Contributing to the recent interest in the dynamics of activity-travel patterns, this chapter discusses a framework of an agent-based modeling approach focusing on the dynamic formation of (location) choice sets. Individual travelers are represented as agents, each with their cognition of the environment, habits, and activity-travel patterns. Agents learn through their experiences with the transport systems, changes in the environments and from their social network. Conceptually, agents are assumed to have an aspiration level associated with choice sets that in combination with evaluation results determine whether the agent will start exploring or persist in habitual behavior; an activation level of each (location) alternative that determines whether or not the alternative is included in the choice set in the next time step, and an expected (utility) function to evaluate each (location) alternative given current beliefs. Each of these elements is dynamic. Based on principles of reinforcement learning, Bayesian learning, and social comparison theories, the framework specifies functions for experience-based learning, extended and integrated with social learning.