Contributors: adamgreen, Jan Dolejsi,
The domain syntax in PDDL2.1 extended upon version 1.2 to include two key new features,
functions which are referred to as numeric fluents. Additional new requirements were specified on top of the 1.2 spec to allow older planners to identify that they could not solve these neweer domains.
(define (domain rover-domain) (:requirements :durative-actions :fluents :duration-inequalities) (:types rover waypoint) (:predicates ... ; Predicates omitted ) (:functions (battery-amount ?r - rover) (sample-amount ?r - rover) (recharge-rate ?r - rover) (battery-capacity) (sample-capacity) (distance-travelled) ) (:durative-action move :parameters (?r - rover ?fromwp - waypoint ?towp - waypoint) :duration (= ?duration 5) :condition (and (at start (rover ?rover)) (at start (waypoint ?from-waypoint)) (at start (waypoint ?to-waypoint)) (over all (can-move ?from-waypoint ?to-waypoint)) (at start (at ?rover ?from-waypoint)) (at start (> (battery-amount ?rover) 8))) :effect (and (decrease (fuel-level ?t) (* 2 #t)) (at end (at ?rover ?to-waypoint)) (at end (been-at ?rover ?to-waypoint)) (at start (not (at ?rover ?from-waypoint))) (at start (decrease (battery-amount ?rover) 8)) (at end (increase (distance-travelled) 5)) ) ) ... ; Additional actions omitted )
Requirements are similar to import/include statements in programming languages, however as PDDL is a kind of declarative language, it is a
:requirement as a given planner is “required” to facilitate some aspect of the language.
:requirements can be specified through a space separated list e.g.
(:requirements :strips :adl :typing)
The following is a list of requirements that were added by PDDL2.1 to the language of PDDL.
(:functions (<variable_name> <parameter_name> - <object_type>) ... (<variable_name> <parameter_name> - <object_type>) )
A numeric fluent, similar to a predicate, is a variable which applies to zero or more objects and maintains a value throughout the duration of the plan. It is declared with a name followed by the object type to which it applies. e.g.
(:functions (battery-level ?r - rover) )
Means that every rover object in the domain has a variable which maintains a value for
battery-level. A function can apply to zero or more objects, meaning we could also use it to represent a numeric value between two values, for example a distance.
(:functions (distance ?wp1 - waypoint ?wp2 - waypoint) )
Numeric fluents can be altered through the effects of both
durative-actions. There are a number of supported effects for numeric fluents.
(+ (sample-capacity) (battery-capacity))
(- (sample-capacity) (battery-capacity))
(/ (sample-capacity) (battery-capacity))
(* (sample-capacity) (battery-capacity))
Prefix notation is used to represent maths operations on numeric fluents. We can see the four primarily binary operations we can perform on numeric fluents. In all of these cases, to convert to infix notation we place the operator between the name of the two fluents.
(increase (battery-level ?r) 10)
An increase effect increases the value of a numeric variable by the given amount. It is possible to use another numeric variable as the increase value for example.
(increase (battery-level ?r) (charge-available - ?solarpanel))
(decrease (battery-level ?r) 10)
A decrease effect decreases the value of a numeric variable by the given amount. It is possible to use another numeric variable as the decrease value for example.
(decrease (battery-level ?r) (power-needed-for-work - ?task))
(assign (battery-level ?r) 10)
An assign effect assigns the value of a numeric variable to the given amount. It is possible to use another numeric variable as the assign value for example.
(assign (battery-level ?r) (max-charge ?r))
(scale-up (battery-level ?r) 2)
A scale up effect increases the value of the numeric variable by the given scale factor. The scale factor can be another numeric variable.
(scale-up (battery-level ?r) (charge-rate ?r))
(scale-down (battery-level ?r) 2)
A scale down effect decreases the value of the numeric variable by the given scale factor. The scale factor can be another numeric variable.
(scale-down (battery-level ?r) (consumption-rate ?r))
(:durative-action <action_name> :parameters (<arguments>) :duration (= ?duration <duration_number>) :condition (logical_expression) :effect (logical_expression) )
A durative action is an action which represents an action which takes an amount of time to complete. The amount of time is expressable as either a value or as an inequality (allow for both fixed duration and ranged duration actions). Similar to traditional
actions we have
effects, but it should be noted that the keyword in durative actions is
This semantic change is designed to represent that a durative action may not just condition when the action starts, but may have conditions which need to be true at the end or over the duration of the action. A good example of this can be found in flight planning, where an action
fly requires that a runway be free at the start and end of an action, in order for the plane to take off and land, whilst the runway does not need to be free whilst the plane is flying.
:parameters (argument_1 ... argument_n)
:parameters (?s -site ?b - bricks)
The parameters defines the type of object we’re interested in. Note that a parameter can take any type or subtype. If we have for example three instances of site such as
s3 and we have two instances of bricks
b2, our planner considers all possible actions such as:
(BUILD-WALL s1 b1)
(BUILD-WALL s2 b1)
(BUILD-WALL s3 b1)
(BUILD-WALL s1 b2)
(BUILD-WALL s2 b2)
(BUILD-WALL s3 b2)
Therefore it is not up to us as a user to specify the specific object to which an action applies but rather the type of objects to which the action applies.
In this case, when we build a wall we need to know what bricks we’re using to build it and where we’re building it. Our actions are specific to the problem we’ve chosen to consider and model, therefore there might additional things that other user want/need to model that this model doesn’t.
Your domain and problem should only consider and model the aspects of the problem which you’re trying to solve. For example here we haven’t modelled the person who’s actually going to perform the work, but maybe if we were the manager of a larger building site we might want to and therefore we would need to adapt my models.
Support: Universal (in temporal planners)
:duration (= ?duration <duration_number>)
:duration (> ?duration <duration_number>)
:duration (< ?duration <duration_number>)
:duration (and (> ?duration <duration_number>) (< ?duration <duration_number>))
A duration can be expressed as either a fixed value or an inequality. It is also possible to express duration as the value of a Numeric Fluent, which means an action such as
move can have a
duration dependent on say
distance between two points.
:duration (= ?duration 10)
Support: Universal - in temporal planners
A condition is a logical and temporal expression which must be met in order for a durative action to execute. Because a durative action occurs over time, we may wish to express that additional conditions be met for the duration or end of the action, not just the start. This gives rise to three new keywords
at end and
An expression or predicate with
at start prefixed to it, means that the condition must be true at the start of the action in order for the action to be applied. e.g.
(at start (at ?rover ?from-waypoint))
at start the given
from-waypoint. Confusingly in this particular domain, the
at is a predicate representing the location of an object
at a point, whilst
at start is a keyword.
at start is usually applied per predicate.
An expression or predicate with
at end prefixed to it, means that the condition must be true at the end of the action in order for the action to be applied e.g.
(at end (>= (battery-amount ?rover) 0)
In essence we are saying that whilst this fact doesn’t have to be true at the start or during the action, it must be true at the end. In this case, we’re expressing that the battery amount at the end of the action must be greater than zero.
An expression or predicate with an
overall prefixed to it, means that the condition must be true throughout the action, including at the start and end. e.g.
(over all (can-move ?from-waypoint ?to-waypoint))
At all points in the execution of the action the given expression must evaluate to true. In the case above, we are expressing that it must be possible to move from the
from waypoint to the
to waypoint all the way through the action. I.e. we don’t want to get half way through the action to find that after a certain point a path has become blocked.
Support: Universal - in temporal planners
An effect similar to in traditional actions, is a condition which is made true when an action is applied. Note that the effect is always more restrictive and typically only allows
not as logical expressions.
Temporal expressions, such as
at start and
at end are available, however,
over all is typically not used. because it’s not common to express a boolean effect which is true over the duration of the action. Instead you would set it to true at the start, using an
at start and set it to false at the end using
:effect (and (at start (not (at ?rover ?from-waypoint))) (at start (decrease (battery-amount ?rover) 8))) (at end (at ?rover ?to-waypoint)) (at end (been-at ?rover ?to-waypoint))
The above effect is saying that
at start the rover can no longer be considered as being at the
from waypoint, and that
at end it can now be considered as being at the
to waypoint. It also adds a second predicate
been-at which indicates that at some point the rover has visited the given waypoint.
(increase (fuel ?tank) #t)
(decrease (battery ?battery) (* 5 #t))
Continuous effects are an additional way of defining effects, which merit their own dedicated section. A continous effect defines an effect on a numeric variable which is continuous through the application of a durative action. Put simply, it defines that a variable changes continously over the duration of the action.
We can see these kind of effects in real world fuel and battery problems. As we drive for longer, we consume more fuel, therefore can be modelled using a continous effect. Furthermore, continuous effects allow planning models to consider the application of an action prior to the termination of a durative action.
Imagine we have a
recharge action which charges 1% of a battery for every unit of time, we would model it with this line within the effects block of an action
(increase (battery ?b) #t)
#t acts a numeric variable representing the current point in time within the action so a function such as
(* 2 #t) we are saying, for every time unit of an action, the value of the change is multiplied by 2.
Lets say that our recharge action is half way through being applied and by that point the battery has recharged enough for us to apply another action which may consume some battery, without continuous effects, we would most likely apply the change at the end of the recharge action, meaning any action which wished to consume battery would have to wait for recharge to complete before it can be applied
Without Continuous effects (plan length: 15) b=0 |--(recharge)--| |--(drive--| (b+=10) (b>5) (b-=5) With Continuous effects (plan length: 10) b=0 |--(recharge)--| (b+=#t) |--(drive)--| (b>5) (b-=5)
With continous effects, the battery has recharge to a level that is satisfies the drive action’s precondition half way through recharging. Without, we have to wait until the recharge effect of the recharge action is applied, before we can apply the drive action, resulting in a longer (and arguably sub-optimal) solution.
- PDDL - The Planning Domain Definition Language, [Ghallab, M. Howe, A. Knoblock, C. McDermott, D. Ram, A. Veloso, M. Weld, D. Wilkins, D.]
- PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains, [Fox, M. Long, D.]
- PDDL Examples
- OPTIC - Optimising Preferences and Time Dependent Costs