Name
Abstract | ||
|---|---|---|
Interactive dynamic influence diagrams (I-DIDs) are recognized graphical models for sequential multiagent decision making under uncertainty. They represent the problem of how a subject agent acts in a common setting shared with other agents who may act in sophisticated ways. The difficulty in solving I-DIDs is mainly due to an exponentially growing space of candidate models ascribed to other agents over time. in order to minimize the model space, the previous I-DID techniques prune behaviorally equivalent models. In this paper, we challenge the minimal set of models and propose a value equivalence approach to further compress the model space. The new method reduces the space by additionally pruning behaviorally distinct models that result in the same expected value of the subject agentu0027s optimal policy. To achieve this, we propose to learn the value from available data particularly in practical applications of real-time strategy games. We demonstrate the performance of the new technique in two problem domains. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5555/2936924.2937094 | AAMAS |
Keywords | Field | DocType |
Influence Diagrams,Decision Making,Multiple Agents | Intelligent agent,Computer science,Expected value,Equivalence (measure theory),Influence diagram,Artificial intelligence,Graphical model,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.36 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ross Conroy | 1 | 7 | 0.86 |
| Yifeng Zeng | 2 | 415 | 43.27 |
| M. Cavazza | 3 | 1605 | 161.76 |
| Jing Tang | 4 | 163 | 16.75 |
| Yinghui Pan | 5 | 30 | 5.21 |