Information Technology Reference
In-Depth Information
In all cases
Room1
is substituted with
lab
and
Room2
is substituted with
kitchen
.
Furthermore, in the first situation
D
is substituted with
door1
and the precondition is
possibly-derivable with respect to the agents domain model and the set of open-ended lit-
erals
. In the second case,
D
is substituted with
door2
and the precondition is possibly-
derivable with respect to the set of open-ended literals
{}
.Inthelast
case,
D
is not instantiated and the precondition is possibly-derivable with respect to the
set of open-ended literals
{
connect(lab,D,kitchen), open(D)
}
. Thus, in this ex-
ample the open-ended domain model ACogDM can tell the robot agent that it can cross
door1
,orcross
door2
if it can find out that
door2
is open, or cross another door
D
if it finds another door
D
that connects the lab and the kitchen and is open. In this way,
ACogDM can enable a planner to reason about possible and relevant extensions of its
domain model.
{
open(door2)
}
2.3
Planning Algorithm
In this section, we present the key conceptualizations and the algorithm of the proposed
planning system.
Preliminaries.
Dependencies between open-ended literals need to be considered by
the generation of knowledge acquisition plans. For example, for the set of open-ended
literals
one cannot independently acquire an instance of
mug(X)
and an instance of
color(X,red)
, because one needs to find an instance
of
X
which represents a mug as well as a red object. Let
l
1
,l
2
be literals that are part
of a precondition
p
in disjunctive normal form and
var
(
l
)
denote the set of variables of
a literal
l
.
l
1
and
l
2
are called
dependent
(denoted as
l
1
↔
{
mug(X),color(X,red)
}
l
2
)iff
l
1
and
l
2
are part of
the same conjunctive clause and
((
var
(
l
1
)
∩
var
(
l
2
)
=
∅
)
,or
l
1
and
l
2
are identical, or
(
l
2
))
.
Agents (e.g., robots) can usually acquire information from a multitude of sources.
These sources are called
external knowledge sources
. While submitting questions to
external databases or reasoning components might be “simply” achieved by calling ex-
ternal procedures, submitting questions to other sources (e.g., perception), however,
involves additional planning and execution. For the purpose of enabling ACogPlan to
generate knowledge acquisition plans, we use a particular kind of task, namely a
knowl-
edge acquisition task
. A Knowledge acquisition task has the form
det
(
l,I,C,ks
)
where
l
is a literal,
I
is the set of all derivable instances of
l
,
C
is a set of literals that
are dependent on
l
,and
ks
is a knowledge source. In other words,
det
(
l,I,C,ks
)
is
the task of acquiring an instance
lσ
of
l
from the knowledge source
ks
such that
lσ /
∃
l
3
l
1
↔
l
3
∧
l
3
↔
∈
I
(i.e.,
lσ
is not already derivable) and for all
c
C
an instance of
cσ
is derivable. For
example,
det(open(kitchen door),
∅
,
∅
,percept)
is the task of determining
whether the kitchen door is open by means of perception. Furthermore,
det(mug(X),
[mug(bobs mug)],[in room(X,r1),red(X)],hri(bob))
constitutes the
task of finding a red mug which is located in the room
r1
and is not Bob's mug
by means of human robot interaction with Bob. Like for other tasks, we can define
HTN methods that describe how to perform a knowledge acquisition task. For example,
Fig. 2 shows a simple method for the acquisition task of determining whether a door is
∈
