Minds for mobile agents
We model large sets of interacting mobile agents whose movement choices are determined in a discrete-choice random-utility framework spanning simple multinomial logit models to crossed-nested logit models that account for velocity-related correlations. The agents are predictive, so their choice utility is in part based on projecting the future positions of other agents they observe. They can have diverse characteristics and individual movement plans consisting of goals about visiting sets of locations. When a plan is disrupted through interactions with other agents in crowded scenarios, they can dynamically create sub-goals to enable them to return to complete their mission. Additive combinations of choice utilities provide a method to combine, weight, and resolve sets competing demands from goals (e.g., heading to the next location), individual preferences (e.g., for speed and inter-personal distance), rules (e.g., passing on the right) and social factors (e.g., following a leader and grouping). We report simulations showing that these agents can competently navigate and achieve their goals in difficult environments and results on Bayesian estimation of agent parameters from movement data. We discuss the potential for this framework to build, parametrize, explore, and predict systems of agents guided by complex and flexibly specified cognitive states.