Abstract
In this work, we address the problem of learning planning domain
models from example action traces that contain missing
and noisy data. In many situations, the action traces that form
the input to domain model acquisition systems are sourced
from observations or even natural language descriptions of
plans. It is often the case that these observations are noisy
and incomplete. Therefore, making domain model acquisition
systems that are robust to such data is crucial. Previous
approaches to this problem have relied upon having access to
the underlying state in the input plans. We lift this assumption
and provide a system that does not require any state information.
We build upon the LOCM family of algorithms,
which also lift this assumption in the deterministic version
of the domain model acquisition problem, to provide a domain
model acquisition system that learns domain models
from noisy plans with missing information.
Original language | English |
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Pages | - |
Publication status | Published - 18 Jun 2017 |
Event | Workshop on Knowledge Engineering for Planning and Scheduling (KEPS). The 27th International Conference on Automated Planning and Scheduling (ICAPS) - Pittsburgh, United States Duration: 18 Jun 2017 → 23 Jun 2017 |
Conference
Conference | Workshop on Knowledge Engineering for Planning and Scheduling (KEPS). The 27th International Conference on Automated Planning and Scheduling (ICAPS) |
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Abbreviated title | KEPS 2017 |
Country/Territory | United States |
City | Pittsburgh |
Period | 18/06/17 → 23/06/17 |