Name
Abstract | ||
|---|---|---|
We present the Bayesian Online Prediction for Ad hoc teamwork (BOPA), a novel algorithm for ad hoc teamwork which enables a robot to collaborate, on the fly, with human teammates without any pre-coordination protocol. Unlike previous works, BOPA relies only on state observations/transitions of the environment in order to identify the task being performed by a given teammate (without observing the teammate's actions and environment's reward signals). We evaluate BOPA in two distinct settings, namely (i) an empirical evaluation in a simulated environment with three different types of teammates, and (ii) an experimental evaluation in a real-world environment, deploying BOPA into an ad hoc robot with the goal of assisting a human teammate in completing a given task. Our results show that BOPA is effective at correctly identifying the target task, efficient at solving the correct task in optimal and near-optimal times, scalable by adapting to different problem sizes, and robust to non-optimal teammates, such as humans. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86230-5_50 | PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2021) |
Keywords | DocType | Volume |
Ad hoc teamwork, Multi-agent systems, Human-robot collaboration | Conference | 12981 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| João G. Ribeiro | 1 | 0 | 0.68 |
| Miguel Faria | 2 | 68 | 2.98 |
| Alberto Sardinha | 3 | 36 | 8.27 |
| Francisco A. Melo | 4 | 399 | 46.33 |