Principles of artificial intelligence
Principles of artificial intelligence
The cascade-correlation learning architecture
Advances in neural information processing systems 2
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Planning under time constraints in stochastic domains
Artificial Intelligence - Special volume on planning and scheduling
Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Learning to Predict by the Methods of Temporal Differences
Machine Learning
The Journal of Machine Learning Research
Practical solution techniques for first-order MDPs
Artificial Intelligence
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
The metric-FF planning system: translating "Ignoring delete lists" to numeric state variables
Journal of Artificial Intelligence Research
Where "Ignoring delete lists" works: local search topology in planning benchmarks
Journal of Artificial Intelligence Research
The first probabilistic track of the international planning competition
Journal of Artificial Intelligence Research
mGPT: a probabilistic planner based on heuristic search
Journal of Artificial Intelligence Research
Probabilistic planning via heuristic forward search and weighted model counting
Journal of Artificial Intelligence Research
Conformant planning via heuristic forward search: A new approach
Artificial Intelligence
Learning to act using real-time dynamic programming
Artificial Intelligence
Incremental plan aggregation for generating policies in MDPs
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Automatic induction of bellman-error features for probabilistic planning
Journal of Artificial Intelligence Research
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Enforced hill-climbing is an effective deterministic hill-climbing technique that deals with local optima using breadth-first search (a process called "basin flooding"). We propose and evaluate a stochastic generalization of enforced hill-climbing for online use in goal-oriented probabilistic planning problems. We assume a provided heuristic function estimating expected cost to the goal with flaws such as local optima and plateaus that thwart straightforward greedy action choice. While breadth-first search is effective in exploring basins around local optima in deterministic problems, for stochastic problems we dynamically build and solve a heuristic-based Markov decision process (MDP) model of the basin in order to find a good escape policy exiting the local optimum. We note that building this model involves integrating the heuristic into the MDP problem because the local goal is to improve the heuristic. We evaluate our proposal in twenty-four recent probabilistic planning-competition benchmark domains and twelve probabilistically interesting problems from recent literature. For evaluation, we show that stochastic enforced hill-climbing (SEH) produces better policies than greedy heuristic following for value/cost functions derived in two very different ways: one type derived by using deterministic heuristics on a deterministic relaxation and a second type derived by automatic learning of Bellman-error features from domain-specific experience. Using the first type of heuristic, SEH is shown to generally outperform all planners from the first three international probabilistic planning competitions.