  Using artificial intelligence (AI) to automatically test a game remains a
critical challenge for the development of richer and more complex game worlds
and for the advancement of AI at large. One of the most promising methods for
achieving that long-standing goal is the use of generative AI agents, namely
procedural personas, that attempt to imitate particular playing behaviors which
are represented as rules, rewards, or human demonstrations. All research
efforts for building those generative agents, however, have focused solely on
playing behavior which is arguably a narrow perspective of what a player
actually does in a game. Motivated by this gap in the existing state of the
art, in this paper we extend the notion of behavioral procedural personas to
cater for player experience, thus examining generative agents that can both
behave and experience their game as humans would. For that purpose, we employ
the Go-Explore reinforcement learning paradigm for training human-like
procedural personas, and we test our method on behavior and experience
demonstrations of more than 100 players of a racing game. Our findings suggest
that the generated agents exhibit distinctive play styles and experience
responses of the human personas they were designed to imitate. Importantly, it
also appears that experience, which is tied to playing behavior, can be a
highly informative driver for better behavioral exploration.
