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Learning ranking functions for efficient search
This dissertation explores algorithms for learning ranking functions to efficiently solve search problems, with application to automated planning. Specifically, we consider the frameworks of beam search, greedy search, and randomized search, which all aim to maintain tractability at the cost of not guaranteeing completeness nor optimality. Our learning objective for each of these frameworks is to induce a linear ranking function for guiding the search that performs nearly as well as unconstrained search, hence gaining computational efficiency without seriously sacrificing optimality.
We first investigate the problem of learning ranking functions to guide beam search, with a focus on learning feature weights given a set of features. We present a theoretical analysis of the problem's computational complexity that identifies the core efficient and hard subclasses. In addition we study online learning algorithms for the problem and analyze their convergence properties. The algorithms are applied to automated planning, showing that our approach is often able to outperform an existing state-of-the-art planning heuristic as well as a recent approach to learning such heuristics.
Next, we study the problem of automatically learning both features and weights to guide greedy search. We present a new iterative learning algorithm based on RankBoost, an efficient boosting algorithm for ranking and demonstrate strong empirical results in the domain of automated planning.
Finally, we consider the problem of learning randomized policies for guiding randomized greedy search with restarts. We pose this problem in the framework of reinforcement learning and investigate policy-gradient algorithms for learning both features and weights. The results show that in a number of domains this approach is significantly better than those obtained for deterministic greedy search
Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO
The design of spacecraft trajectories for missions visiting multiple
celestial bodies is here framed as a multi-objective bilevel optimization
problem. A comparative study is performed to assess the performance of
different Beam Search algorithms at tackling the combinatorial problem of
finding the ideal sequence of bodies. Special focus is placed on the
development of a new hybridization between Beam Search and the Population-based
Ant Colony Optimization algorithm. An experimental evaluation shows all
algorithms achieving exceptional performance on a hard benchmark problem. It is
found that a properly tuned deterministic Beam Search always outperforms the
remaining variants. Beam P-ACO, however, demonstrates lower parameter
sensitivity, while offering superior worst-case performance. Being an anytime
algorithm, it is then found to be the preferable choice for certain practical
applications.Comment: Code available at https://github.com/lfsimoes/beam_paco__gtoc
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