  Intelligent agents collect and process information from their dynamically
evolving neighbourhood to efficiently navigate through it. However, agent-level
intelligence does not guarantee that at the level of a collective; a common
example is the jamming we observe in traffic flows. In this study, we ask: how
and when do the interactions between intelligent agents translate to desirable
or intelligent collective outcomes? We explore this question in the context of
a bidisperse crowd of agents with opposing desired directions of movement, like
in a pedestrian crossing. We model a facet of intelligence, viz. memory, where
the agents remember how well they were able to travel in their desired
directions and make up for their non-optimal past. We find that memory has a
non-monotonic effect on the dynamics of the collective. When memory is short
term, the local rearrangement of the agents lead to the formation of
symmetrically jammed arrangements, which take longer to unjam. However, when
agents remember across longer time-scales, we find that the dynamics of an
agent becomes sensitive to the relatively small differences in the history of
the nearby agents. This gives rise to heterogeneity in the movement that causes
agents to unjam more readily and form lanes that ease the movement.
