General: Write a paper investigating an advanced version of one of the major topics we have covered in the AI course.

Length: 5 pages, space and a half, 12-point font, reasonable margins, etc.

Topics are due:Wednesday April 23rd. This just needs to be a brief paragraph describing the advanced version.

References: At least 4, of which 2 should be research publicaitons, the others can be books, magazine articles, etc.

Content: The paper be a detailed review of a successful, or an unsuccessful, advanced version of one of the topics we've discussed in class (search, game playing, logical programming, etc.).

The paper should include the follow material:

1. An explanation of the basic technique.
2. An explaination of the advanced technique that is sufficiently detailed that the reader can understand how it works and, prefereably, implement it.
3. A description of the specific sub-domain (if any) that the advanced technique is designed for. E.g. an advanced game playing search strategy might be specifically designed for games of chance.
4. Whether or not in your opinion the advanced technique successfully meets its goals.

1. D* - A version of A* search used when the map is discovered over time [Anthony Stentz, "Optimal and Efficient Path Planning for Partially-Known Environments," Proceedings IEEE International Conference on Robotics and Automation, May 1994].
2. Cooperative pathfinding - used when multiple agents are finding paths simultaniously, which may lead to collisions [Silver, David, "Cooperative Pathfinding," Proceedings of the First Conference on Artificial Intelligence and Interactive Digital Environments, 2005.
3. Minmax with learning evaluation functions - adverserial search where the evaluation function is learned over time [ MINITEX Subscribe (Full Service) Register (Limited Service, Free) Login Search: The ACM Digital Library The Guide Feedback Michael Buro, "Improving heurisitic mini-max search by supervised learning", Artificial Intelligence, Volume 134 , Issue 1-2 (January 2002), Pages: 85-99, 2002 ].
4. Large scale logical knowledge bases - scaling up logical agents in an attempt to reach "humanish" intelligence [Sicilia, M.A. Garcia, E. Sanchez, S. Rodriguez, E., "On integrating learning object metadata inside the OpenCyc knowledge base" IEEE Conference on Advanced Learning Technologies, 2004. Proceedings. IEEE International 2004, pgg: 900- 901][http://www.opencyc.org/]
5. Fuzzy logic with learning - fuzzy logic where the boundries of the fuzzy categories are learned over time.