Adaptive planning depth in human problem solving

Abstract

We humans are capable of solving challenging planning problems, but the range of adaptive strategies that we use to address them are not yet fully characterized. Here, we designed a series of problem-solving tasks that require planning at different depths. After systematically comparing the performance of participants and AI planners, we found that when facing manageable problems that require planning to a certain number of subgoals (from 1 to 6), participants make an adaptive use of their cognitive resources - namely, they tend to select an initial plan having the minimum required depth, rather than selecting the same depth for all problems. When facing more challenging problems that require planning up to 7 or 8 subgoals, participants tend to select a simpler (greedy) strategy wherein plans consider only the next 1 or 2 subgoals. Furthermore, we found a strong similarity between how different participants solve the same problems. These results support the view of problem solving as a bounded rational process, which adapts costly cognitive resources to task demands.

Beyond simple laboratory studies: developing sophisticated models to study rich behavior

“…To exemplify the extent to which motor behavior may bring a rich trace of mental activity, Figure 1 shows the finger movements of a participant during a problem-solving task that resembles the "traveling salesman" problem [33]. The task consists of starting from the yellow node and finding a path that connects all the red nodes, without passing through the same node twice.…”

“…Figure 1. The richness of movement kinematics data during the solution of a problem-solving task, which requires participants to find a path that starts from the yellow node and passes through all the red nodes, without crossing any node twice [33]. The nine panels show the time course of the movements, from the beginning (a) to the end (i) of the task.…”