From: AAAI-84 Proceedings. Copyright ©1984, AAAI (www.aaai.org). All rights reserved.
FOCUSING IN PLAN RECOGNITION
Norman
Department
of Computer
and Information
The University
Amherst,
F. Carver,
Victor R. Lesser, Daniel L. McCue*
*Digital Equipment
Science
Massachusetts,
Merrimack,
01003
future
ABSTRACT
A plan recognition
architecture
is presented
which
exploits
application-specific
heuristic
knowledge
to
quickly focus the search to a small set of plausible
plan interpretations
from the very large set of possible
The heuristic knowledge is formalized
interpretations.
maintenance
system
where
for
use
in
a truth
heuristic
assumptions
and
their
interpretation
justifications
are
recorded.
By
formalizing
this
knowledge,
the system is able to reason about the
behind
the
current
state
of
the
assumptions
This makes intelligent backtracking and
interpretations.
error detection possible.
An
issue for plan
search spaces is how to rapidly
number
plan
based
interface
has
system
in
recognition
in large
and accurately
recognixe
the
[3, 6, 7J.
POISE
the
goals
actions
they
By
accomplish.
in the context
intelligent
POISE
intelligent
plan
that
could
of
user
plans
are
of user actions and
recognizing
a
assistance
recognition
is a
recognizer
may be forced
of
interpretations
plan
because
of: 1) concurrency
complex
user‘s
of an
task
active
plan
since
constraints
on the temporal
among
2) sharing
of plan steps among alternative
possibility
that any partial
among
of
alternative
and accurately
and
reduce
in order
provide
the
for
most
to the user.
Complete-Purchase
name
are
specified
in
a
formal
and its parameters
(Amount,
description
Items,
Vendor)
!
A textual
DESC
When the invoice is received, check with the requester
to find out if the goods have been accepted and
should be paid for or if they have been returned
and so should be canceled.
2
The steps
Receive-Invoice
(Pay-For-Goods
’ Check-Goods
I Cancel-Goods)
!
The
on the plan
COND
= qtc%&ved”)
(Check-GoodsStatus
WILL-EXIST
Pay-For-Goods
= qwlxrmd”)
(Check-GoalsStatus
WILL-EXIST
Cancel-Goods
the
involved
of the plan
IS
cmstmints
and their
relative
ordering
’
sttps
and
objects
<=>
<=>
Receive-Invoice&em
= Check-Goods&ems
Receive-Invoiceltems
= Pay-For-GoodsJtems
Cancel-GoaMtems
A definition
parameters
Amount
Items
Vendor
of plan
OR
= Receive-Invoice Amount
= Receive-InvoiceItems
= Receive-Invoice.Vendor
z~plan
is one in a hierarchy
of plans *for purchasing
in an
The plan, Complete-Purchase,
consts~ of three steps of
which’ the final sten is either Pav-For-Goods
or Cancel-goods.
(i.e., loose
Additional
COND clause statements
umstmin
stateme;lts.
the parameters
of the subplans
(e.g., the item being paid for
must be the one referred
to in the invoice).
WhiIe this plan
dots not make use of it, the Is chmse language
provides
for
concurrency
with the shufffe operator
which leaves the relative
ordering of the plan steps unqecified.
The steps of concurrent
toplevel
plans are implicitly shuffled W.
clause
plan steps);
plans; 3) the
plan might be continued
number
in the design of such a
interpretations
efficient
The plan
!
consideration
in user activities
ordering
be
!
system.
to keep a very large number
under
to
PROC
to a user
be used as part
assistant for an office automation
Plan
of active
system
assistance
detection
and
management,
error
agenda
(e-g.,
correction, and plan completion).
Figure 1 describes a
simple
disambiguate
is how to rapidly
of this model of possible actions,
POISE is able to provide
to
information
of a small
plan recognixer
the number
of a small
intelligent
Hierarchies
used to specify typical combinations
actions
constraint
observation
A key problem
The need for this type of plan
arisen
4) insufficient
the
The descri ‘ens of procedures/plans
language [l p” as depicted here:
on the observation
of plan steps.
recognition
from
03054
interpretations.
the
INTRODUCI’ION
important
the current
user
New Hampshire,
user actions;
acquired
I
Corporation
Blvd. (MKOl-2GO9)
Continental
of Massachusetts
by
This research
was sponsored,
in part, by the External
Research
Program
of Digjtal Equipment
Corporation
and by a grant from
Rome Air Development
Center.
Figure
42
1 - POISE Complete-Purchase
plan
Our solution
a
to this problem
Plan
application-specific
the constraint
knowledge
quickly
heuristic
is used
focus
the
search
from
interpretations.
The
likelihood
steps are shared,
deal
plans,
and the
set of
large
heuristics
architecture
to
of
with
the
heuristic
for representing
heuristic
plan recognition
systems.
about
the assumptions
interpretation
and
information
to recognize
the
behind
represents
heuristic
information
II
they
system
The plan
possible
new
ability
the user has made
error
led
an
has
to
these assumptions,
information.
This
allowing
system
been
can
the
We believe
made,
used
an
to
interpretation,
to
the
that this approach
the new
benefit
of
why
not only applies to
heuristic
systems
knowledge
for
Adaptability
to different
applications
and
environments
with changes to the heuristic
control knowledge alone (i.e., without requiring
changes to the underlying reasoning system).
The capability
users.
backtracking
of explaining
scheme.
its reasoning
considered
in
interpretation
basic
and semantic
i.e., plan
by
process
step
providing
validity
a
use
context
which
to be recovered
the current
2.
temporal
It also permits
interpretations
unlikely
invalidates
Figure
were
if new
likely interpretations.
tracks a user action,
by attempting
it initiates
to integrate
the
as follows:
When the predictions reach the level of the
new
instantiation,
constraint
values
are
checked.
If the constraints are satisfied, the
monitor integrates the user action instantiation
into the higher-level plan instantiations.
This
is done by “abstracting”
from the action
instantiation
up to the plan level of the
predicting
instantiation.
The
uabstraction”
creates
instantiations
and
Pro==
Plan
propagates
constraint
values.
‘Ihe new and
the
predicting
instantiation
structures
are
copied’ and merged into a single structure
which represents
a new interpretation.
The
syntactically
and
semantically
valid
continuations
of existing interpretations
are
The use of an intelligent
focus-ofcontrol
strategy which can reason about context and
the
relationships
between
competing
and
cooperating
interpretations
when
integrating
new information.
The use of an intelligent
previously
allows
of five
If a plan of this type was expected by any
interpretation
entries in the focus set, the
monitor
expands
the predictions
from
the
higher-level
instantiation
towards
the
instantiation
of the action.
This generates a
hierarchy of instantiations
on the prediction
blackboard.
it
plan.
interpretation
This
consists
An instantiation
of the primitive plan which
represents the user action is placed on the
instantiation blackboard.
to
for it address issues which must be faced by
to exploit
the
explained
user action into existing interpretations
any such system:
The ability
control.
the
the
contraints.
focusofcontrol
mechanism.
the recognition
an
what
how
user
out a particular
but to complex
uses
to restrict
ARCHITECXLJRE
syntactic
instantiations,
When the monitor
intelligent
decide
has the added
explain
If
plan
heuristic
and
implements
and attribute
information
or that
error.
and how to integrate
believes the user is carrying
plan recognition,
be
invalid
approach
to
an error
interpretation
of
of the
When
made.
contradicts
the
how
and
architecture
depicted
which checks
orderings
to reason
state
as
of possible
for
recognition
architecture
machinery
system
the system can use this reasoning
scheme
in general
A formal
for
current
interpretation
the
formalized
the
that
which
has made
assumptions
were
PLAN RECOGNITION
components
the current
into two
recognition
shows
strategy
an
has advantages
It becomes
why
backtracking
undo
assumptions
is acquired
interpretation,
system [4].
III
application-specific
relative
a new plan.
use in a reason maintence
plan
that plan
of continuing
has been
section
and
the
possible
the likelihood
knowledge
discusses
focus-f-attention
This
The
II
search.
plausible
set
likelihood
existing plan versus starting
strategy
of the paper is organized
Section
SectiOnS.
This heuristic
a small
very
which
supplement
focusofcontrol
to
the
of alternative
can
in the plans.
by the
interpretations
in
knowledge
information
The remainder
has been to develop
architecture
recognition
’ This copy permits constraint
values to be propagated
and then
easily retracted
if the system decides to backtrack
(i.e., revise
prevmus
decisions
about
which
higher-Ieve
structure
an
mstantiation
is part of.)
to
43
posted
to the
focusing
system,
Continue” facts (see section III).
PLAN
LIBRARY
as
“can
l
The monitor also checks to see if the user
action could be the start of a new activity.
It scans the plan library to determine whether
or not the user action could syntactically start
a new high-level plan.
l
If a new plan could be started, the monitor
“abstracts” from the action instantiation
to
constraints
and
higher-level
plans, checking
propagating constraint values until a top-level
The plans which the user
plan is reached.
action can syntactically and semantically
start
are posted to the focusing system as “can
STart” facts (see section III).
SEMANTIC
DATABASE
AGENDA
To
MONITOR
INSTANTIATION
BLACKBOARD
PREDICI’ION
BLACKBOARD
l
l
l
.
.
l
0
consider
the
interface
is monitoring
represented
The
whose
step
only
sub-step
Send-Information,
where
future
SEMANTIC
DATABASE
- maintains a representation
the state of objects in the users world.
blackboard.
current
the
in progress.
mechanism
plan,
of
focus
plan
interpretation
Send-Information
which,
abstraction
The
for
sequence
triggered
44
of other
monitor
partial
monitor
is
action
a
that
this
is the most
likely
seen
so far.
the
A
and its plan
instantiation
in Figure 3.
Send-Information.7,
the
indicates
leading
of
instantiation
blackboard.
entry,
next
AEO9,
level
of
(Send-Information.7).
up to this point
agenda
entries
has
some of
Note that most agenda
if the focuser
is correctly
user actions.
The
of
the
of an agenda
will not be executed
tracking
interpretation
generate
given
of actions
the creation
plan,
actions
the
would
which are pictured
entries
the
on
the
plan,
primitive
has just occurred
the creation
if executed,
primitive
the
on
set
of
instantiated
This triggered
Request-Information.
instantiation
action
is Check-Goods
the
partial
is
action
Receive-Information.
by
The
is a user
Purchase
is the
plan
The
has been
plan,
followed
Complete-Purchase
Architecture
propagation
that
activity
sub-step
consists
Receive-Information.
action
constraint
plan
interact,
l), has as its first step,
only
of Complete
Request-Information
PLAN LIBRARY
- contains
data structures
describing
the plans.
The monitor
makes use of the temporal
specifications
of the subpIans
to predict the next steps
in
the
plans
and
ma k esuse
of
the
constraint
specifications
when propagating
attribute
values between
plan instantiations.
the
basic
Assume
a purchase
by the primitive
next
whose
FOCUSOF-ATTENTION
DATABASE
a
data
structure where the monitor records its interpretations
of
user actions and the assumptions
it made to arrive at
those
interpretations.
The partial
pIan instantiations
xseg=;he
current best interpretations
are listed in
.
of
the
focusing
example.
(see Figure
Receive-Invoice,
MONITOR
AGENDA
- a prioritized
list of possibfe
next
steps
for
the
monitor
to
take
to
expand
interpretations.
The monitor
selects actions from this
agenda to expand the interpretations
that it is currently
%est”
interpretations).
The
pusuing
(i.e., its current
action
of
constructing
an
interpretation
on
the
blackboard
triggers the addition of new actions to the
monitor agenda. If all agenda actions were executed,
ah
possible (valid) interpretations
would be generated.
discussion
in [7].
how
heuristic
following
Complete-Purchase
Figure 2 - Plan Recognition
2 A full
contained
and
Complete-Purchase
INSTANTIATION
BLACKBOARD
- a data structure
where the monitor
records
potential
interpretations
of
user activities as hierarchically
related
sets of partially
instantiated
plans
BLACKBOARD
- a data structure
“simulates”
various
predictions
of
understand
system
The state of the focus set and instantiation
blackboard
Figure
3.
The
depicted
in
pm
are
as
MONITOR
- the interpreter
which tracks user actions
and system events, instantiates
pIans corresponding
to
those
actions
and events,
and propagates
constraint
information?
PREDICTION
the monitor
actions
better
recognition
compares
the
instantiation,
to the types of actions
interpretations
considers
only
in
those
which were expected
entries.
Through
the
monitor
detects
a
the
focus
interpretations
set.
for
by
The
the
by focus set interpretation
use of precomputed
match
expected
between
the
tables,
plan
the
type,
Send-Information,
the plan,
focus
the
and
Check-Goods,
set entry
generate
agenda.
Constraint
the prediction
the
level
of
values
the
shown
meets
predictions
representing
the
user
In this case,
parameter
the partial
The action
interpretation
how focusing
interpretations
Send-Information
of
constraint
original
instantiation
Check-Goods.
the
from
the
the
from
been
the
Focusing
Figure
integrated
5, the
into
action,
focus
set
cannot
the
interpretation.
The
partial
4,
the
top
structure
needs
copied
Check-Goods24
this
revise
of
was
when
copy
its
the previous
it
was
operation
decision
that
interpretation
or
could
be
an
a
valid
really
error
revised
will
because
the existing
reuse
to pursue interpretations
later
be
by
make it less likely.
within
interface!
be
should
be
of
from further
still
might
heuristics
to
is
instance
Complete-Purchase.6,
be interpreted
the
the
be part of Complete-Purchase.6.
differently
focus entries
of
noted,
(Send-Information.7
user), the focusing
which
has
should
and
level
Figure
new
to
While
interpreted
set
as
Send-Information.7
The
Receive-Information.1,
shows
the
(Complete-Purchase-
system
interpretation
in
instaniations
interpretation
previously
consideration.
of
that
the
also eliminates
structure,
Current best: Complete-Purchase.6
Predictor:
CompIete-Purchase.6
Next step:
Check-Goods
Agenda entry: AEO6
the
If the
actions
interpretation
these
“discarded”
previously
thought
to be “unlikely.”
bhudiation
Blackboard
Complete-Purchase.6
hi=&
AEO6 Predict
Check-Goods
...
AE09
As
Check-Goods24
are considered.
In
to
up
Complete-Purchase.6
permits
Send-Information.19
with
values
interpretations
values
Note
in
integrated.
constraint
This example
plan
prediction.
Complete-Purchase
down through
action,
has created
Complete-Purchase.6.
on
the potentially explosive
plans
such
primitive
by limiting
propagated
to
meets the expectation
in focus.
helps control
The
in Figure 4. When
the level of the
compatible
Send-Information.7.
process
on
actions
values are propagated
abstraction
executed
for
removing
of
to actions
be
predictions
are compared.
has
of
could
those
instantiations
instantiation
references
types
next.
predictions
instantiates
blackboard,
plan
is expected
which
appropriate
monitor
prediction
which
agenda
the
sub-step
also contains
monitor’s
The
starting
Abstract
from
Rcqucst-Infurmation
Receiy-Invoice3
Complete-Purchase6
from
Send-Information.7
Receive-In&mation.l
Send-Lnformatioa.7
Figure 3
Lwtandndon
Blackboard
I’rdlctlon
Gxyylctc-Purchase.6
Agcab
...
AE4I9 Abstract
Request-Information
from
Send-Information.7
Rcceiv -Lnvoicc3
2
Receive-Lnformation.1
Black-
Check-Goods.16
I
Request-Information.18
Send-I&mation.l9
Send-lnformation.7
Figure 4
FIml
Focus
Iastantlat.lon
Set
Complete-Purch
25
7
/
Check-Goods24
Receive-Lnvoiee3
\
/
Request-Information21
Receive-Lnformation.l
\
Send-Lnformation.7
gerr;znbcst:
Complete-Purchas+
.
Request-Information21
Receive-Information
Next step:
Agenda entry: AEll
4Wh
...
AEll
Predict
Receive-Information
Blnckboard
from
Request-Lnformatioa21
Figure 5
45
Prcdktion
BIaekboard
aeckiGd-16
Request-Information.18
Send- IJ ormation.19
III
Focusing
mechanism
POISE
in
which limits
interpretations
is
a
heuristic
the interpretations
considers
system
the
which
actions
User action k is SHared by plan
instantiation
Xu and Yv (i.e., the
action fulfills parts of two goals).
FOCUSING
deemed
most
control
Heuristics
likely.
User
action
k
has
GreaTeR
likelihood of fulfilling part of plan
instantiation
Xu
than
of
plan
instantiation Yv.
of the user
those
to
about
the likelihood
of user actions are of three types:
1.) Application-specific
- Derived from observations of
For example, ua single
the application
environment.
action is most likely to be taken to satisfy some part
of a single goal.” That is, sharing a single low-level
plan among several high-level plans is unlikely.
Also,
“initiation
of a new top-level plan is a less likely
reason for executing some action than completing some
plan already in progress.” Since these heuristics are
derived
from
a particular
application
(e.g., office
systems), their relative importance
(or usefulness) will
depend upon the application.
Other applications may
have different characteristics.
of specific actions
2.) Plan-specific
- The likelihood
being taken to satisfy particular goals (iz., as steps of
particular high-level plans) may be specified as part of
the plans.
For example, “action A is more likely to
be part of plan X than plan Y.”
3.) User Goals - The user may explicitly state goals
plans with some or all of their
(i.e., high-level
accomplished.
filled
in)
to
be
parameters
Interpretations
which match these explicit user goals
may be considered extremely likely.
A Most Likely Explanation
for user
action k in “state” w (i.e., after
considering the sequence of actions,
w) is that
it is part
of plan
instantiation
Xu.
The interpretation
entries in a focus set are the plan
instantiations
in the union of the
MLEs in a particular
state.
A
formal
definition
of Most Likely
Explanation:
IF PE(k,Xu) AND NOT
[PE(k,Yv) AND GTR(k,Yv,Xu)]
THEN MLm%kw.
where k stands for a user action, u, v, and w are
sequences of user actions, X and Y are high-level
plans, and plan instantiations
are represented as Xu for
the high-level plan underway (X) and the actions which
partially fulfill it (u).
The
over
these
heuristics
facts
application-specific
The
make
focusing
strategy
uses
these
about
the
most
assumptions
interpretations
a form
of nonmonotonic
by default.
A reason
recording
along
to be expanded.
and
with
retracted
scheme
proposes
without
having
work
because
assumption
undo
the effects
be
unrelated
of changing
a
the heuristic
are represented
knowledge,
the
as follows:
sT(kJ9
-
User action k can STart plan X.
WJW
-
User action k can
instantiation Xu.
WWW
-
A Possible
Explanation
action
k
is as
part
instantiation Xu.
Continue
for
of
developed
for
rules
of
the
the
office
are:
Initiation of a new top-level plan is a less likely
reason for executing an action than completing
some plan already in progress:
IF C(k,Xu) AND PE(k,Xuk) AND
ST(k,Y) AND PE(k,Yk) AND
NOT SH(k,Xuk,Yk) AND
CONSISIENT(GTR(k,Xuk,Yk))
THEN GTR(kXuk,Yk).
and
can be inferred.
In order to formal.&
facts and assumptions
to
are
backtracking
can
heuristics
Three
H2-
assumptions
assumptions
as inference
A single user action is most likely to be taken
to satisfy some part of a single goal so it is
unlikely
that the input action be shared by
multiple high-level plans.
This is implemented
by stating that if no explicit assumption
has
been made that an input is shared by multiple
interpretations,
assume that it has not been
shared:
IF PE(k,Xu) AND PE(k,Yv) AND
CONSISTENT(NOT
SH(k,Xu,Yv)y
THEN NOT SH(k,Xu,Yv).
is used for
Assumptions
formalized
assumptions.
Hl -
represent
Assumptions
incorrect
assumptions.
application
as reasoning
system
dependency-directed
identifies
better
known
interpretation
justifications.
a
which
interpretation
particular
their
with
retracted
reasoning
to
“reasonable”
The heuristics
maintenance
maintaining
heuristics
are
and
plan
user
plan
3 See [8] for an explanation
CONSISTENT.
of the nonmonotonic
modal
upcxator
H3 -
(fact
Of two Possible
Explanations
for an input
action, if one is assumed to be a Most Likely
Explanation for a later user action then it has a
GReaTer
likelihood of being the Most Likely
Explanation for this input:
IF PE(k,Xukj) AND PE(k,Yvk) AND
NOT SH(k,Xukj,Yvk) AND MLE(w,j,Xukj~
AND CONSISTENT(GTR(k,Xukj,Yvk))
THEN GTR(k,Xukj,Yvky
11).
assumed
This must be done because,
to continue
l
l
l
l
algorithm
When
there
the
is no
only
the focusing
given
the
constraint
possible
interpretation
process
Explanation”
algorithm
Figure
6.
a
fact
The second
(facts
heuristics
interpretation
formalized
is more
likely
(H2), results in interpreting
of plan X rather
and
10).
new
“Possible
than
part
Explanation”
for
action
partial
to
plan
backtrack,
that
than starting
interpretation
14).
User
of
action
c
Y
the interpretation
of action
(facts
c
22
Hl and I-D.
FUTURE RESEARCH
system described
built.
by the focusing
office
is currently
A special-purpose
was
reason
The
additional
heuristics
has been
automation
We are planning
applications
the
of plan
system.
system
the
of
as a continuation
AND
provided
existing
the heuristic
the
b (fact
the
fact
b to be reinterpreted
POISE
in
studied.
application
being
to explore its effectiveness
which are not as tightly
constrained
in
as the
office.
We
most
plan
to
be
invloves
whose
order
to retract
scheme
retraction
explained.
reasoning
in
a more
current
A
about
assumptions
interpretation
interpretations
assumption
developing
The
assumption
action
approach
the
allows
the
more
the
and
to
intelligent
locates
intelligent
“quality”
the
identify
or to recognize
of
resulting
the
“best”
that the user has
made an error.
One of
a new
currently
scheme.
recent
current
only
an
are
backtracking
b, can
b results
4 Implicitly, w includes k and later user actions.
s This is a slightly simplified version of the actuaI
of the
knowledge
a, the
continuing
a being
that
a and
STATUS
effective
Another
this action as a continuation
of action
Note
from
forward
by action
actions
maintenance
than the start of plan Y (see facts 9
This interpretation
15-17).
some
system
is more likely
The plan recognition
actions,
user action,
starting
push
is recognized,
is no
is
the
9 generated
and
c,
fully,
3) is the
that
Zu
causes
be interpreted
N
the plan begun by action a
above,
a, it is
b action which
it within
there
and 23) because of heuristics
“Most Likely
or as the start of a new plan (facts 5 and 7).
now
example
user action,
The
(see
as continuing
of
plan,
causes
(i.e.,
an interpretation
Y started
can
Since there is only one
for the first
action
more
The
interpretation
information.
in focus.
be interpreted
the
plan
than one “Possible
heuristic
rules to
assumptions.
is straightforward.
for
interpretation
in
syntactic
ignoring
focusing
a new
action,
interpret
where
This
assumption
continuing
user
to
structure
C(c,Zu)
retract
Post the resulting “Most Likely Explanations”
for the user action and propagate the results
of this latest interpretation
back to earlier
interpretations.
is
third
way
instantiation).
If there are no “Possible Explanations” for the
action, either an interpretation
error or a user
error has occurred.
Backtrack
through the
assumptions which have been made to find
one
which
could
result
in an alternate
interpretation
(previously disregarded because it
seemed unlikely)
which would explain
the
current action.
Retract this assumption, revise
the interpretation
of the previous actions and
repeat
the interpretation
process
for the
current action.
example
considers
form
Use the “can Continue” and “can STart” facts
posted
by the monitor
during
the basic
interpretation
process (see section II) to post
“Possible Explanation” assumptions for the user
action.
To understand
an
plan including
to be “Shared” by two X plans
(i.e., Xab and Xab’ where b’ is another
is as follows:
If an action has more
Explanation,”
apply
the
generate relative likelihood
b was
has not yet ocurred).
interpretation
The focusing
the partial
possible that a is meant
although
current
approach
exploit
the information
in
in a
the
semantic
knowledge
posted
likelihood
creating
rule.
47
direction
about
in which we are extending
is the use of focusing
about
database.
objects
heuristics
that
the
which
is contained
These
heuristics
include
the use of objects
in plans,
e.g., the
that plans share objects
new objects.
or the likelihood
of
ACKNOWLEDGEMENTS
Bruce
Croft
contributions
plan
and
Larry
to the design
recognition
Lefkowitz
have
as
part
of
Croft, W. B., L. Lefkowitz,
V. Lesser, and K.
Huff,
“POISE:
An
Intelligent
Interface
for
Profession-Based
Systems,” Conference
on Artificial
Intelligence, Oakland, Michigan, 1983.
PI
Doyle, J., “A Truth Maintenance
Inielligence, 12231-272.
PI
Gischer, J., “Shuffle Languages,
Petri Nets, and
Context-Sensitive
Grammars,” Communications of the
ACM, 24(9)597405.
made
and implementation
architecture
PI
of the
the
POISE
System,” Artificial
project.
REFERENCES
PI
Bates, P., J. Wileden,
and V. Lesser, “Event
Definition
Language: An Aid to Monitoring
and
Debugging
Complex Software Systems,” Technical
Report
81-17, Department
of
Computer
and
Information
Science, University
of Massachusetts,
Amherst, Massachusetts, 1981.
[fd Huff,
PI
Croft,
W. B. and L. Lefkowitz,
“An Office
Procedure
Formalism
Used
for
an Intelligent
Interface ,n Technical Report 824, Department
of
Computer
and Information
Science, University
of
Massachusetts, Amherst, Massachusetts, 1982.
PI
McCue, D. and V. Lesser, “Focusing and Constraint
Management
in
Intelligent
Interface
Design,”
Technical Report 83-36, Department
of Computer
and
Information
Science,
University
of
Massachusetts, Amherst, Massachusetts, 1983.
PI
McDermott, D. and J. Doyle, uNon-monotonic
I,” Artificial In!elligence, l3:41-72.
Plan
Grammar:
The
sets of facts
X = a b d...
and assumptions
a
1. n(aS)
2. PE(a,Xa)
3. MLE(a,a,Xa)
Focus
?&+a}
Y = b c...
for
each
User
su cewsive
K.
and
V.
Lesser,
“Knowledge-Based
Command
Understanding:
An Example
for the
Software
Development
Environment ,” Technical
Report
82-6,
Department
of
Computer
and
Information
Science, University
of Massachusetts,
Amherst, Massachusetts, 1982.
Actions:
ab before
backtrackina
4. c@Xa)
5. PE ,Xab)
6. ST ,y)
7. PE % ,Yb)
8. -SH@,Xab,Yb)
a b c...
T&ate” (string
of user
actions)
arc:
ab after
backtrackina
4.
5.
6.
7.
8.
c@JW
PE ,Xab)
ST ,Y)
PE ! ,Yb)
-SH(b,Xab,Yb)
&7?HlJ
10. MLE(ab,b,Xab)
l14. MLE(ab,b,Yb)
abc
15. qab)
16. PE(c,Ybc)
17. MLE(abc,c,Ybc)
4. CCbSa)
2 E % %“’
7: PE :Yb)
18. PE(b,Ybc)
19. --SH@,fiSlab)
WV-U
1.
2.
11.
12. GTR(a,Xab,Xa)
20. GTR@,Ybc,Xab)
t5,17,lg@31
21. GTR@,Ybe,Yb)
t2,ww3~
l3. MLE(ab,a,Xab)
Focus
Set-b)
13. MLE(ab,a,Xab)
Focus
t7,17,18J-1
22. MLE(abc,b,Ybc)
Sct=jXab,Yb)
23. MLE(abc,a,Xa)
Focus
Sct=(Xa,Ybc}
Facts and assumptions
are grouped by the user action to which they refer.
only ‘Sn” [4] facts are represented
and some obvious -SH
assumptions
have been ignored.
The justifications
for assumptions
made using the three
heuristics
formalized
in the paper are
’ en in braces ({ )) below the assumptions
with the three heuristics
denoted as Hl-H3.
Thart
assumptions
cK $ed durin
backtracking
are denoted with lk
C and ST facts arc
posted by the monitor
as described
in stct~on II.
t-h e remainder
of the facts rcsuIt from the application
of
various bookeeping
ruIes.
Figure 6 - Focusing
in a Sample Grammar
48
Logic