BFGP++

A framework based on Best-First Generalized Planning where solutions are either assembly-like programs, or structured programs that are syntactically terminating.

Installation

1. Prepare the virtual environment for PDDL preprocessing

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt
   pip install -e .

2. Compile the BFGP++ generalized planner

   ./scripts/compile.sh

Running Example

1. Synthesis Mode

Synthesis of a generalized plan with 8 lines for Gripper domain:

./main.bin -m synthesis -l 8 -f domains/gripper/synthesis/ -o gripper -pgp True

The returned strategy, must pick a ball from room A, move to room B, drop the ball and get back to room A for the next ball, until all balls have been moved. This is represented with the following planning program:

0. for(ptr_ball_0++,6)
1. pick(ptr_ball_0,ptr_room_0,ptr_gripper_0)
2. inc(ptr_room_1)
3. move(ptr_room_0,ptr_room_1)
4. drop(ptr_ball_0,ptr_room_1,ptr_gripper_0)
5. move(ptr_room_1,ptr_room_0)
6. endfor(ptr_ball_0++,0)
7. end

2. Validation Mode

Validation of the planning program above in easy, medium and hard Gripper problems:

./main.bin -m validation-prog -p gripper.prog -f domains/gripper/validation/easy/
./main.bin -m validation-prog -p gripper.prog -f domains/gripper/validation/medium/
./main.bin -m validation-prog -p gripper.prog -f domains/gripper/validation/hard/ 

The results will be placed in experiments/gripper/validation/ folder, where all files should contain the input program, the number of input instance, whether the GOAL has been achieved for all instances, and the execution time, e.g., the output for the medium difficulty command is:

[INFO] Parsed domain. [0.000s]
[INFO] Generalized Domain created. [0.000s]
[INFO] Generalized Planning Problem created. [0.032s]
[INFO] Program loaded: [0.000s]
[INFO] Read program:
0. for(ptr_ball_0++,6)
1. pick(ptr_ball_0,ptr_room_0,ptr_gripper_0)
2. inc(ptr_room_1)
3. move(ptr_room_0,ptr_room_1)
4. drop(ptr_ball_0,ptr_room_1,ptr_gripper_0)
5. move(ptr_room_1,ptr_room_0)
6. endfor(ptr_ball_0++,0)
7. end

[INFO] Number of instances: 10
[INFO] GOAL ACHIEVED!
[INFO] Total time: [0.153s]

3. Repair Mode

It is similar to synthesis mode in that it synthesizes a solution for a generalized planning problem, but requires an initial planning program which might be repaired, e.g., find the generalized plan above given the following program, located in domains/gripper/repair/gripper.prog, where only the first loop is programmed:

0. for(ptr_ball_0++,6)
1. empty
2. empty
3. empty
4. empty
5. empty
6. endfor(ptr_ball_0++,0)
7. end

The solution will be found much faster than in synthesis mode for this case. Run the repair mode as follows:

./main.bin -m repair -f domains/gripper/synthesis/ -p domains/gripper/repair/gripper.prog -pgp True 

Input Arguments

The framework has a helper to guide the user with the required input arguments for each running mode. Next we list some of the most important:

• Running modes (-m): synthesis, validation-prog and repair as explained above.
• Theories (-t): cpp (default) and assembler. These are target languages, each with its own set of instructions and constraints. Also, there are 4 target languages for synthesizing planning action models, i.e.,
  • actions_strips, generate STRIPS-like action models,
  • actions_cellular, generate 1D cellular automa transition functions,
  • actions_adl, generate ADL-like action models, limited to universally quantified effects,
  • actions_ram, generate transition functions as planning programs.
• Lines (-l): bounds the program size.
• Evaluation Functions (-e): they are structural-based or performance-based, can be combined and are used to enumerate the solution space in a particular order, e.g.,
  • ed, is the aggregation of Euclidean distances between the resulting states and their corresponding goal conditions,
  • lc, counts the number of loops in a program,
  • mri, counts the number of repeated instructions,
  • ...

References

• Segovia-Aguas, J., Celorrio, S. J., & Jonsson, A (2023a). Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects, AIJ 2023 (under review).
• Segovia-Aguas, J., Ferrer-Mestres, J., & Celorrio, S. J. (2023b). Synthesis of Procedural Models for Deterministic Transition Systems, ECAI 2023.
• Segovia-Aguas, J., Celorrio, S. J., & Jonsson, A (2022a). Computing Programs for Generalized Planning as Heuristic Search, IJCAI-ECAI 2022.
• Segovia-Aguas, J., Celorrio, S. J., Sebastiá, L., & Jonsson, A (2022b). Scaling-up generalized planning as heuristic search with landmarks, SoCS 2022.
• Segovia-Aguas, J., E-Martín, Y., & Celorrio, S. J. (2022c). Representation and Synthesis of C++ Programs for Generalized Planning, Workshop in Generalization in Planning at IJCAI-ECAI 2022.
• Segovia-Aguas, J., Celorrio, S. J., & Jonsson, A (2021). Generalized planning as heuristic search, ICAPS 2021.