Name
Abstract | ||
|---|---|---|
Traditional search techniques are difficult to apply to cooperative negotiation games, due to the often enormous search trees and the difficulty in calculating the value of a players position or move. We propose a generic agent architecture that ensembles negotiation, trust and opponent modeling, simplifying the development of agents capable of playing these games effectively by introducing modules to handle these challenges. We demonstrate the application of this modular architecture by instantiating it in two different games and testing the designed agents in a variety of scenarios; we also assess the role of the negotiation, trust and opponent modeling modules in each of the games. Results show that the architecture is generic enough to be applied in a wide variety of games. Furthermore, we conclude that the inclusion of the three modules allows for more effective agents to be built. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.5220/0006253101070118 | ICAART: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 1 |
Keywords | Field | DocType |
Multi-agent Systems, Cooperative Games, Negotiation, Opponent Modeling, Trust Reasoning | Computer science,Humanities,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.38 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| João Marinheiro | 1 | 1 | 0.38 |
| Henrique Lopes Cardoso | 2 | 223 | 34.02 |