appears in y with signum

![0_image_2.png](0_image_2.png)

![0_image_0.png](0_image_0.png)

![0_image_1.png](0_image_1.png)

![0_image_3.png](0_image_3.png)

n appears in n does not appear in

## References

Bellman, R. (1957). Dynamic programming. Princeton University Press.

Blythe, J. (1999). Decision-theoretic planning. AI Magazine, 20 ( ),  7 54.

Boutilier, C., Dean, T. L., & Hanks, S. (1999). Decision-theoretic planning: Structural assumptions and computational leverage. Journal of AI Research, 11, 1 94.

Boutilier, C., Dearden, R., & Goldszmidt, M. (1995). Exploiting structure in policy construction. In Extending Theories of Action: Formal Theory & Practical Applications:
Papers from the 1995 AAAI Spring Symposium (pp.

8).  AAAI Press, Menlo Park, California.
Burago, D., de Rougemont, M., & Slissenko, A. (1996). On the complexity of partially observed Markov decision processes. Theoretical Computer Science, 157 ( ), 161 18 .

Cassandra, A. (1998). Exact and approximate algorithms for partially observable Markov decision processes. Unpublished doctoral dissertation, Brown University.

Cassandra, A.,   aelbling, L., & Littman, M. L. (1995).  Efficient dynamic-programming updates in partially observable Markov decision processes (Tech. Rep. No. CS-95-19).

Providence, Rhode Island: Brown University.
Greenlaw, R., Hoover, H. J., & Ruzzo, W. L. (1995). Limits to parallel computation:
P-completeness theory. Oxford University Press.

Hansen, E. (1998a). Finite-memory control of partially observable systems. Unpublished doctoral dissertation, Dept. of Computer Science, University of Massachussets at Amherst .

Hansen, E. (1998b). Solving POMDPs by searching in policy space. In Proceedings of the Conference Uncertainty in Artificial Intelligence (pp. 11 19).