Spider Search: an Efficient and Non-Frontier-Based Real-Time Search Algorithm Presenter: Chao Lin Chu 599B Advisor: Dr. Russell J. Abbott

Outline ● Introduction ● Problem formulation ● AI approaches ● Experimental result ● Future work ● Conclusion

Introduction ● Real-time searches use limited depth. ● Standard searches don't work on large spaces. ● We can't simply reduce the depth to solve problem. ● Spider Search uses a very low constant space and time to solve this.

Problem Formulation – Boat/Torpedo problem ● 2x2 square sea, toroidal world, from -1 to 1 ● Torpedoes are faster. Boat has tighter turning radius. ● The smart boat plans ahead to avoid collision. ● They are small compared to the space. ● Small lookahead is not enough.

AI Approaches ● Breadth-First Search ● Depth-First Search ● A* Search ● Learning Real-Time A* ( LRTA* ) ● Spider Search

Spider Search ● Different from Frontier-based Searches ● Separate  Generation  Selection

Spider Search ● Population and fitness from Genetic Algorithm. ● Use probabilities to control randomness of path distribution. ● Limited depth. ● Keep the best path for next step.

Spider Search ● Turn the binary tree into an N-ary tree.  LLLLL  RLLLL  RRLLL  RRRLL  RRRRL  RRRRR ● Build out significantly longer paths. ● Standard AI searches don't do this.

Fitness Function

Spider Search ● Then pick the best leaf node. ● The next state will be the neighbor of the root that is on the path to the best leaf node. ● Then keep the best path for the next step.

Result – part 1

Result – part 2

Comparison Between LRTA* and Spider Search ● LRTA* -- grow exponentially with depth ● Spider Search: O( d * ( d + 1 ) ) where d is the depth. O( c d ) where c is a constant of population ● The most important thing is that the depth is not changed in a single run so the required time and space is a small constant throughout each run, for instance, 75 * 14 = 1050.

Future Work ● Enhancement of the Boat ● Enhancement of the Torpedoes ● Adapt Spider Search into Other Search Algorithm

Conclusion ● Not optimal but efficient. ● Use very low constant space and time.

Thank you!

Spider Search