Fast Comprehensive Planner for Fully Observable Nondeterministic Problems Andres Calderon Jaramillo – Faculty Advisor: Dr. Jicheng Fu Department of Computer Science, University of Central Oklahoma Research of artificial intelligence planning aims to design planning algorithms (i.e., planners), which are targeted at finding plans to take a system from an initial state to a goal state. In this project, we propose an algorithm that uses an existing classical planner to efficiently find strong and strong cyclic plans for nondeterministic problems in fully observable domains. Additionally, we equip our planner with heuristics that inform the search for a plan along relevant directions. Our experiments suggest that our algorithm significantly outperforms state-of-the-art planners in commonly used benchmark domains. This work was supported in part by the National Institute of General Medical Sciences of the National Institutes of Health through Grant Number 8P20GM INTRODUCTION In the context of artificial intelligence planning, a domain is a structure that describes the possible actions that can be used in finding a plan. A planning problem for a given domain specifies the initial state of a system and a set of goals to achieve. A planner is an algorithm that solves a planning problem by finding a suitable set of actions in the domain to take the system from the initial state to at least one goal state. FOND (Fully Observable Nondeterministic) problems assume that each state in a system can be fully observed and that the actions in the domain may have more than one possible outcome. Solutions can be classified in three categories (Cimatti et al. [1]): weak plans, strong cyclic plans, and strong plans. See Figure 1 and Figure 2. BACKGROUND Figure 2(a). A weak plan. There is at least one successful path to a goal. Figure 2(b). A strong cyclic plan. A plan may use actions that can cause cycles but every state has a path to a goal. Figure 2(c). A strong plan. The goal is achieved from any state without using actions that cause cycles. Initial State Goal Initial State Goal Initial State Goal Given a domain and a problem, our planner attempts to find a strong solution. If one does not exist, it proceeds to search for a strong cyclic solution. Principles of Operation Our basic approach is based on (Kuter et al. [2]): 1.The domain is relaxed into a deterministic domain by splitting nondeterministic actions into individual outcomes. 2.A classical planner is used to solve the initial state (find a path to the goal). The path is then expanded into a weak plan according to the original domain. OUR PLANNER Constraints A strong plan search prevents the formation of cycles in the solution by temporarily disabling actions. Unsolvable states are handled using a backtrack procedure. Heuristics The following heuristics are used in addition to those embedded in the classical planner. The heuristics are explained in (Fu et al. [3]): State reuse: the classical planner is stopped if a solved state is found. 3.Unsolved states along the weak plan are solved in the same manner. The process is repeated until no unsolved states remain. Initial State Goal Unsolved! AC B pick-up(B, A) put-down(B) Figure 1. Example of a simple FOND plan. The action pick-up(x, y) is nondeterministic as it can succeed or fail (block x may fall on the table). The action put-down(x) is deterministic. Initial State Goal AC B ACB Goal alternative: the search is first directed to a solved sibling instead of the original goal. Reused state Omitted classical plan Solved sibling [1] A. Cimatti, M. Pistore, M. Roveri, and P. Traverso, "Weak, strong, and strong cyclic planning via symbolic model checking," Artif. Intell., vol. 147, pp , [2] U. Kuter, D. Nau, E. Reisner, and R. P. Goldman, "Using classical planners to solve nondeterministic planning problems," in 18th International Conference on Automated Planning and Scheduling (ICAPS), [3] J. Fu, V. Ng, F. B. Bastani, and I.-L. Yen, "Simple and fast strong cyclic planning for fully-observable nondeterministic planning problems," in Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Three, Barcelona, Catalonia, Spain, REFERENCES