Abstract
Interactive dynamic influence diagrams (I-DIDs) offer a
transparent and semantically clear representation for the sequential
decision-making problem over multiple time steps in
the presence of other interacting agents. Solving I-DIDs exactly
involves knowing the solutions of possible models of
the other agents, which increase exponentially with the number
of time steps. We present a method of solving I-DIDs
approximately by limiting the number of other agents' candidate
models at each time step to a constant. We do this by
clustering the models and selecting a representative set from
the clusters. We discuss the error bound of the approximation
technique and demonstrate its empirical performance.
| Originalsprog | Engelsk |
|---|---|
| Titel | PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE |
| Antal sider | 6 |
| Forlag | AAAI Press |
| Publikationsdato | 2007 |
| Sider | 782-787 |
| Status | Udgivet - 2007 |
| Begivenhed | The Twenty-Second Conference on Association for the Advancement of Artificial Intelligence (AAAI 2007) - Vancouver, Canada Varighed: 22 jul. 2007 → 26 jul. 2007 Konferencens nummer: 22 |
Konference
| Konference | The Twenty-Second Conference on Association for the Advancement of Artificial Intelligence (AAAI 2007) |
|---|---|
| Nummer | 22 |
| Land/Område | Canada |
| By | Vancouver |
| Periode | 22/07/2007 → 26/07/2007 |