# Role
Your task is to identify the different types of objects which exist and are relevant in a domain. [Further task details.]
# Example
[Chain-of-Thought Example]
# Task
## Domain
The AI agent here is a logistics planner that has to plan to transport packages within the locations in a city through a truck and
between cities through an airplane. In a city, all the locations are connected. Similarly, cities are directly connected allowing airplanes
to travel between them. Also, there is no limit to how many packages a truck or plane can carry.
Currently, I’ve got four packages to ship. . .
## Types
First, we need types related to locations and transportation.
‘‘‘
- city: Each city contains an airport and other locations.
- location: Places within cities trucks can visit.
- airport: A location where planes land and take off.
‘‘‘
Next, we consider the transportation vehicles.
‘‘‘
- plane: A type of vehicle used for transporting packages between cities.
- truck: A type of vehicle used for transporting packages within a city.
‘‘‘
Lastly, we need the to define the items being transported.
‘‘‘
- package: An item that needs to be transported from one location to another.
‘‘‘

# Role
Your task is to identify the different types of objects which exist and are relevant in a domain. [Further task details.]
# Example
[Chain-of-Thought Example]
# Task
## Domain
The AI agent here is a logistics planner that has to plan to transport packages within the locations in a city through a truck and
between cities through an airplane. In a city, all the locations are connected. Similarly, cities are directly connected allowing airplanes
to travel between them. Also, there is no limit to how many packages a truck or plane can carry.
Currently, I’ve got four packages to ship. . .
## Types
First, we need types related to locations and transportation.
‘‘‘
- city: Each city contains an airport and other locations.
- location: Places within cities trucks can visit.
- airport: A location where planes land and take off.
‘‘‘
Next, we consider the transportation vehicles.
‘‘‘
- plane: A type of vehicle used for transporting packages between cities.
- truck: A type of vehicle used for transporting packages within a city.
‘‘‘
Lastly, we need the to define the items being transported.
‘‘‘
- package: An item that needs to be transported from one location to another.
‘‘‘

# Role
Your task is to identify what actions an AI Agent would have available in a domain. [Further task details.]
# Example
[Chain-of-Thought Example]
# Task
## Domain
The AI agent here is a logistics planner that has to plan to transport packages. . .
Currently, I’ve got four packages to ship. . .
##Types:
- object: Everything is an object
- city: Each city contains. . .
- location: Places within cities. . .
- airport: A location where planes. . .
- vehicle: Vehicles transport packages.
- truck: A type of vehicle. . . .
- plane: A type of vehicle. . .
- package: An item that needs to. . .
##Actions
###Package related actions
Packages need to be loaded onto vehicles and unloaded at the destination.
‘‘‘
load package
A package is loaded onto a vehicle at a location. Requires that the package and the truck to be at the same location. Example:
package 1 is loaded onto truck 1 at location 1.
‘‘‘
‘‘‘
unload package
A package is unloaded from a vehicle at a location. Requires the package to be on the vehicle and the vehicle to be at the destination
location. Example: package 2 is unloaded from plane 1 at airport 1.
‘‘‘
[Further headers and actions.]

# Role
You are defining the preconditions and effects (represented in PDDL format) of an AI agent’s actions. [Further task details.]
# Example
[Chain-of-Thought Example]
# Task
## Domain
The AI agent here is a logistics planner that has to plan to transport packages. . .
Currently, I’ve got four packages to ship. . .
##Types:
- object: Everything is an object
- city: Each city contains. . .
- location: Places within cities. . .
- airport: A location where planes. . .
- vehicle: Vehicles transport packages.
- truck: A type of vehicle. . . .
- plane: A type of vehicle. . .
- package: An item that needs to. . .
## Future actions
The following actions will be defined later and together they make up the entire domain:
- load package: A package is loaded. . .
- unload package: A package is unloaded. . .
[Remaining actions.]
## Action
load package
A package is loaded onto a vehicle at a location. Requires that the package and the truck to be at the same location. Example:
package 1 is loaded onto truck 1 at location 1.
### Available Predicates
No predicate has been defined yet.
### Action Parameters
First, we need to know which vehicle and package are being loaded:
‘‘‘
- ?v - vehicle: The vehicle being loaded.
- ?p - package: The package being loaded.
‘‘‘
Lastly, we need to know where the loading is taking place:
‘‘‘
- ?l - location: The location where the loading happens.
‘‘‘
### Action Preconditions
To be able to load at ?l it’s specified that:
1: The vehicle is at the location.
2: The package is at the location.
For this we need a predicate:
- “at” to define where something is. This should work for both packages and vehicles, so let’s define “(at ?o - object ?l - location)”
Let’s specify this in PDDL:
‘‘‘
(and ; All these have to hold
(at ?v ?l) ; The vehicle is at the location
(at ?p ?l) ; The package is at the location
)
‘‘‘
### Action Effects
So, what happens when the action is performed?
- The package is no longer at the location
- The package is instead inside the vehicle
For this, we need a new predicate
- “l(fā)oaded” to specify when and where a package is loaded. “(loaded ?p - package ?v - vehicle)”
Specifying this in PDDL we get:
‘‘‘
(and ; All these have to hold
(not (at ?p ?l)) ; The package is not at the location
(loaded ?p ?v) ; The package is instead loaded in the vehicle
)
‘‘‘
### New Predicates
We used two new predicates. Those have to be specified and re-iterated:
‘‘‘
- (at ?o - object ?l - location): true if the object ?o (a vehicle or package) is at the location ?l
- (loaded ?p - package ?v - vehicle): true if the package ?p is loaded in the vehicle ?v
‘‘‘

# Role
Your task is to estimate the initial state and the goal state for a PDDL problem based on a domain description and the available
actions. [Further task details.]
# Example
[Chain-of-Thought Example]
# Task
## Domain
The AI agent here is a logistics planner that has to plan to transport packages. . .
Currently, I’ve got four packages to ship. Two are in a London storage and the rest in Paris. Those from London should be
sent to Addr1 in Berlin and to Addr2 in Paris. Those from Paris should both be moved to the London storage.
##Types:
- object: Everything is an object
- city: Each city contains. . .
- location: Places within cities. . .
- airport: A location where planes. . .
- vehicle: Vehicles transport packages.
- truck: A type of vehicle. . . .
- plane: A type of vehicle. . .
- package: An item that needs to. . .
## Predicates
- (at ?o - object ?l - location): true if the object ?o (a vehicle or package) is at the location ?l
- (loaded ?p - package ?v - vehicle): true if the package ?p is loaded in the vehicle ?v
[Further predicates.]
## Object Instances
There are four packages. The first two start in London, and the remaining two start in Paris:
‘‘‘
- L1 - package: The first London package
- L2 - package: The second London package
- P1 - package: The first Paris package
- P2 - package: The second Paris package
‘‘‘
[Further object instances.]
## State
The London packages all start in the London storage:
‘‘‘
(at L1 LStorage): The first London package location
(at L2 LStorage): The second London package location
‘‘‘
[Further initial predicates.]
## Goal
The goal is for L1 to go to Addr1 and for L2 to be delivered to Addr2, as well as for both P1 and P2 to be transported to London
storage. Here’s how we can define the goal:
‘‘‘
(and ; All these have to hold
(at L1 Addr1)) ; L1 is delivered
(at L2 Addr2)) ; L1 is delivered
(at P1 LStorage)) ; L1 is delivered
(at P2 LStorage)) ; L1 is delivered
)
‘‘‘

NL2Plan: Robust LLM-Driven Planning from Minimal Text Descriptions
https://arxiv.org/pdf/2405.04215