Exploring Options for Efficiently Evaluating the Playability of Computer Game Agents

Automatic generation of game content is an important challenge in computer game design. Such generation requires methods that are both efficient and guaranteed to produce playable content. While existing methods are adequate for currently available types of games, games based on more complex entities and structures may require new methods. In this paper, we use computational complexity analysis to explore algorithmic options for efficiently evaluating the playability of and generating playable groups of enhanced agents that are capable of exchanging items and facts with each other and human players. Our results show that neither of these problems can be solved both efficiently and correctly either in general or relative to a surprisingly large number of restrictions on enhanced agent structure and gameplay. We also give the first restrictions under which the playability evaluation problem is solvable both efficiently and correctly.