Julian Evans | Searcharxiv

arXiv2023

Colloidal Directional Structures at a Nematic Liquid Crystal-Air Interface

We present a variety of structures formed by colloidal droplets at a nematic liquid crystal-air interface, where the elastic dipole-dipole, quadrupole-quadrupole, and dipole-quadrupole interactions are all essentially involved. The colloidal structures observed not only include chains with kinks or clusters, but also comprise directional structures, such as directional chains and branches, whose direction is associated with the tilting director in the liquid crystal layer. The dipole-quadrupole interaction, originating from the polydispersity of the droplets, plays a central role for the formation of these directional structures. Clusters consisting of directional branches and chains are also observed and found to be fractal statistically.

cond-mat.soft↗

arXiv2020

Multi-AI competing and winning against humans in iterated Rock-Paper-Scissors game

Predicting and modeling human behavior and finding trends within human decision-making processes is a major problem of social science. Rock Paper Scissors (RPS) is the fundamental strategic question in many game theory problems and real-world competitions. Finding the right approach to beat a particular human opponent is challenging. Here we use an AI (artificial intelligence) algorithm based on Markov Models of one fixed memory length (abbreviated as "single AI") to compete against humans in an iterated RPS game. We model and predict human competition behavior by combining many Markov Models with different fixed memory lengths (abbreviated as "multi-AI"), and develop an architecture of multi-AI with changeable parameters to adapt to different competition strategies. We introduce a parameter called "focus length" (a positive number such as 5 or 10) to control the speed and sensitivity for our multi-AI to adapt to the opponent's strategy change. The focus length is the number of previous rounds that the multi-AI should look at when determining which Single-AI has the best performance and should choose to play for the next game. We experimented with 52 different people, each playing 300 rounds continuously against one specific multi-AI model, and demonstrated that our strategy could win against more than 95% of human opponents.

cs.GT↗