## Satisficing-Planning MetricTime Track

### TPP

In this planning domain there are 40 problems (named by numbers on the
x-axis). In the quality plot, the plan metric has to be minimized.
For the problems in this domain, the main challenge is computing a
valid plan with *good quality*, but for a fully-automated planner in
some cases even finding any valid plan is hard. Computing the optimal
solution in this domain is known NP-hard problem.

### Openstacks

In this planning domain there are 20 problems (named by numbers on the
x-axis). In the quality plot, the plan metric has to be minimized.
For the problems in this domain, the main challenge is computing a
valid plan with *good quality*, but for a fully-automated planner in
some cases even finding any valid plan is hard.

### Pathways

In this planning domain there are 30 problems (named by numbers on the
x-axis). In the quality plot, the plan metric has to be minimized.
For the problems in this domain, the main challenge is computing a
valid plan with *good quality*, but for a fully-automated planner in
many cases even finding any valid plan is hard.

### Pipesworld

In this planning domain there are 50 problems (named by numbers on the
x-axis). The problems not indicated are solved by no planner.
In the quality plot, the plan metric has to be minimized.

### Rovers

In this planning domain there are 40 problems (named by numbers on the
x-axis). In the quality plot, the plan metric has to be minimized.
The problems not indicated are solved by no planner.
For the problems in this domain, the main challenge is computing a
valid plan with *good quality*, but for a fully-automated planner in
some cases even finding any valid plan is hard.

### Storage

In this planning domain there are 30 problems (named by numbers on the
x-axis). In the quality plot, the plan metric has to be minimized.

### Trucks

In this planning domain there are 30 problems (named by numbers on the
x-axis). In the quality plot, the plan metric has to be minimized.
For the problems in this domain, the main challenge is computing a
valid plan with *good quality*, but for a fully-automated planner in
some cases even finding any valid plan is hard.