skill-131

name	skill-131
description	Provides utilities for optimizing PDDL planning problems by transforming domain specifications and enhancing state representations.
license	Proprietary. LICENSE.txt has complete terms

Requirements for Outputs

General Guidelines

• PDDL domain files must meet optimization standards.
• Problem files must adhere to the transformations specified.
• Outputs should clearly indicate optimization metrics.

Optimization Behavior

• Optimization processes should complete within a predefined time.
• If no optimization is possible, return an indication of failure.

---

PDDL Optimization Utilities

1. Load and Transform Domain

load-and-transform-domain(domain_path)

Description:
Loads a PDDL domain file and applies transformations to optimize the state representation.

Parameters:

• domain_path (str): Path to the PDDL domain file.

Returns:

• optimized_domain: A transformed domain for better efficiency.

Example:

optimized_domain = load_and_transform_domain("domain.pddl")

Notes:

• Utilizes unified_planning.io.PDDLTransformer.
• Throws an error if transformation fails.

2. Optimize Problem

optimize-problem(problem_path, optimized_domain)

Description: Optimizes the specified problem based on the optimized domain loaded previously.

Parameters:

• problem_path (str): Path to the PDDL problem file.
• optimized_domain: The transformed domain.

Returns:

• optimized_problem: An optimized problem object ready for planning.

Example:

optimized_problem = optimize_problem("task01.pddl", optimized_domain)

Notes:

• Uses unified_planning.shortcuts.ProblemOptimizer.
• Returns None if no optimization is possible.

3. Save Optimized Problem

save-optimized-problem(optimized_problem, output_path)

Description: Writes the optimized problem to disk in standard PDDL format.

Parameters:

• optimized_problem: A PDDL problem that has been optimized.
• output_path (str): Output file path.

Example:

save_optimized_problem(optimized_problem, "optimized_task01.pddl")

Notes:

• Uses unified_planning.io.PDDLWriter for output.
• Outputs a text file with optimized specifications.

4. Validate Optimization

validate-optimization(optimized_problem)

Description: Validates that the optimizations applied to the problem are effective and correct.

Parameters:

• optimized_problem: The problem that has undergone optimization.

Returns:

• bool: True if optimizations are valid, False otherwise.

Example:

is_valid = validate_optimization(optimized_problem)

Notes:

• Uses unified_planning.shortcuts.OptimizationValidator.
• Ensures that optimized goals are reachable and valid.

Example Workflow

# Load and transform domain
optimized_domain = load_and_transform_domain("domain.pddl")

# Optimize problem
optimized_problem = optimize_problem("task01.pddl", optimized_domain)

# Validate optimization
if not validate_optimization(optimized_problem):
    raise ValueError("Optimized problem is invalid")

# Save optimized problem
save_optimized_problem(optimized_problem, "optimized_task01.pddl")

Notes

• This skill set enhances the efficiency of PDDL planning problems through optimization.
• Designed for research in automated planning improvement.