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1. Learning to Ground Existentially Quantified Goals

2. Symmetries and Expressive Requirements for Learning General Policies

3. Learning General Policies for Classical Planning Domains: Getting Beyond C$_2$

4. Learning Generalized Policies Without Supervision Using GNNs

5. Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits

9. Learning General Policies with Policy Gradient Methods

13. Learning General Optimal Policies with Graph Neural Networks : Expressive Power, Transparency, and Limits

16. Learning Generalized Unsolvability Heuristics for Classical Planning

17. Methods for Detecting Unsolvable Planning Instances using Variable Projection

19. Analysing Approximability and Heuristics in Planning Using the Exponential-Time Hypothesis

20. Identifying Unsolvable Instances, Forbidden States and Irrelevant Information in Planning

21. Tractable Cost-Optimal Planning over Restricted Polytree Causal Graphs

23. Implementation och utökning av en typhärledningsalgoritm för Common Lisp

24. Fast Detection of Unsolvable Planning Instances Using Local Consistency

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