MORE EXPRESSIVE PLANNING GRAPH EXTENSION

Joseph Zalaket and Guy Camilleri

epartement informatique, Universit

e Saint-Esprit de Kaslik,B.P. 446 Jounieh, Liban

IRIT CSC, Universit

e Paul Sabatier,118 route de Narbonne, 31062 Toulouse, France

Keywords:

AI Planning, planning graph, numerical planning, functional planning.

Abstract:

The most of the real world problems have characteristics that can not be handled like pure propositional

expressions. Many classical planners have been extended to deal with more expressive problems which require

features like temporal actions, numeric functions, conditional effects, ... etc. Extensions have been made to

planners like (D. and D., 1999), MetricFF02, (Do and Kambhampati, 2001), which use the planning graph as

their base structure. However, each of these extensions deals with the one or the other of the features required

for more expressive planning problems without being able to integrate all or at least an important combination

of these features. In this paper, we propose an extension to the planning graph structure to deal with more

expressive planning problems that can contain real word data characteristics, such as: symbolic, temporal and

numeric data.

1 INTRODUCTION

The domain independent-planning still a complex

problem. The most of the domain-independent plan-

ners are able to solve symbolic planning problems

but, real-world problems necessity dealing with more

complex types of data such as temporal and numeric

data. This kind of data requires features like numeric

function and temporal actions to be added and sup-

ported by the planning process. Some planners have

been extended to deal with some real world knowl-

edge, and thus, to support one or the other of these

features. Planners like SAPA (Do and Kambham-

pati, 2001) and Metric-FF (Hoffmann, 2002) have

extended their heuristic search to deal with numeric

knowledge. However, to combine multiple features

which can be necessary to solve some more real-

world problems in a domain-independent planner still

a complex task. In the other hand, many planners

are using the planning graph structure as their base

for exploring the state space due to its compact struc-

ture. After its introduction in Graphlan (Blum and

Furst, 1995) as its basic expansion graph, the plan-

ning graph became the subject of multiple extensions

to solve directly some more expressive problems like

temporal Graphplan (TGP) (Smith and Weld, 1999)or

to serve as a base to calculate heuristic function for

such problems like in like SAPA (Do and Kambham-

pati, 2001) and Metric-FF (Hoffmann, 2002). In this

paper we propose a general extension to the planning

graph, which allow it to deal with functions appli-

cation. The functions that we propose can be incor-

porated into the action effects and/or the action pre-

conditions. This extension allows the planning graph

to support all types of features that are necessary to

deal with more expressive planning problems, such as

problems that contain numeric and temporal knowl-

edge. In our extension we introduce the application

of functions during the planning graph expansion, in

a way that the results of these functions can be in-

corporated within the nodes of this graph and thus

they can be treated as any other type of nodes. Us-

ing functions application will allow us to deal with

any combination of features required for solving some

real world problems as the functions that we propose

can be written in any programming language without

restrictions to their implementation. Therefore, the

extended planning graph can be used directly as an

expansion phase graph as in Graphlan-like planners

(Blum and Furst, 1995), (Smith and Weld, 1999) to

424

Zalaket J. and Camilleri G. (2007).

MORE EXPRESSIVE PLANNING GRAPH EXTENSION.

In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, pages 424-427

 SciTePress

solve planning problems or it can also be used as the

base graph to calculate heuristics as in FF-like plan-

ners (Hoffman, 2001), (Hoffmann, 2002).

We ﬁrst present the motivation for our work in sec-

tion 2. We then present an overview of the planning

graph structure in section 3. We present the extension

to the deﬁnition of the planning problem in section

4. We explain the planning graph extension to han-

dle numerical variables and functions application in

section 5. We ﬁnally conclude this paper in section 6.

2 MOTIVATION

Many extensions have tried to adapt the planning

graph to solve more expressive planning problems

than those that can be expressed in pure propositional

STRIPS language. Real-world problems have com-

plex characteristics. Most of these characteristics can

be modeled with the PDDL3.0 (A. and D., 2005) lan-

guage which is the mostly used by recent planners.

Therefore, PDDL3.0 gives the possibility of express-

ing optimization criterion such that maximization or

minimization of resource consumption, without be-

ing able to represent all king of resource handling.

The extension that we propose to the planning graph

structure aims to allow this graph to supprot the exe-

cution of functions. Functional Strips (Geffner, 1999)

and FSTRIPS (Schmid et al., 2000) have studied the

integration of functions to solve planning problems.

Functional Strips and FSTRIPS use functions instead

of relations, which allow reducing the generated lit-

erals in a planning problem. In our work we intro-

duce the function application which allow us to rep-

resent complex numerical and conditional effects, as

well as complex preconditions and goals. For exam-

ple, to plan the motion of a robot that is expected

to travel from an initial position to a target position

in the presence of physical obstacles in its environ-

ment. To avoid collisions between the robot and the

obstacles we have to use trigonometric functions that

allow the robot to follow a circular path in the en-

vironment (BAK M. and O., 2000). Trigonometric

expressions are simple to be solved by classical pro-

gramming languages, but they can not be expressed

in classical AI planning languages like PDDL3.0. For

this reason our extension to th planning graph is based

in the integration of functions written in any program-

ming language. Inspiring from the representation of

functions in object-oriented databases. We add to the

problem deﬁnition written in PDDL3.0 the declara-

tion of external functions by specifying their execu-

tion path. In addition to their capabilities of solving

numerical problems like the circular movement of a

robot, external function are able to handle all kind of

conditional and probabilistic effects. To avoid all kind

of side effects we restrict all the formal parameters of

a function to be constant, which mean a function can

only return a value without updating any state vari-

able. This returned value is affected to one state vari-

able at a time.

The use of non-bijective functions requires that their

application in the search space could be done only in

forward. As the planning graph is constructed in pro-

gression, thus it can support the application of this

kind of functions.

3 PLANNING GRAPH

Because The planning graph introduced in Graphplan

(Blum and Furst, 1995) is the basis of our extension,

we brieﬂy summarize the planning graph structure

without detailing how it is used by the Graphplan al-

gorithm.

A planning graph consists of a sequence of levels,

where level 0 corresponds to the initial state. Each

level contains a set of literals and a set of actions. The

actions of a level are those that have their precondi-

tions satisﬁed and mutually consistent by the literals

of the same level. The literals of a next level i+1 are

the effects of the applicable actions of previous level

i. The literals that satisfy the preconditions of action

instances at the same level are connected with these

actions via direct edges. Also, the action instances

are connected via direct edges with their literals

effects at the next level.

The planning graph is expanded up to a level in

which all the literals of the goal are present and con-

sistent. Then, a solution extraction phase can start

in a way that for each sub-goal at the last level n, it

tries to ﬁnd an action instance at level n-1 that has

this sub-goal as an add effect. The preconditions of

the selected action instances become the new set of

sub-goals at level n-1 and so on until reaching the ini-

tial state (level 0).

4 PLANNING PROBLEM

EXTENSION

Each distinct instance of the world is called a state,

denoted by s. The set of all possible states is called a

state space S. S is formed by two disjoint sets: a set

of logical propositions P and the set of numeric vari-

ables N, such that S =

{

(p, n)/p ∈ P,n : N → R

}

We denote by P(s) the subset of logical propositions

of a state s and by N(s) the subset of its numeric vari-

ables.

A state transition functions t transforms s into s

′

t(s,a) by 3 ways:

• Adds logical propositions to P(s)

• Removes logical propositions from P(s) (when

propositions become false)

• Assigns new values to existing numeric variables

in N(s)

An action a is deﬁned as a tuple

arg, pre,ef f

where:

• arg is the list of arguments made by variables

which represent constant symbols and/or numeric

variables in N.

• pre = pre

∪ pre

is the list of preconditions.

pre

deﬁned over P are propositional pre-

conditions

pre

are numeric preconditions, such that:

∀c ∈ pre

, c = (nθg), where n ∈ N, θ ∈

{

<,≤,=,>,≥

}

and g is an external function or

an expression.

Deﬁnition-1: An expression is an arithmetic ex-

pression over N and the rational numbers, using

the operators +, -, * and /.

Deﬁnition-2: An external function is written in a

high level programming language on the form of

type f(n

,,n

) where arguments n

,,n

∈

N. The function and all its arguments (if exist) are

declared as constants in a way that the function

calculate and return a value without affecting any

numeric state variable.

• e f f = ef f

∪ ef f

is the list of effects.

ef f

deﬁned over P are positive and negative

propositional effects that add or remove litterals.

ef f

are numeric effects, such that:

∀e ∈ ef f

, e = (n := g), where n ∈ N and g is an

external function or an expression.

A planning problem is deﬁned as:

1. A nonempty state space S, which is a ﬁnite or

countably inﬁnite set of states.

2. For each state s ∈ S, a ﬁnite set of applicable ac-

tions A(s).

3. A state transition function f that produces a state

′

= f(s, a) ∈ S for every s ∈ S and a ∈ A(s).

4. An initial state s

∈ S.

5. A set of goal conditions G satisﬁed in s

∈ S.

The set of goal conditions G = G

∪ G

, where:

deﬁned over P are propositional goals and G

are numeric goals that should be satisﬁed in the

goal state s

, such that: ∀l ∈ G

, l = (nθg), where

n ∈ N, θ ∈

{

<,≤,=,>,≥

}

and g is an external

function or an expression.

5 PLANNING GRAPH

EXTENSION

In this section we present an extension to the plan-

ning graph structure to support numeric variables han-

dling and external functions executions. We start the

creation of the planning graph by instantiating the

propositional variables of all the actions of the plan-

ning problem. This instantiation is done once before

the graph expansion by substituting the propositional

variables by all the combinations of the world ob-

jects. Therefore, none of numeric variables are substi-

tuted before the planning graph expansion process.//

We initialize the fact level 0 to the initial state of the

planning problem then we loop while the goal is not

satisﬁed at the current fact level which is initially the

level 0. For each one of the previously instantiated

actions, we test ﬁrst if all the propositional precondi-

tions pre

exist in the current level. If the ﬁrst test

is successful then we proceed to test the numerical

conditions pre

otherwise, we skip this second test.

If the numerical preconditions pre

of the action are

satisﬁed in the current level we add this action to the

applicable actions list of the current action level. For

rotate(Position p1, Position p2, robot r)

pre: p1 6= p2, at(r, p1), fcos(cont short θ) ≺ 1

eff: at(r, p2), ¬at(r, p1), θ :=frotation(const short θ)

Figure 1: An action with external functions.

example, in the action rotate (ﬁg.1) the ﬁrst condition

p1 6= p2 is tested at the propositional instantiation of

the actions as the condition concerns only world ob-

jects which are not covered by a relation (not a literal)

or an external function. This kind of tests avoids in-

stantiating non useful actions from the beginning. The

second condition at(r, p1) is a literal so it belongs to

the propositional preconditions pre

and it is tested

ﬁrst for choosing the applicable actions at the current

level. Suppose that at(r, p1) exists in the current fact

level thus, the algorithm will proceed by testing the

external function ”fcos()” for the value of the formal

parameter θ passed from the current fact level. If the

value returned by ”fcos()” is less than 1, then this ac-

tion will be added to the applicable actions of the cur-

rent action level.

At each action level, the numeric variables of an

action are instantiated just before the action applica-

tion at this current level. This instantiation is done

by replacing the variables by the corresponding val-

ues from the current fact level. This technique of

”‘on the ﬂy”’ instantiation allows avoiding the ﬂood

of numeric values and can be used for continuous and

discrete numeric values. Only the variables that are

parameters of external functions and the right hand

side numerical variables of expressions are instanti-

ated from the current fact level. All these on the ﬂy

instantiated variables are added implicitly to the ac-

tion preconditions by direct edges. These edges to

implicit preconditions will allow us to follow the trace

of the functions application within the planning graph

during the extraction process. We run through the

planning graph during the extraction of a plan with-

out making difference between implicit and explicit

preconditions. This technique allows us to use black

box functions because we are only concerned by the

parameters and the returned value of a function to fol-

low its trace through edges during the planning pro-

cess. After all actions are instantiated propositionally

and numerically at the current level, the application of

these actions can take place.

The application of actions at the current action

level will lead to the creation of the next fact level.

Positive propositional effects in ef f

of an applica-

ble action a are pointed as added by a to the next fact

level and negative effects are pointed as deleted by

a from the current fact level using edges. For each

numerical variable which is assigned to an external

function or to an expression at the numerical effects

in ef f

, the old values of these variables are pointed

as deleted by a from the current fact level and the

new values returned by functions or expressions are

pointed as added by a to the next current level. This

organization of numerical effects will allow a uniform

representation for all types of values and thus, the run

through the graph will not be affected by the function

results. From now on, the next fact level becomes the

current fact level and the loop continue until the goal

conditions are found at this current fact level or until a

sufﬁcient condition indicates that the problem has no

solution.

6 CONCLUSION

In this paper we proposed an extension to the plan-

ning graph construction algorithm in order to allow

it supporting the application of functions written in a

given programming language. This extension allows

the application of functions in a planning graph irre-

spective of their contents. As the planning graph is

build in a forward pass the parameters and the results

of the functions can be registered consequently during

the graph construction process. During the extraction

of a plan in a backward pass the algorithm is only con-

cerned by the graph nodes from which it ﬁnds a solu-

tion by following the direct edges of the graph. We

have proposed a method to instantiate numeric vari-

ables dynamically, in a way that the instantiation of

these variables is done step by step during the graph

expansion process. This technique resolves the prob-

lem of the ﬂood that can be generated by the numeric

domains and allows handling continuous and discrete

numerical domains during a planning task.

REFERENCES

A., G. and D., L. (2005). Plan constraints and preferences

for PDDL3. Technical Report Technical report, R.T.

2005-08-07, Dept. of Electronics for Automation, U.

of Brescia, Brescia, Italy.

BAK M., P. N. and O., R. (2000). Path following mobile

robot in the presence of velocity constraints. Tech-

nical report, Kongens Lyngby, Denmark : Technical

University of Denmark.

Blum, A. L. and Furst, M. L. (1995). Fast planning through

planning graph analysis. Proceedings of the 14th In-

ternational Joint Conference on Artiﬁcial Intelligence

(IJCAI95), pages 1636–1642.

D., S. and D., W. (1999). Temporal planning with mutual

exclusion reasoning. 16th International Joint Confer-

ence on Artiﬁcial Intelligence (IJCAI99).

Do, M. B. and Kambhampati, S. (2001). Sapa: A domain-

independent heuristic metric temporel planner. In

Proceedings of the European Conference on Planning

(ECP 2001).

Geffner, H. (1999). Functional strips: A more ﬂexible lan-

guage for planning and problem solving. Logic-based

AI Workshop, Washington D.C.

Hoffman, J. (2001). FF: The fast-forward planning system.

AI Magazine, 22:57 – 62.

Hoffmann, J. (2002). Extending FF to numerical state vari-

ables. In Proceedings of the 15th European Confer-

ence on Artiﬁcial Intelligence, Lyon, France.

Schmid, U., M

uller, M., and Wysotzki, F. (2000). Integrat-

ing function application in state-based planning. Sub-

mitted manuscript.

Smith, D. and Weld, D. (1999). Temporal planning with

mutual exclusion reasoning. In Proceedings of the

Sixteenth International Joint Conference on Artiﬁcial

Intelligence (IJCAI-99).