Abstract
| Original language | English |
|---|---|
| Title of host publication | Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) |
| Publisher | AAAI Press |
| Pages | 3622-3628 |
| Number of pages | 7 |
| Publication status | Published - Aug 2017 |
| Event | Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) - San Francisco, United States Duration: 4 Feb 2017 → 9 Feb 2017 |
Publication series
| Name | AAAI Conference on Artificial Intelligence (AAAI) |
|---|---|
| Publisher | AAAI |
| ISSN (Print) | 2159-5399 |
| ISSN (Electronic) | 2374-3468 |
Conference
| Conference | Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) |
|---|---|
| Country | United States |
| City | San Francisco |
| Period | 4/02/17 → 9/02/17 |
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Landmark-Based Heuristics for Goal Recognition. / Pereira, Ramon Fraga ; Oren, Nir; Meneguzzi, Felipe.
Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). AAAI Press, 2017. p. 3622-3628 (AAAI Conference on Artificial Intelligence (AAAI)).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Landmark-Based Heuristics for Goal Recognition
AU - Pereira, Ramon Fraga
AU - Oren, Nir
AU - Meneguzzi, Felipe
N1 - 6 volumes http://www.aaai.org/Press/Proceedings/aaai17.php
PY - 2017/8
Y1 - 2017/8
N2 - Automated planning can be used to efficiently recognize goals and plans from partial or full observed action sequences. In this paper, we propose goal recognition heuristics that rely on information from planning landmarks — facts or actions that must occur if a plan is to achieve a goal when starting from some initial state. We develop two such heuristics: the first estimates goal completion by considering the ratio between achieved and extracted landmarks of a candidate goal, while the second takes into account how unique each landmark is among landmarks for all candidate goals. We empirically evaluate these heuristics over both standard goal/plan recognition problems, and a set of very large problems. We show that our heuristics can recognize goals more accurately, and run orders of magnitude faster, than the current state-of-the-art.
AB - Automated planning can be used to efficiently recognize goals and plans from partial or full observed action sequences. In this paper, we propose goal recognition heuristics that rely on information from planning landmarks — facts or actions that must occur if a plan is to achieve a goal when starting from some initial state. We develop two such heuristics: the first estimates goal completion by considering the ratio between achieved and extracted landmarks of a candidate goal, while the second takes into account how unique each landmark is among landmarks for all candidate goals. We empirically evaluate these heuristics over both standard goal/plan recognition problems, and a set of very large problems. We show that our heuristics can recognize goals more accurately, and run orders of magnitude faster, than the current state-of-the-art.
UR - http://www.aaai.org/Press/Proceedings/aaai17.php
M3 - Conference contribution
T3 - AAAI Conference on Artificial Intelligence (AAAI)
SP - 3622
EP - 3628
BT - Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
PB - AAAI Press
ER -