Date:
Part I - August 26, 2005, 12 Midday, Interaction Room
Part II - August 30, 2005, 4pm, Conference Room, Physics Annexe
Who: Sanjeev Naguleswaran
Seminar type: Research seminar
Abstract:
Current Automated Planning techniques to extract valid plans from a plan specification are typically exponential in complexity. In addition, representation of planning problems as Markov Decision Processes (MDP) and their subsequent solution suffers from the “state space explosion” problem where the number of states grows exponentially large with the size of the problem.
This presentation will endeavor to describe research undertaken with the purpose of developing a reduced complexity/tractable automated planning methodology. This work is based on concepts borrowed from fields such as Mathematics, Physics, Computer Science and Engineering.
The representation of the planning problem as a directed bi-partite graph which can be unfolded to a tree like structure will be described first. The equipping of this structure with probabilities and the Markov property will then be briefly explored resulting in a Markov Decision Process with a reduced number of states. A method for adapting the Grover Search Algorithm for use within a Dynamic Programming algorithm such as Value Iteration is then described as a possible low complexity solution to optimise the MDP.
Preliminary investigations into adapting quantum walks to extract a valid plan from a graphical structure will also be presented.