Revisiting Goal Probability Analysis in Probabilistic Planning
نویسندگان
چکیده
Maximizing goal probability is an important objective in probabilistic planning, yet algorithms for its optimal solution are severely underexplored. There is scant evidence of what the empirical state of the art actually is. Focusing on heuristic search, we close this gap with a comprehensive empirical analysis of known and adapted algorithms. We explore both, the general case where there may be 0-reward cycles, and the practically relevant special case of acyclic planning, like planning with a limited action-cost budget. We consider three different algorithmic objectives. We design suitable termination criteria, search algorithm variants, dead-end pruning methods using classical planning heuristics, and node selection strategies. Our evaluation on more than 1000 benchmark instances from the IPPC, resource-constrained planning, and simulated penetration testing reveals the behavior of heuristic search, and exhibits several improvements to the state of the art.
منابع مشابه
Revisiting Partial-Order Probabilistic Plannin
We present a partial-order probabilistic planning algorithm that adapts plan-graph based heuristics implemented in Repop. We describe our implemented planner, Reburidan, named after its predecessors Repop and Buridan. Reburidan uses plan-graph based heuristics to first generate a base plan. It then improves this plan using plan refinement heuristics based on the success probability of subgoals....
متن کاملGoal Probability Analysis in Probabilistic Planning: Exploring and Enhancing the State of the Art
Unavoidable dead-ends are common in many probabilistic planning problems, e.g. when actions may fail or when operating under resource constraints. An important objective in such settings is MaxProb, determining the maximal probability with which the goal can be reached, and a policy achieving that probability. Yet algorithms for MaxProb probabilistic planning are severely underexplored, to the ...
متن کاملTo appear, AAAI-94 An Algorithm for Probabilistic Least{Commitment Planning
We de ne the probabilistic planning problem in terms of a probability distribution over initial world states, a boolean combination of goal propositions, a probability threshold, and actions whose e ects depend on the execution-time state of the world and on random chance. Adopting a probabilistic model complicates the de nition of plan success: instead of demanding a plan that provably achieve...
متن کاملfor Probabilistic Nicholas Kushmerick Steve Hanks Daniel Weld
We define the probabilistic planning problem in terms of a probability distribution over initial world states, a boolean combination of goal propositions, a probability threshold, and actions whose effects depend on the execution-time state of the world and on random chance. Adopting a probabilistic model complicates the definition of plan success: instead of demanding a plan that proovably ach...
متن کاملProbabilistic Planning is Multi-objective!
Probabilistic planning is an inherently multi-objective problem where plans must trade-off probability of goal satisfaction with expected plan cost. To date, probabilistic plan synthesis algorithms have focussed on single objective formulations that bound one of the objectives by making some unnatural assumptions. We show that a multi-objective formulation is not only needed, but also enables u...
متن کامل