Name
Abstract | ||
|---|---|---|
The Multiagent POMDP (MPOMDP) framework provides well-known methods to model and solve fully communicative multiagent problems. However, the size of these models grows exponentially in the number of agents, and agents are required to act in synchrony. We show how these problems can be mitigated through an event-driven, asynchronous formulation of the MPOMDP dynamics. We can prove that the optimal value function in our framework is piecewise linear and convex, allowing us to extend a standard point-based solver to the event-driven setting. We also show how belief states can be updated at run-time in asynchronous domains. Our results show that asynchronous models scale better to larger domains than synchronous analogues, while retaining solution quality. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.5555/2484920.2485179 | AAMAS |
Keywords | Field | DocType |
asynchronous execution,larger domain,asynchronous domain,event-driven setting,mpomdp dynamic,optimal value function,asynchronous formulation,belief state,asynchronous models scale,multiagent pomdp,multiagent pomdps,communicative multiagent problem | Asynchronous communication,Mathematical optimization,Partially observable Markov decision process,Computer science,Regular polygon,Bellman equation,Artificial intelligence,Solver,Piecewise linear function,Machine learning,Exponential growth | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| João V. Messias | 1 | 26 | 4.77 |
| Matthijs T.J. Spaan | 2 | 863 | 63.84 |
| Pedro U. Lima | 3 | 516 | 69.88 |