Name
Abstract | ||
|---|---|---|
Episodic memory can store time sequential events and retrieve them anytime with specific cues. However, if the episodic memory only stores events comprised of actions and objects, execution of episodes may fail if current situation is different from the settings it learned in. As a solution, we propose Deep C-ART (Context-Adaptive Resonance Theory) which considers not only time sequential events but also their contexts. In addition to the learning process of Deep ART, Deep C-ART stores context information such as situation of objects, states of robots, place, and time of episodes. Since context changes over each event in an episode, Deep C-ART forms an episode with an event sequence and a context sequence. During retrieval and execution of episode, it compares the current situation with the learned one to verify that it is executable or in an anomaly situation. The effectiveness of Deep C-ART is demonstrated through computer simulations. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | Episodic memory, situated object, context, Deep C-ART |
Field | DocType | ISSN |
Episodic memory,Computer science,Real-time computing,Robustness (computer science),Context model,Human–computer interaction,Artificial intelligence,Event sequence,Robot,Machine learning,Executable,Encoding (memory) | Conference | 1062-922X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gyeong-Moon Park | 1 | 13 | 2.04 |
| Sanghyun Cho | 2 | 0 | 0.68 |
| Jong-Hwan Kim | 3 | 8 | 6.01 |