Sabre is a narrative planner—a centralized, omniscient decision maker that solves a multi-agent storytelling problem. The planner has an author goal it must achieve, but every action taken by an agent must make sense according to that agent's individual intentions and limited, possibly wrong beliefs. This paper describes the implementation of Sabre, which supports a rich action syntax and imposes no arbitrary limit on the depth of theory of mind. We present a search procedure for generating plans that achieve the author goals while ensuring all agent actions are explained, and we report the system's performance on several narrative planning benchmark problems.

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In "The Logic of Campaigning", Dean and Parikh consider a candidate making campaign statements to appeal to the voters. They model these statements as Boolean formulas over variables that represent stances on the issues, and study optimal candidate strategies under three proposed models of voter preferences based on the assignments that satisfy these formulas. We prove that voter utility evaluation is computationally hard under these preference models (in one case, #P -hard), along with certain problems related to candidate strategic reasoning. Our results raise questions about the desirable characteristics of a voter preference model and to what extent a polynomial-time-evaluable function can capture them.