Name
Abstract | ||
|---|---|---|
Current negotiation algorithms often assume that utility has an explicit representation as a function over the set of possible deals and that for any deal its utility value can be calculated easily. We argue however, that a more realistic model of negotiations would be one in which the negotiator has certain knowledge about the domain and must reason with this knowledge in order to determine the value of a deal, which is time-consuming. We propose to use Game Description Language to model such negotiation scenarios, because this may enable us to apply existing techniques from General Game Playing to implement domain-independent, reasoning, negotiation algorithms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46840-2_9 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Automated negotiation,General game playing,Game description language | Game description language,Domain knowledge,Computer science,Theoretical computer science,General game playing,Artificial intelligence,Machine learning,Negotiation | Conference |
Volume | ISSN | Citations |
10003 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dave de Jonge | 1 | 26 | 8.04 |
| Dongmo Zhang | 2 | 368 | 40.10 |