  Powerful domain-independent planners have been developed to solve various
types of planning problems. These planners often require a model of the acting
agent's actions, given in some planning domain description language. Manually
designing such an action model is a notoriously challenging task. An
alternative is to automatically learn action models from observation. Such an
action model is called safe if every plan created with it is consistent with
the real, unknown action model. Algorithms for learning such safe action models
exist, yet they cannot handle domains with conditional or universal effects,
which are common constructs in many planning problems. We prove that learning
non-trivial safe action models with conditional effects may require an
exponential number of samples. Then, we identify reasonable assumptions under
which such learning is tractable and propose SAM Learning of Conditional
Effects (Conditional-SAM), the first algorithm capable of doing so. We analyze
Conditional-SAM theoretically and evaluate it experimentally. Our results show
that the action models learned by Conditional-SAM can be used to solve
perfectly most of the test set problems in most of the experimented domains.
