Planning social actions through the others' eyes for emergent storytelling

Stories have become an important element of games, since they can increase their immersion level by giving the players the context and the motivation to play. However, despite the interactive nature of games, their stories usually do not develop considering every decision and/or action the players are capable of, because depending on the game size, it would take too much effort to author alternative routes for all of them. To make these alternatives viable, an interesting solution would be to procedurally generate them, which could be achieved by using the story generation approaches already developed by many works of the storytelling field. Some of these approaches are based on the simulation of virtual worlds, in which the stories are generated by making the characters that inhabit the worlds act trying to reach their goals. The resulting actions and the world's reactions compose the final story. Since the actions are the building blocks of the stories, the characters' acting capabilities are determinant features of the generation potential of simulations. For instance, it is only possible to generate stories with deception if the characters are capable of deceiving each other. To allow the generation of stories where the characters are capable of manipulation, cooperation and other social behaviors by actively using what the others will do based on what they know and see, we propose a recursive planning approach that deals with the uncertainty of the others' knowledge and with a purposely error-prone perception simulation. To test our proposal we developed a story generation system and designed an adaptation of Little Red Riding Hood world as test scenario. With our approach, the system was capable of generating coherent story variations with deceptive actions.