Abstract
1. Introduction
     - Background/motivations (maybe from project definition)
     - Aims
     - Methodology used (research methodology?)
     - Intelligent agents

2. Computational Learning Techniques
     - Added section on evolution strategies. Expand other evolutionary
     computation approaches.
 
3. Intelligent agents in auction markets

4. Methodology
 - Design Decisions (informal)
   * Choice of learning technique & justification
   * Application of learning technique & justification
     * Trader Design
       - When do they learn?
       - What do they learn?
       - Strategy:
	  - Interactive traders
          - Non-interactive traders.
     * Operation of the market (ie. discuss trading period vs life cycle)
     * Market simulation design, eg. OMT describing how the evolution
         strategy class relates to the trader class, etc., discussion of
         evolution strategy class.

5. Validation of Traders
 - Validation of evolution strategy
 - Validation of traders (ie. if we know the evolution strategy works from
the previous section for model objective functions like the sphere model,
how do we prove that it is working correctly for the traders?)

6. Hypothesis Testing
  - Method
    - How do we prove that ES traders perform better than original
traders. 
  - Results

7. Discussion
 - What do the results prove?

8. Conclusion

Appendices
- Raw data generated by tests.

References