Symbolic models play a key role in cognitive science, expressing computationally precise hypotheses about how the brain implements a cognitive process. Identifying an appropriate model typically requires a great deal of effort and ingenuity on the part of a human scientist. Here, we adapt FunSearch (Romera-Paredes et al. 2024), a recently developed tool that uses Large Language Models (LLMs) in an evolutionary algorithm, to automatically discover symbolic cognitive models that accurately capture human and animal behavior. We consider datasets from three species performing a classic reward-learning task that has been the focus of substantial modeling effort, and find that the discovered programs outperform state-of-the-art cognitive models for each. The discovered programs can readily be interpreted as hypotheses about human and animal cognition, instantiating interpretable symbolic learning and decision-making algorithms. Broadly, these results demonstrate the viability of using LLM-powered program synthesis to propose novel scientific hypotheses regarding mechanisms of human and animal cognition.

This paper addresses the problem of decentralized, outdoor formation coordination with multiple quadcopters. The problem is formulated as a receding horizon, mixed-integer non-linear program (RH-MINLP). Each quadcopter solves this RH-MINLP to generate its time-optimal speed profile along a minimum snap spline path while coordinating its position in a desired formation with other quadcopters. Constraints on quadcopter kinematics, dynamics, collision avoidance, wireless communication connectivity, and geometric formations are modeled. Communication connectivity is modeled as a constraint on maximum separation distance based on a minimum viable received signal strength in the presence of path loss attenuation. The resulting RH-MINLP is non-convex, and is solved using an outer-approximation branch and bound solver with a warm-starting scheme. The framework is validated via Hardware-in-the-Loop (HITL) and outdoor flight test with up to 6 quadcopters. Results demonstrate the effect of number of quadcopters and formation type on total transit time. Average radio packet loss statistics during transit indicate robust network performance for a round robin communication scheduling scheme.