Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind

When inferring the goals that others are trying to achieve, people intuitively understand that others might make mistakes along the way. This is crucial for activities such as teaching, offering assistance, and deciding between blame or forgiveness. However, Bayesian models of theory of mind have generally not accounted for these mistakes, instead modeling agents as mostly optimal in achieving their goals. As a result, they are unable to explain phenomena like locking oneself out of one's house, or losing a game of chess. Here, we extend the Bayesian Theory of Mind framework to model boundedly rational agents who may have mistaken goals, plans, and actions. We formalize this by modeling agents as probabilistic programs, where goals may be confused with semantically similar states, plans may be misguided due to resource-bounded planning, and actions may be unintended due to execution errors. We present experiments eliciting human goal inferences in two domains: (i) a gridworld puzzle with gems locked behind doors, and (ii) a block-stacking domain. Our model better explains human inferences than alternatives, while generalizing across domains. These findings indicate the importance of modeling others as bounded agents, in order to account for the full richness of human intuitive psychology.Comment: Accepted to CogSci 2021. 6 pages, 5 figures. (Appendix: 1 page, 1 figure

Traffic smoothing using explicit local controllers

The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving. As part of the 100 automated vehicles experiment named MegaVanderTest, feedback controls were used to induce strong dissipation via velocity smoothing. More precisely, a single vehicle driving differently in one of the four lanes of I-24 in the Nashville area was able to regularize the velocity profile by reducing oscillations in time and velocity differences among vehicles. Quantitative measures of this effect were possible due to the innovative I-24 MOTION system capable of monitoring the traffic conditions for all vehicles on the roadway. This paper presents the control design, the technological aspects involved in its deployment, and, finally, the results achieved by the experiment.Comment: 21 pages, 1 Table , 9 figure

Change Point Detection in Time Series via Multivariate Singular Spectrum Analysis

The objective of change-point detection (CPD) is to estimate the time of significant and abrupt changes in the dynamics of a system through multivariate time series observations. The setup of CPD covers a wide range of real-world problems such as quality control, medical diagnosis, speech recognition, and fraud detection to name a few. In this thesis, we develop and analyze a principled method for CPD that combines a variant of multivariate singular spectrum analysis (mSSA) approach with the cumulative sum (CUSUM) procedure for sequential hypothesis testing. In particular, we model the underlying dynamics of multivariate time series observations through the spatio-temporal model introduced recently in the mSSA literature. The change points in such a setting correspond to a change in the underlying spatio-temporal model. As the primary contributions of this work, we develop a CUSUM-based algorithm to detect such change points in an online fashion. Further, we extend the analysis of CUSUM statistics, traditionally done for the setting of independent observations, to the dependent setting of (multivariate) time series under the spatiotemporal factor model. Specifically, we analyze the performance of our algorithm in terms of the average running length (ARL) – a common metric used traditionally in sequential hypothesis testing to measure the trade-off between the delay in a true detection and the running time until a false detection. We formally establish that for any given detection parameter h > 0, on average, the algorithm detects a change point with a delay of (h) time steps, while in the case of no change it takes at least Ω(exp(h)) time steps until it makes a false detection. Finally, we empirically show that the proposed CPD method provides state-of-the-art performance across synthetic and benchmark datasets.S.M

Traffic smoothing using explicit local controllers

The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving. As part of the 100 automated vehicles experiment named MegaVanderTest, feedback controls were used to induce strong dissipation via velocity smoothing. More precisely, a single vehicle driving differently in one of the four lanes of I-24 in the Nashville area was able to regularize the velocity profile by reducing oscillations in time and velocity differences among vehicles. Quantitative measures of this effect were possible due to the innovative I-24 MOTION system capable of monitoring the traffic conditions for all vehicles on the roadway. This paper presents the control design, the technological aspects involved in its deployment, and, finally, the results achieved by the experiment