Name
Abstract | ||
|---|---|---|
The application of DCOP models to large problems faces two main limitations: (i) Modeling limitations, as each agent can handle only a single variable of the problem; and (ii) Resolution limitations, as current approaches do not exploit the local problem structure within each agent. This paper proposes a novel Multi-Variable Agent (MVA) DCOP decomposition technique, which: (i) Exploits the co-locality of each agent's variables, allowing us to adopt efficient centralized techniques within each agent; (ii) Enables the use of hierarchical parallel models and proposes the use of GPUs; and (iii) Reduces the amount of computation and communication required in several classes of DCOP algorithms. |
Year | Venue | Field |
|---|---|---|
2016 | AAAI | Computer science,Exploit,Theoretical computer science,Artificial intelligence,Machine learning,Computation,Distributed computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ferdinando Fioretto | 1 | 88 | 20.13 |
| William Yeoh 0001 | 2 | 30 | 4.30 |
| Enrico Pontelli | 3 | 1901 | 181.26 |