Name
Abstract | ||
|---|---|---|
Frequency opponent modeling is one of the most widely used opponent modeling techniques in automated negotiation, due to its simplicity and its good performance. In fact, it outperforms even more complex mechanisms like Bayesian models. Nevertheless, the classical frequency model does not come without its own assumptions, some of which may not always hold in many realistic settings. This paper advances the state of the art in opponent modeling in automated negotiation by introducing a novel frequency opponent modeling mechanism, which soothes some of the assumptions introduced by classical frequency approaches. The experiments show that our proposed approach outperforms the classic frequency model in terms of evaluation of the outcome space, estimation of the Pareto frontier, and accuracy of both issue value evaluation estimation and issue weight estimation. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-69131-2_16 | PRINCIPLES AND PRACTICE OF MULTI-AGENT SYSTEMS (PRIMA 2017) |
Keywords | Field | DocType |
Agreement technologies, Automated negotiation, Opponent modeling, Multi-agent systems | Computer science,Multi-agent system,Artificial intelligence,Adversary,Pareto principle,Machine learning,Negotiation,Bayesian probability | Conference |
Volume | ISSN | Citations |
10621 | 0302-9743 | 1 |
PageRank | References | Authors |
0.39 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Okan Tunali | 1 | 1 | 0.39 |
| Reyhan Aydoǧan | 2 | 51 | 12.96 |
| Victor Sanchez-Anguix | 3 | 102 | 14.87 |