| 13 | |
| 14 | == IAMHAGGLER2010 == |
| 15 | '''Author(s):''' |
| 16 | |
| 17 | '''Summary:''' [[BR]] |
| 18 | This paper gives a brief description of the strategy and methods the IAMHAGGLER uses. It uses the discount factor to calculate an estimated utility of the opponent. With the use of the discount factor (and other formulas) IAMHAGGLER is able to predict the maximum possible discounted utility at a given time t and si able to determine what sort bid the agent should offer at any given time t. IAMHAGGLER makes a distinction between ordered and unordered issues. With unordered issues it uses baysean learning (choosing an iso-utility space, which is the same as a threshold range for the bids). |
| 19 | |
| 20 | == Benefits of Learning in Negotiation == |
| 21 | '''Author(s):''' Dajun Zeng, Katia Sycara |
| 22 | |
| 23 | '''Summary:''' [[BR]] |
| 24 | Looks at the idea of learning with in a sequential desicion making protocol. They created a protocol called BAZAAR. Lays focus also on the learning of reservation value and how that can be benefitial by creating a better bid exchange (less bids exchanged before agreement is made) and a better joint utiity. |