Pelita: Coding Algorithms to Win @ PyLadies Berlin, 2014

Transcript

2. The Pelita contest or… writing a pedagogical game framework
3. The Pelita contest or… writing a pedagogical game framework Rike-Benjamin Schuppner Institut für Theoretische Biologie, HU Berlin rikebs@debilski.de // debilski.de // @debilski
4. Pelita
5. What’s it about?
6. http://verdoux.wordpress.com/2009/06/09/the-shining-1980/ A maze
7. Moving around http://verdoux.wordpress.com/2009/06/09/the-shining-1980/
8. http://verdoux.wordpress.com/2009/06/09/the-shining-1980/ Enemy bots
9. http://img.timeinc.net/time/photoessays/2008/top10_1950s/top10_1950s_them.jpg Attack
10. Pelita
11. Before you ask •Pelita •Actor-based Toolkit for Interactive Language Education in Python •‘Pill-eater’ •Created 2011–2012 especially for the G-Node School ‘Advanced Scientific Programming in Python’ •(Idea from John DeNero and Dan Klein,
 UC Berkeley¹) ¹ http://www.denero.org/content/pubs/eaai10_denero_pacman.pdf
12. A bit of history why did we create it in the first place
13. Science these days •Many scientists need to write code for simulations, evaluations, modelling, data fitting •Code is often ‘inherited’ from someone in the workgroup •Scientists may lack (practical) education in programming
14. Main problems •Scientists/students feel they do not have the time or the confidence to properly learn coding •Or best practices •Or version control
15. Back in summer 2009 •The German Neuroinformatics Node (G- Node) decides to fund a summer school •‘Advanced Scientific Programming in Python’ •Targeted at scientists who spend their days writing and debugging software •But who (mostly) are no computer scientists
16. http://python.g-node.org
17. 7th iteration of the school in 2014 •All across Europe •Berlin, Warszawa, Trento, St Andrews, Kiel, Zürich, Split •30 students (~170 applications) •~10 tutors (~15 in total) •6 days program •targeted at Master or PhD students and Post-docs from all areas of science
18. School programme •Usually a talk followed by pair programming exercises (two students per laptop – not their own) •Best practices for scientific computing •Version control •Advanced Python, advanced NumPy •Cython, parallelisation, memory management •At half-time, the project starts
19. http://grolschfilmworks.com/ca/agegate/?url=ca%2Ffeatures%2Fandrew-bujalski-talks-computer-chess Pair programming
20. Finishing the school on a high note ¹ http://www.denero.org/content/pubs/eaai10_denero_pacman.pdf 2009 version
21. The contest •Idea: enable teamwork and encourage experimentation with Python •Idea: Let’s play a game •Competing with a predefined computer AI is boring •Playing against other groups makes it unpredictable and more fun •Started in 2009 using an AI game framework from John DeNero and Dan Klein, UC Berkeley¹ ¹ http://www.denero.org/content/pubs/eaai10_denero_pacman.pdf
22. The contest •Original game should not be published •So between 2011–2012 we wrote our own •5 teams à 6 people working two afternoons and one day on creating a playable bot •On the final day, have a tournament between all teams •Did it work? ¹ http://www.denero.org/content/pubs/eaai10_denero_pacman.pdf
23. http://www.moviemail.com/images/large/computer-chess-35397_1.jpg What usually happens
24. What usually happens •On the second evening, some students do not want to leave the workshop •Instead of helping out, the tutors start building their own agents or try to find bugs in the game to exploit •People get highly nervous 2 hours before the deadline •In the end, ~40 people stare at a screen for an hour, watching four coloured blobs move around
25. What usually happens
26. What usually happens •On the final party, people have only one thing to talk about: •Analysing and complaining about why their code did not work as intended •Well, mostly
27. The game
28. } Each Team owns two Bots Overview Bots for team 1 Bots for team 0
29. } Each Team owns two Bots } Each Bot is controlled by a Player Overview 29 Bots for team 0 Bots for team 1 Player for team 0 Player for team 1 st er ): ): st from pelita.datamodel import west from pelita.player import AbstractPlayer class UnidirectionalPlayer(AbstractPlayer): def get_move(self): return west
30. } Each Team owns two Bots } Each Bot is controlled by a Player } Harvester or Destroyer Bots Overview 30
31. } Each Team owns two Bots } Each Bot is controlled by a Player } Harvester or Destroyer Bots } Bots are Destroyers in homezone } Harvesters in enemy’s homezone } Game ends when all food pellets are eaten Overview 31
32. Overview 32
33. The rules http://filmscoreclicktrack.com/2013/01/the-ten-commandments-of-film-music/
34. The rules •Eating: When a Bot eats a food pellet, the food is permanently removed and one point is scored for that Bot’s team. •Timeout: Each Player only has 3 seconds to return a valid move. If it doesn’t, a random move is executed. (All later return values are discarded.)
 5 timeouts and you’re out! •Eating another Bot: When a Bot is eaten by an opposing destroyer, it returns to its starting position (as a harvester). 5 points are awarded for eating an opponent. •Winning: A game ends when either one team eats all of the opponents’ food pellets, or the team with more points after 300 rounds. •Observations: Bots can only observe an opponent’s exact position, if they or their teammate are within 5 squares of the opponent bot. If they are further away, the opponent’s positions are noised.
35. http://www.berlinale.de/de/archiv/jahresarchive/2012/02_programm_2012/02_filmdatenblatt_2012_20126311.php Controlling the bots
36. Player code •Fork the demo repository:
 https://github.com/ASPP/pelita_player •Make changes •Run with
 ./pelitagame ./PATH_TO/pelita_player/ team
37. My first players Pelita imports Inherit from AbstractPlayer Use the Player API • Careful: Invalid return values of get_move result in a random move. from pelita.datamodel import east from pelita.player import AbstractPlayer class UnidirectionalPlayer(AbstractPlayer): def get_move(self): return east class DrunkPlayer(AbstractPlayer): def get_move(self): directions = self.legal_moves random_dir = self.rnd.choice(directions) return random_dir
38. API examples •In your get_move method, information about the current universe and food situation is available. See the documentation for more details. •self.current_pos
 Where am I? •self. me
 Which bot am I controlling? •self. enemy_bots
 Who and where are the other bots? •self. enemy_food
 Which are the positions of the food pellets? •self. current_uni
 Retrieve the universe you live in. •self. current_uni.maze
 How does my world look like? •self. legal_moves
 Where can I go? •self.me.is_destroyer
 Am I dangerous?
39. Building a team •A team consists of two players (and a name) •Create it using the SimpleTeam class •SimpleTeam("Magnificent Team", GoodPlayer(), RemarkablePlayer()) •Export your team using the factory function • def factory():
 return SimpleTeam(…)

40. Demo bots •In ./players directory •Also, many examples in the test suite
41. Testing http://verdoux.wordpress.com/2009/06/09/the-shining-1980/
42. Testing •Two ways to test your Players •first: Simply run the game and test by watching •$ ./pelitagame MyTeam EnemyTeam •second: Write unittests and test by testing •Example in the template •Probably not enough time today :)
43. Tournament http://magiaeimagem.files.wordpress.com/2010/02/ingmar-bergman-the-seventh-seal.jpg
44. Tournament •No proper tournament due to time constraints •Just hack the code, push to github and we’ll try to run a few matches live on screen
45. Strategies http://mubi.com/lists/the-labyrinth
46. Finite state machine • Evaluate the current situation and choose the algorithm accordingly Going to opponent half Looking for food Fleeing Start arrived in your half arrived in opponent’s half opponent far away opponent very close
47. Look-ahead Player •Create a function which calculates a score for each situation, eg. •value(game_state) = −1 × distance_from_nearest_food + 100 × score •At each turn do •get the legal moves for your bot •request the future universe, given one of the actions
 self.current_uni.copy().move_bot(self._index, direction) •compute the score •choose the direction with the best score
48. Notes on writing http://verdoux.wordpress.com/2009/06/09/the-shining-1980/
49. Notes on writing •Mazes won’t have dead-ends •Hard to catch another bot which outruns you •We’d like to see bots which combine their powers and attack from two sides
50. Notes on writing •Think about shortest-path algorithms •Keep track of opponents •Investigate communication between the Players •Re-use your code •Think about working in a team
51. Getting ready •Clone the pelita and group repos:
 git clone https://github.com/ASPP/pelita.git
 git clone https://github.com/FORK/pelita_player.git •Run a simple demo game:
 ./pelita/pelitagame ./pelita_player/team •For help:
 ~/pelita/pelitagame --help •See the Pelita documentation:
 http://ASPP.github.io/pelita •Questions? Ask us.
52. Movie stills • ‘Gibel sensatsii’ (1935, dir. Aleksandr Andriyevsky) • ‘Them’ (1954, dir. Gordon Douglas) • ‘The Ten Commandments’ (1956, dir. Cecil B. DeMille) • ‘Det sjunde inseglet’ (1957, dir. Ingmar Bergman) • ‘The Shining’ (1980, dir. Stanley Kubrick) • ‘Computer Chess’ (2013, dir. Andrew Bujalski)