_cell = frontier.pop(random.randrange(len(frontier))) interior_cell = random.choice( neighbours(grid, frontier_cell, is_interior)) remove_wall_between(interior_cell, frontier_cell, grid) grid[frontier_cell]['set'] = INTERIOR new_frontier_cells = neighbours(grid, frontier_cell, is_unvisited) for nfc in new_frontier_cells: grid[nfc]['set'] = FRONTIER frontier.append(nfc) ! return grid
15. http://goo.gl/teGL8D
16. http://goo.gl/vZ0EH1
24. • Poisson Disks • Cellular Automata • Perlin Noise
26. class Variable(object): ! def __init__(self, values): self.values = set(values) ! ...
27. class Variable(object): ! ... ! def narrow(self, values): new_values = self.values & values if not new_values: return False ! if self.values != new_values: if self._last_save_frame != self._current_save_frame: self._save(new_values) ! for constraint in self.constraints: constraint.propagate(self)
28. import abc ! ! class Constraint(object): __metaclass__ = abc.ABCMeta ! @abc.abstractproperty def variables(self): """ The variables involved in the constraint.""" ! @abc.abstractmethod def propagate(self, variable): """ Propagate the values in `variable` to each of the variables involved in this constraint. """
29. # from a hypothetical InequalityConstraint … def propagate(self, variable): if variable.is_unique: for v in self.variables: v.narrow(v.values - variable.values)
30. def solve(): if all(v.is_unique for v in variables): return (v.values[0] for v in variables) ! # some undo-stack management code here... ! variable = random.choice(v for v in variables if v.values > 1) for value in variable.values: if not variable.narrow(value) return ! if not any(v.is_empty for v in variables): if not solve(): return ! # more undo stack management
31. Optimize! • Represent sets as bitmasks • Undo-stack • Use as few variables as possible stay in the discrete/finite domain as much as possible
32. Thanks!
33. • Rogue Basin http://goo.gl/UTlwFU • How to Build a Contraint Propagator in a Weekend http://goo.gl/sdrbkJ • Poisson Disk Sampling http://goo.gl/71OzY • Artificial Intelligence: A Modern Approach http://goo.gl/CCCel8 • The Art of Computer Programming http://goo.gl/EUhZ8M
34. • Doryen Library http://goo.gl/71EpZx • Horton http://goo.gl/xpLTFB • Pygame http://goo.gl/1hv6DK • PyAngband http://goo.gl/mr5MMD