Three laboratory experiments involving students' behavior and brain imaging and one randomized field experiment in a college physics class explored the importance of physical experience in science learning. We reasoned that students' understanding of science concepts such as torque and angular momentum is aided by activation of sensorimotor brain systems that add kinetic detail and meaning to students' thinking. We tested whether physical experience with angular momentum increases involvement of sensorimotor brain systems during students' subsequent reasoning and whether this involvement aids their understanding. The physical experience, a brief exposure to forces associated with angular momentum, significantly improved quiz scores. Moreover, improved performance was explained by activation of sensorimotor brain regions when students later reasoned about angular momentum. This finding specifies a mechanism underlying the value of physical experience in science education and leads the way for classroom practices in which experience with the physical world is an integral part of learning.

We consider stochastic model predictive control of a multi-agent systems with constraints on the probabilities of inter-agent collisions. We first study a sample-based approximation of the collision probabilities and use this approximation to formulate constraints for the stochastic control problem. This approximation will converge as the number of samples goes to infinity, however, the complexity of the resulting control problem is so high that this approach proves unsuitable for control under real-time requirements. To alleviate the computational burden we propose a second approach that uses probabilistic bounds to determine regions with increased probability of presence for each agent and formulate constraints for the control problem that guarantee that these regions will not overlap. We prove that the resulting problem is conservative for the original problem with probabilistic constraints, ie. every control strategy that is feasible under our new constraints will automatically be feasible for the original problem. Furthermore we show in simulations in a UAV path planning scenario that our proposed approach grants significantly better run-time performance compared to a controller with the sample-based approximation with only a small degree of sub-optimality resulting from the conservativeness of our new approach.

We consider a class of mixed-integer optimization problems subject to N randomly drawn convex constraints. We provide explicit bounds on the tails of the probability that the optimal solution found under these N constraints will become infeasible for the next random constraint. First, we study constraint sets in general mixed-integer optimization problems, whose continuous counterpart is convex. We prove that the number of support constraints (i.e., constraints whose removal strictly improve the optimal objective) is bounded by a number depending geometrically on the dimension of the decision vector. Next, we use these results to show that the tails of the violation probability are bounded by a binomial distribution. Finally, we apply these bounds to an example of robust truss topology design. The findings in this paper are a first step towards an extension of previous results on continuous random convex programs to the case of problems with mixedinteger decision variables that naturally occur in many realworld applications.

Abstract. We present an auction-flavored multi-robot planning mechanism where coordination is to be achieved on the occupation of atomic resources modeled as binary inter-robot constraints. Introducing virtual obstacles, we show how this approach can be combined with particlebased obstacle avoidance methods, offering a decentralized, auction-based alternative to previously established centralized approaches for multirobot open-loop control. We illustrate the effectiveness of our new approach by presenting simulations of typical spatially-continuous multirobot path-planning problems and derive bounds on the collision probability in the presence of uncertainty.

The health and economic outcomes of the COVID-19 pandemic will in part be determined by how effectively experts can communicate information to the public and the degree to which people follow expert recommendation. Using a survey experiment conducted in May of 2020 with almost 5,000 respondents, this paper examines the effect of source cues and message frames on perceptions of information credibility in the context of COVID-19. Each health recommendation was framed by expert or non-expert sources, was fact- or experience-based, and suggested potential gain or loss to test if either the source cue or framing of issues affected responses to the pandemic. We find no evidence that either source cue or message framing influence people’s responses—instead, respondents’ ideological predispositions, media consumption, and age explain much of the variation in survey responses, suggesting that public health messaging may face challenges from growing ideological cleavages in American politics.