arXiv:2605.30563v1 Announce Type: new Abstract: Factored tasks are a classical planning representation that extends SAS+ with limited forms of disjunctive preconditions, conditional effects, and angelic nondeterminism. This allows for a more compact representation of tasks than traditional formalisms such as STRIPS or SAS+, and supports a wide range of task transformations. However, existing planning approaches for factored tasks have been limited to heuristic search methods. In this work, we investigate how to encode factored tasks in SAT. We propose several ways to encode the tasks, focusing
The paper demonstrates ongoing academic advances in AI planning, specifically how to make complex AI tasks more efficiently solvable by translating them into widely understood computational problems.
Improving the efficiency and scalability of AI planning systems is crucial for developing more capable and autonomous AI agents, impacting applications from smart assistants to robotics.
This work introduces new methods for encoding 'factored tasks' into SAT, potentially expanding the capabilities and applicability of satisfiability solvers in AI planning.
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More complex AI planning problems become computationally tractable through SAT encoding.
This could accelerate the development of more sophisticated AI agents capable of handling richer environmental models and action spaces.
Improved planning capabilities contribute to the broader viability of truly autonomous systems in real-world, dynamic environments.
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Read at arXiv cs.AI