= Masterthesis: Opponent Models in Bilateral Negotiation =
by Mark Hendrikx.

== Literature Survey ==
The newest version can be found on the [https://mmi.tudelft.nl/svn/timb/Opponent%20Models%20Survey SVN repository]

== Main Research Questions ==
 * How to measure the quality of opponent models?
 * Are agents using opponent models "better" than other agents?
 * How do opponent models compare regarding accuracy, convergence, and computational cost?
 * How to create an opponent model which is better than the state-of-the-art?

== Meeting ==
[http://mmi.tudelft.nl/trac/negotiation/wiki/MeetingMinutes Meeting agendas and minutes]

== Planning ==
The datas in the overview below refer to deadlines.

 * ~~Validated all decoupled agents (13-01-12)~~

 * ~~Study for exam (15-01-12 -> 03-02-12)~~

 * ~~First version of short paper ACAN (18-02-12)~~

 * ~~Finished ANAC2012 agent for qualification round (18-03-12)~~

 * ~~Finished Camera ready of short paper ACAN (13-04-12)~~

 * Finished editing OM paper (20-04-12)
   * ~~Abstract / Introduction (01-04-12)~~
   * ~~Preliminaries (03-04-12)~~
   * ~~OM in automated negotiation (05-04-12)~~
   * ~~OM Framework (07-04-12)~~
   * ~~Learning methods for opponent models~~
   * Learning of OM (12-04-12)
     * Revise
       * ~~Introduction~~
       * ~~Section 1: Reservation value~~
       * ~~Section 2: Negotiation deadline~~
       * ~~Section 3: Issue Preference Order~~
       * ~~Section 4: Outcome Preference Order~~
       * Section 5: Bidding Strategy
       * ~~Section 6: Acceptance Strategy~~
     * Add new papers
       * Short paper
       * Long papers
   * ~~Use of OM (13-04-12)~~
   * ~~Evaluating OM (14-04-12)~~
   * ~~OM in ANAC (15-04-12)~~
   * Quality of OM in ANAC
       * ~~Test setup~~
       * Test results evaluation
   * Conclusion (20-04-12)

 * ANAC competition
   * Finished ANAC2012 agent for finals (07-05-12)
   * Wrote paper (6 - 8 pages)

 * Compare accuracy, convergence, and computational cost of existing models (05-05-12)
   * Implement measurement methods per round
   * Create testsetup
   * Analyze data

 * Create a model which is beter than the state of the art
   * Improve frequency model by dynamic learning rate
   * Improve Bayesian model by improving decision function assumption

 * Conference paper about results (30-05-12)

 * Draft of masterthesis (10/06/12)

 * Finish masterthesis (10/07/12)