| Autor: |
Koça, Thimjo, de Jonge, Dave, Baarslag, Tim |
| Zdroj: |
Annals of Mathematics & Artificial Intelligence; Aug2024, Vol. 92 Issue 4, p903-924, 22p |
| Abstrakt: |
This work presents several new and efficient algorithms that can be used by negotiating agents to explore very large outcome spaces. The proposed algorithms can search for bids close to a utility target or above a utility threshold, and for win-win outcomes. While doing so, these algorithms strike a careful balance between being rapid, accurate, diverse, and scalable, allowing agents to explore spaces with as many as 10 250 possible outcomes on very run-of-the-mill hardware. We show that our methods can be used to respond to the most common search queries employed by 87 % of all agents from the Automated Negotiating Agents Competition between 2010 and 2021. Furthermore, we integrate our techniques into negotiation platform GeniusWeb in order to enable existing state-of-the-art agents (and future agents) to handle very large outcome spaces. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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