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Paper detail

Cost Optimal Planning as Satisfiability

We investigate upper bounds on the length of cost optimal plans that are valid for problems with 0-cost actions. We employ these upper bounds as horizons for a SAT-based encoding of planning with costs. Given an initial upper bound on the cost of the optimal plan, we experimentally show that this SAT-based approach is able to compute plans with better costs, and in many cases it can match the optimal cost. Also, in multiple instances, the approach is successful in proving that a certain cost is the optimal plan cost.

preprint2021arXivOpen access
Mohammad Abdulaziz
Logic in Computer ScienceArtificial Intelligence
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Mohammad Abdulaziz

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