Changes between Version 94 and Version 95 of OpponentModels

Timestamp:

04/27/11 21:04:04 (14 years ago)

Author:

mark

Comment:

--

OpponentModels

@@ -349 +349 @@
 ||'''Author(s)'''||H.H. Bui, D. Kieronska and S. Venkatesh||
 ||'''Cited'''||5||
-||'''Subject(s)'''||||
+||'''Subject(s)'''||Logic-like representation negotiation model, bin-based opponent model, one-issue continious||
 ||'''Summary'''||The used method assumes that domain knowledge is available to partition the search space. Each turns, the agents communicate [[br]]the space where an agreement is possible. Each turn there is a negotiation between all agents to find a common space, which [[br]]means that the agent recommunicate a refined space of agreement until an agreement is reached. The proces continues until [[br]]a common decision is found (a decision is an element in the space of agreement). A learning algorithm can be used as follows: [[br]]first the full domain space is split into zones, which are allocated a uniform chance. This chance is updated for each region [[br]]for each agent based on the received space of agreement. When agents do not agree about the space, then a the space is chosen [[br]]which has the maximum support based on the chances of each space for each agent. This leads to a higher chance of agreement.
 ||