Name
Abstract | ||
|---|---|---|
We present a model for recursive Bayesian filtering based on lifted multiset states. Combining multisets with lifting makes it possible to simultaneously exploit multiple strategies for reducing inference complexity when compared to list-based grounded state representations. The core idea is to borrow the concept of Maximally Parallel Multiset Rewriting Systems and to enhance it by concepts from Rao-Blackwellisation and Lifted Inference, giving a representation of state distributions that enables efficient inference. In worlds where the random variables that define the system state are exchangeable - where the identity of entities does not matter - it automatically uses a representation that abstracts from ordering (achieving an exponential reduction in complexity) and it automatically adapts when observations or system dynamics destroy exchangeability by breaking symmetry. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.24963/ijcai.2018/703 | IJCAI |
DocType | Volume | Citations |
Conference | abs/1801.10495 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan Lüdtke | 1 | 0 | 1.35 |
| Max Schröder | 2 | 4 | 5.86 |
| Sebastian Bader | 3 | 51 | 14.66 |
| Kristian Kersting | 4 | 1932 | 154.03 |
| Thomas Kirste | 5 | 93 | 18.37 |