  Previous STRIPS domain model acquisition approaches that learn from state
traces start with the names and parameters of the actions to be learned.
Therefore their only task is to deduce the preconditions and effects of the
given actions. In this work, we explore learning in situations when the
parameters of learned actions are not provided. We define two levels of trace
quality based on which information is provided and present an algorithm for
each. In one level (L1), the states in the traces are labeled with action
names, so we can deduce the number and names of the actions, but we still need
to work out the number and types of parameters. In the other level (L2), the
states are additionally labeled with objects that constitute the parameters of
the corresponding grounded actions. Here we still need to deduce the types of
the parameters in the learned actions. We experimentally evaluate the proposed
algorithms and compare them with the state-of-the-art learning tool FAMA on a
large collection of IPC benchmarks. The evaluation shows that our new
algorithms are faster, can handle larger inputs and provide better results in
terms of learning action models more similar to reference models.
