More Like this

1. Acting as Inverse Inverse Planning

   https://doi.org/10.1145/3588432.3591510  

   Chandra, Kartik; Li, Tzu-Mao; Tenenbaum, Joshua; Ragan-Kelley, Jonathan (July 2023, ACM)

   Great storytellers know how to take us on a journey. They direct characters to act—not necessarily in the most rational way—but rather in a way that leads to interesting situations, and ultimately creates an impactful experience for audience members looking on. If audience experience is what matters most, then can we help artists and animators directly craft such experiences, independent of the concrete character actions needed to evoke those experiences? In this paper, we offer a novel computational framework for such tools. Our key idea is to optimize animations with respect to simulated audience members’ experiences. To simulate the audience, we borrow an established principle from cognitive science: that human social intuition can be modeled as “inverse planning,” the task of inferring an agent’s (hidden) goals from its (observed) actions. Building on this model, we treat storytelling as “inverse inverse planning,” the task of choosing actions to manipulate an inverse planner’s inferences. Our framework is grounded in literary theory, naturally capturing many storytelling elements from first principles. We give a series of examples to demonstrate this, with supporting evidence from human subject studies. 

   more » « less
2. Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner

   https://doi.org/10.1109/CDC51059.2022.9992959  

   Pattanayak, Kunal; Krishnamurthy, Vikram; Berry, Christopher (December 2022, 2022 IEEE 61st Conference on Decision and Control (CDC))

   Inverse reinforcement learning (IRL) deals with estimating an agent’s utility function from its actions. In this paper, we consider how an agent can hide its strategy and mitigate an adversarial IRL attack; we call this inverse IRL (I-IRL). How should the decision maker choose its response to ensure a poor reconstruction of its strategy by an adversary performing IRL to estimate the agent’s strategy? This paper comprises four results: First, we present an adversarial IRL algorithm that estimates the agent’s strategy while controlling the agent’s utility function. Then, we propose an I-IRL result that mitigates the IRL algorithm used by the adversary. Our I-IRL results are based on revealed preference theory in microeconomics. The key idea is for the agent to deliberately choose sub-optimal responses so that its true strategy is sufficiently masked. Third, we give a sample complexity result for our main I-IRL result when the agent has noisy estimates of the adversary-specified utility function. Finally, we illustrate our I-IRL scheme in a radar problem where a meta-cognitive radar is trying to mitigate an adversarial target. 

   more » « less
3. Quantile Inverse Optimization: Improving Stability in Inverse Linear Programming

   https://doi.org/10.1287/opre.2021.2143  

   Shahmoradi, Zahed; Lee, Taewoo (January 2022, Operations research)
4. High-Frequency Limit of the Inverse Scattering Problem: Asymptotic Convergence from Inverse Helmholtz to Inverse Liouville

   https://doi.org/10.1137/22M147075X  

   Chen, Shi; Ding, Zhiyan; Li, Qin; Zepeda-Núñez, Leonardo (March 2023, SIAM Journal on Imaging Sciences)
5. Inverse Optimal Transport

   https://doi.org/10.1137/19M1261122  

   Stuart, Andrew M.; Wolfram, Marie-Therese (January 2020, SIAM Journal on Applied Mathematics)

   null (Ed.)