Abstract
In real world environments the state is almost never
completely known. Exploration is often expensive.
The application of planning in these environments
is consequently more difficult and less robust. In
this paper we present an approach for predicting
new information about a partially-known state. The
state is translated into a partially-known multigraph,
which can then be extended using machinelearning
techniques. We demonstrate the effectiveness
of our approach, showing that it enhances the
scalability of our planners, and leads to less time
spent on sensing actions.
completely known. Exploration is often expensive.
The application of planning in these environments
is consequently more difficult and less robust. In
this paper we present an approach for predicting
new information about a partially-known state. The
state is translated into a partially-known multigraph,
which can then be extended using machinelearning
techniques. We demonstrate the effectiveness
of our approach, showing that it enhances the
scalability of our planners, and leads to less time
spent on sensing actions.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17 |
| Pages | 2032-2038 |
| DOIs | |
| Publication status | Published - 2017 |
| Event | IJCAI-17 - Melbourne, Australia Duration: 19 Aug 2017 → 25 Aug 2017 |
Conference
| Conference | IJCAI-17 |
|---|---|
| Country/Territory | Australia |
| City | Melbourne |
| Period | 19/08/2017 → 25/08/2017 |