README

Note: This is work-in-progress. Current version: 0.1

Content

This repository contains the source files of GOA, a privacy preserving distributed multi-agent planning system. GOA has two execution modes: distributed and centralized.

Setup

0. install Go if you haven't already. (See: https://golang.org/doc/install.)

1. clone the repository:

   mkdir -p $GOPATH/src/github.com/schultet/ \
   && cd $GOPATH/src/github.com/schultet \
   && git clone https://github.com/schultet/goa.git

2. install the required go packages:

   cd $GOPATH/src/github.com/schultet/goa/pkg/
   go get [-u]

3. install the required python modules using pip3:

   pip3 install -r requirements.txt

Running the planner (centralized)

To solve a distributed multi-agent planning problem, execute the following steps:

0. compile the problem description files (factored MA-PDDL) into the required json format

       ./translate.sh <src-folder> <trg-folder>

1. run the planner with search.sh, either specifying all configuration options:

       ./search.sh \ 
       -t <path/of/task/files/> \
       --search "<search-options>" \
       --heuristic [ff|add|addbf|blind|gc] \
       [--threaded --macros]

   or using predefined configuration profiles:

       ./search.sh \ 
       -t <folder-of-task-files> \
       -c <configuration-profile> \
       [--threaded --macros]

   Note: configuration profiles are defined in scripts/configs.py

   Example:

   ./search.sh -t ./experiments/taxi01/ \
       --search '"myconfig -s dmt-gus -h ff -l 100 -c 1.41"' \ 
       --heuristic ff \
       --threaded

Running the planner (distributed)

To run the planner in a distributed fashion, run the following commands in different terminals/shells (one for each agent):

0. Compile the MA-PDDL factors into a local task file for each agent. This is a distributed process in which all planning agents participate. E.g.:

   # Agent 0:
   
   python3 ./translate/translate.py \
   benchmarks/factored/productionsite/2-4-2-1/domain-0.pddl \
   benchmarks/factored/productionsite/2-4-2-1/problem-0.pddl \
   --agent-url tcp://127.0.0.1:3035 \
   --agent-url tcp://127.0.0.1:3036 \
   --agent-id 0 \
   --output test/0.json \
   --json

   # Agent 1 (different terminal):
   
   python3 ./translate/translate.py \
   benchmarks/factored/productionsite/2-4-2-1/domain-1.pddl \
   benchmarks/factored/productionsite/2-4-2-1/problem-1.pddl \
   --agent-url tcp://127.0.0.1:3035 \
   --agent-url tcp://127.0.0.1:3036 \
   --agent-id 1 \
   --output test/1.json \
   --json

1. Run the planner using the compiled task-files (.json):

   # Agent 0:
   
   go run cmd/distributed/main.go \
   --problem test/0.json \
   --agent "0 127.0.0.1 3035" \
   --agent "1 127.0.0.1 3036" \
   -s "mafs -s mafs-g  -h ff -l 1" \
   --heuristic "ff" \
   --planlimit 1

   # Agent 1 (different terminal):
   
   go run cmd/distributed/main.go \
   --problem test/1.json \
   --agent "0 127.0.0.1 3035" \
   --agent "1 127.0.0.1 3036" \
   -s "mafs -s mafs-g  -h ff -l 1" \
   --heuristic "ff" \
   --planlimit 1

Docker

To use our environment, you can build and run a GOA image:

0. Build the docker image:

   docker build -t goa .

1. Run the container:

   docker run -d -t --name my_goa goa

2. Execute planner commands, e.g.:

   # Translate MA-PDDL files
   docker exec -it my_goa /bin/bash translate.sh <path/to/pddl/> <path/to/compiled/task>
   
   # Search for a plan
   docker exec -it my_goa /bin/bash search.sh -t <path/to/compiled/task> -c mafs

Note: you can also use the Docker image to plan in a distributed fashion. In this case, you need to docker run two GOA containers, each representing a single agent. In this case, you have to map host-container ports accordingly, such that one agent uses port 3035 and the other port 3036 (as an example).

Running tests

    go test -v <package>

or

    go test ./pkg/...

Contact

• Tim Schulte schultet@informatik.uni-freiburg.de

References

• https://doi.org/10.6094/UNIFR/221778