Research Summary Adi Botea Computer Go Seminar, 29/09/2003

Overview ● Introduction ● Project #1:Hierarchical Path-finding in Commercial Computer Games ● Project #2:AI Planning ● Conclusion

Introduction ● Main strategy: – Use problem decomposition to reduce search complexity ● Application areas: – Puzzles such as Sokoban – Path-finding in commercial computer games – AI Planning

Project #1:Hierarchical Path-finding ● Find a path on a map ● Limited resources ● Speed is crucial ● Our approach: – Abstract maze – Build a smaller search space

Pre-processing

Problem Abstract Graph Level 1 Level 2

Experimental Results

Project #2:AI Planning ● Abstraction in AI Planning ● Challenge: – Develop a generic and automated method – No prior domain-specific knowledge – No human intervention

Approach Highlights ● Decompose a problem into linked components – Use static predicates to identify components – E.g., camera_on_vehicle(cam1, veh1) ● Pre-process components and store useful information – E.g., discover useful macros for each component ● drive(veh1, target) ● calibrate(cam1, target) ● take_picture(cam1, target)

Approach Highlights (cont'd) ● Reformulate abstracted problems in a standard language – Use any planner to solve problems ● Build an adaptive system – The greater the number of problems seen for a domain, the more efficient in that domain

Conclusion ● Use abstraction to reduce complexity of search problems ● Application areas: – Single-player puzzles – Path-finding in commercial computer games – AI planning