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Planning And Optimization - Overview Of Classical Planning Algorithms.pdf

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Preview of Planning and Optimization - Overview of Classical Planning Algorithms
🔗 Source: ai.dmi.unibas.ch
📊 Size: 182 KB
👤 Author: Malte Helmert and Gabriele Röger
⬇️ Downloads: 164

Summary

Explicit Search:

Involves systematically exploring the state space of a planning task using various search algorithms (depth-first, breadth-first, A, etc.).
Key design choices include search direction (progression vs. regression), search algorithm, and pruning techniques to optimize efficiency.

2. SAT Planning:

Formulates finding a plan as a propositional satisfiability (SAT) problem.
Encodes the planning task and horizon as a Boolean formula, then uses a generic SAT solver to find a satisfying assignment (plan).
 Allows for parallel application of operators to reduce search space.

3. Symbolic Search:

Utilizes symbolic representations of states and actions to efficiently plan.
Research in this area explores various logic-based encodings and solvers tailored for planning tasks.

Key Considerations:

Satisficing vs. Optimal Planning: Distinguish between finding any plan (satisficing) and finding the optimal plan with minimal cost.
 Heuristics: Used in both explicit search and SAT planning to guide the search process towards goals more efficiently.
Algorithm Portfolios:* Many algorithms combine techniques from different categories for improved performance on diverse tasks.

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Document en en

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  • File Format: PDF
  • File Size: 182 KB
  • Pages: 9
  • Language: EN
  • Author: Malte Helmert and Gabriele Röger
  • Total Downloads: 164
  • Last Updated: 1 week ago

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