As the final section of our design document, we will discuss the features we wish we had time to add to the system, but, due to time and resource constraints, must wait until later versions of the project.

• The original goal of this project was to implement agents which could use actual planning strategies to plan out moves in the long-term, instead of the simple one-step lookahead provided in this version. Due to time constraints and the complexity of the issue, we were unable to tackle this. We expect that in the future this will be accomplished, however, as several members of the team will continue working on the system as part of a research project over the next year.
  
  
• It has been mentioned that the general heuristic the agents currently use is extensible. Many more heuristics could be defined; as many ways as one could think about how to play the game Acquire. Since the heuristics are generally defined as the maximization of certain parameters, one could even implement non-intuitive strategies to determine if the best strategy for winning Acquire is not an obvious one. These extensions to the current General Heuristic will be implemented as a parameter passed to each Agent that tells it which Heuristic to load.
  
  
• The system has been implemented around the Acquire domain; however, we believe the same statistical analysis of viable strategies would be useful in many other domains, for example, engineering design, day trading, and so on. The system as it is implemented will provide a clear API for future programmers who wish to implement another domain for which the agents must perform certain actions to reach some goal.
  
  
• The Acquire system in its present incarnation will allow only 2-6 computer agent players. It may be interesting (or fun) for humans to pit their own strategies, which are inherently more adaptable than software agents, against the computer. The system will be easily extensible to simply "plug in" human input to the engine where it expects agent input.
  
  
• The statistical analysis of the game(s) played by the agents utilizing specific heuristics is currently displayed simply as a tally of the number of games played and which agents won using which strategies, or heuristics. We consider that users may want to view the progress of a game, to use their intuition to recognize when an agent is playing poorly or well. Also, it is much easier for a human player (at such time as this is supported, see above) to follow the game if presented with a graphical interface.
  
  
• Agents are not intended to be capable of collaborating with each other during game play in this version of the system. In some future incarnation, however, it could be possible that this would be allowed and also that the user could define the level of privacy afforded to agents who are whispering to each other, ranging from full privacy (i.e., no one knows the pair communicated), to medium privacy (i.e., the other agents are aware that some communication was taking place), to no privacy (i.e., the other agents both see and hear what was communicated).