Machine learning is bad at situations where it is provided with limited training data. Therefore I would design a game with frequently-changing rules. In particular, I would create an expansion set for Betrayal at House on the Hill.
Betrayal at House on the Hill is about exploring a haunted house. The gimmick is you do not know the rules of the game before you begin playing. There are many different rules the haunted house might obey.
Humans could beat machines for a long time if the following two eratta were applied:
An intelligence may not have access to information ahead of time about the expansion packs’ rules. (It is fair play for the AI’s programmers to have access to the base ruleset but not the special iteration-specific rulesets.)
An intelligence only gets points for winning the first time it encounters a particular ruleset.
An AI would need to read the specialized rules on-the-spot and then understand the semantics well enough to devise a strategy. Then the computer would have to execute this strategy correctly on its first try. No software in existence today can do anything like this.
Not only could humans crush machines at this board game, today’s best machine learning software cannot even play this game (follow the rules) without its programmers’ reading the complete rulebook ahead of time, which is cheating.
As for goal #2, Betrayal at House on the Hill is my favorite board game.
Machine learning is bad at situations where it is provided with limited training data. Therefore I would design a game with frequently-changing rules. In particular, I would create an expansion set for Betrayal at House on the Hill.
Betrayal at House on the Hill is about exploring a haunted house. The gimmick is you do not know the rules of the game before you begin playing. There are many different rules the haunted house might obey.
Humans could beat machines for a long time if the following two eratta were applied:
An intelligence may not have access to information ahead of time about the expansion packs’ rules. (It is fair play for the AI’s programmers to have access to the base ruleset but not the special iteration-specific rulesets.)
An intelligence only gets points for winning the first time it encounters a particular ruleset.
An AI would need to read the specialized rules on-the-spot and then understand the semantics well enough to devise a strategy. Then the computer would have to execute this strategy correctly on its first try. No software in existence today can do anything like this.
Not only could humans crush machines at this board game, today’s best machine learning software cannot even play this game (follow the rules) without its programmers’ reading the complete rulebook ahead of time, which is cheating.
As for goal #2, Betrayal at House on the Hill is my favorite board game.