Teaching computers to be more creative than humans through #games
Posted on April 19th, 2016
04/19/2016 @NYU Courant Institute, 251 Mercer Street, NY
Julian Togelius @NYU spoke about #AI, #games and #creativity? He talked about how game playing has been part of the development of AI and how AI can change the creation of games for humans.
Julian first talked about how algos play board games better than the best human, starting with #Chess and finally #Go in 2016. But he feels that board games are only a minute part of the universe of games played by humans. He explained how programs tackled 3 video games
- Car racing
Of the three, Starcraft appears to offer the biggest challenge for computers due to the size and complexity of the game playing universe. The first level of SuperMario can be easily solved with a simple algorithm, but higher levels require more sophistication to get around overhangs. Car racing is simple when there is only a single car, but competitor levels require an understanding of competitor’s strategies. However, the code to successfully solve one of these games does not immediately generalize to solutions for the other games.
He next opined that intelligence is more than solving specific problems, but implies the ability to solve a wide range of problem. This can be summarized by the Legg and Hutter formula which is a sum of skills playing all games weighted by the game complexity.
In competitions of algos across a variety of games, the Monte Carlo Tree Search (a statistical tree search algorithm that uses a forest of trees) appears to do best.
Julian next talked about how AI can be used to create better games for humans: PCG (Procedural Generated Content)
- Save development time
- Non-human creativity – most humans just copy other humans
- Create endless games
- Create player-adaptive games
- Study game design by formalizing it
He talked about using evolution to search for good content using
- Combinatorial creativity = combine lots of ideas to search a space
- Transformational creativity = change the problem definition to come up with new ideas
He proposed a collaboration of humans and algos. One tool to do this is the LUDI game description developed by Cameron Browne. Using the game descriptions of many games, one can use a genetic algorithm to combine the rules to create other games, some of which are interesting to humans. The game #Yavalath was created using this process. He also showed pictures of a collaborative tool for creating versions of the Cut-The-Rope game in which a human places objects in the space and the algo solves it.
Other research looks at humans playing specific games to develop an algorithm that predicts which aspects of the game create user interest and predict whether other individuals with different skill levels will find a game (level of a game) interesting.