Name
Abstract | ||
|---|---|---|
We apply a constrained Hidden Markov Model architecture to the problem of simultaneous localization and surveying from sensor logs of mobile agents navigating in unknown environments. We show the solution of this problem for the case of one robot and extend our model to the more interesting case of multiple agents, that interact with each other through proximity sensors. Since exact learning in this case becomes exponentially expensive, we develop an approximate method for inference using loopy belief propagation and apply it to the localization and surveying problem with multiple interacting robots. In support of our analysis, we report experimental results showing that with the same amount of data, approximate learning with the interaction signals outperforms exact learning ignoring interactions. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/978-3-540-30560-6_14 | European Summer School on Multi-AgentControl |
Keywords | Field | DocType |
multiple interacting robot,interesting case,approximate method,simultaneous localization,exact learning,surveying problem,multiple agent,hidden markov model architecture,approximate learning,occupancy grid,simultaneous localization and mapping,hidden markov model,mobile agent,loopy belief propagation | Proximity sensor,Inference,Mobile agent,Artificial intelligence,Robot,Hidden Markov model,Machine learning,Mathematics,Belief propagation | Conference |
ISBN | Citations | PageRank |
3-540-24457-3 | 0 | 0.34 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sam T. Roweis | 1 | 4556 | 497.42 |
| Ruslan Salakhutdinov | 2 | 12190 | 764.15 |