Evaluating Next Generation Probabilistic Planners

Rutgers University New Brunswick, New Brunswick NJ

Investigators

Abstract

This research project will develop planning algorithms and a set of general methods for evaluating probabilistic planners. Probabilistic planning is the area of sequential decision making concerned with choosing operators that change the state of the world when the available operators have uncertain outcomes. The driving goal of this project is to advance the state of the art of probabilistic planners toward increased efficiency, improved robustness to problem variations, and broadened applicability to real-world problems. To accomplish its goal, the project will focus on two interrelated tasks. First, it will propose and develop a methodology for evaluating probabilistic planners. This will require studying a set of alternatives and running experiments to correlate evaluation metrics with desirable outcomes in increasingly realistic domains. The project efforts will be coordinated closely with the larger research community through the biannual International Planning Competition (IPC), which will soon introduce a probabilistic track to its existing structure. This project will organize the track and will provide the community with a set of software programs for executing and evaluating plans in probabilistic domains. Second, the project members will pursue the development of their own planning algorithms, with a particular emphasis on approaches that exploit the relationship between probabilistic planning and reinforcement learning. The project will have definite research impacts in its study of the problem of probabilistic planning and how progress should be measured in the field. It will also advance the state of the art in planning and reinforcement learning through the exploration of instance-based techniques for learning to plan more effectively. However, the focus of the majority of the work will be on its broader impacts on the planning community as a whole, with concrete domain description languages, evaluation software, and benchmark problems that will serve to focus the community's efforts toward developing algorithms to solve problems of significant scientific and economic interest.