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The top architecture for multiagent task planning and scheduling
Computers and Industrial Engineeri.g Vol. 23, Nos 1-4, pp. 153-156, 1992 Printed in Great Britain. All rights reserved
0360-8352/92 $5.00+0.00 Copyright © 1992 Pergamon Pr~s lad
THE TOP I R C H I T E C T U R B F O R M U L T I A G E N T T ~ K PLANNING ~ SCHEDULING
Celestine A. Ntuen & E.H. Park Industrial Engineering Department NC A&T State University, Greensboro, NC Y-M Wang Moses Cone Hospital, Greensboro,
NC
William P. Byrd Department of Industrial Engineering University of Florida, Gainsville
ABSTRACT
shell and its knowledge organization allows for indirect consultation at This paper describes a planner known various levels of plan abstraction. as TOP. TOP is an acronym for Task THE TOP ByBTEMI~RCHITRCTUPJ Oriented Planner. The planning concept in TOP extends the methods of the GPS In the TOP domain, the planner is means-ends-analysis and NOAH *s least commitment approach to include developed under the ARTFUL TM library, the deliberation. The TOP has been scheduler is developed using NEXPERT TM implemented in two environments - in expert system shell, and the interface aircraft turnaround function scheduling between the planner and scheduler is a complied DBASE III TM Plus and constraint-directed microblock world under environment. In the TOP domain, involving teleoperation. knowledge about a particular job configuration is prepared by the planner. INTRODUCTION The plan for a particular task is passed Multiagent systems consist of to the scheduler who prepares a detailed assignment and time-wlndow humans, computers, and robots which task Similar to the blackboard collaborate to perform several tasks for scheduling. architecture, information migrates a well-defined goal. between the planner and the scheduler as Planning a multiagent system intercellular units based on the levels requires tasks which are complex because of abstractions set up by the planner. of the human-machine requirements that In the planning model, TOP allows must be addressed. In a human system, these issues can well be approached from the user to interact with menus which are The start screen behavioral models. On the other hand, transparently defined. "mechanical" systems such as a robot can is controlled by the pseudo code: be planned algorithmically exploiting the DESKTOP() && stertup sateen available computational techniques. Help_code = "INTRO" && Intro help Classical planning paradigms such as soreen GPS [Newell & Simon 63 ], ABSTRIPTS choice = 2 .T. && Here soles the [Sacerdoti 74], SIPE [Wilkins 88], NOAH DO WHILE [Sefik 80] and DEVISER [Vere 68] are very program... h e l p _ c o d e = "MAIHMENU" difficult to be implemented for D O _ I T ( c h o i c e , oues, msgs, udfm) multiagent planning. Either they have to IF VERIFY ( "Leave this p r o g r ~ and be embellished with new planning r e t u r n to DOS") knowledge or cannot be used at all. ENDIF The current focus of research effort &a ... There goes the among artificial intelligence (AI) ENDDO community working in planning is to p r o g r a m address the problem of planning for 8 1 G N O F F ( e x s _ n a m e , exe_deso, l o g o _ l i n e ) multiagents. Such techniques as Information on a plan is displayed intention inferring [Allen 83], and actor This enables the user synthesis [Sidner, 85] have been on a plan window. to interactively validate an existing investigated. configuration code or replan a new one. plan is written into a DBASE The TOP architecture is designed to Each An be open-ended. TOP uses the principles database file known as JOBLIST.DBF. of collaboration, cooperation, and example plan for configuration code for deliberation to dynamically reconstruct producing a nylon coil tubing is shown plans and allows plans from various below: agents to interact opportunistically [Hayes-Roth & Hayes-Roth, 79]. The TOP is developed with NEXPERT rM expert system
153
Proceedings of the 14th Annual Conference on Computers and Industrial Engineering
154
Configcode Station9 Station8 Station7 Station6
Coil Schedule Heating Forming Cooling Finishing
plan is b a s e d on job This d a t a b a s e d e f i n e d by the user. As shown in Fig.
sequence
1, job s c h e d u l i n g
A job by itself m a y consist of many tasks, and a task may consist of subtasks, etc.. Thus, a plan with a set of c o n f i g u r a t i o n (or job flow matrix) forms a c o m p l i c a t e d set of c o m b i n a t o r i c a l problem. M a t h e m a t i c a l l y , if we define a plan P, job flow s e q u e n c e C, a job set JS and the w o r k stations L, then P can be d e s c r i b e d as: Note the C c o n s i s t s of a single element C P - £(C,
JS,
L) . . . . . . . . . . . . . . . . . . . .
(i)
= (c=1). That is, one c o n f i g u r a t i o n code is a l l o w e d in a plan.
l
J S - {an: n - 1,2 ..... N} ............ (2) w h e r e a . is job set, n is the total number6T jobs in a configuration. For e x a m p l e the c o n f i g u r a t i o n code;
I
i
JS
-AAI
- {Heat,Form,
Cool,Finish}
T . . . . .
£XECUTON
i
!
@
. . . . . . . .
L - {Sj:
i
Fig. 1 : T O P Architecture
- 1,2 .... J} ........ (3)
w h e r e sj is an integer v a r i a b l e that t a k e s on the v a l u e of j, for j=l, ... J, and J is the n u m b e r of w o r k stations. For such a representation, a JS plan can be d e s c r i b e d u n i q u e l y by a single or a c o m b i n a t i o n of t h r e e w o r d grammar. For example, P-
takes p l a c e u p o n the c o m p l e t i o n of a tas~ p l a n n i n g and a s s i g n m e n t of the jobs ir JOBLIST database under resourcG constraints. The TOP s c h e d u l e r is ar expert system written in N E X T P E R T T~ environment. The s c h e d u l e r starts by r e a d i n g the plan d a t a b a s e as initial N E X P E R T facts. The k n o w l e d g e p r o c e s s i n g is triggered by the EXECUTOR after v e r i f y i n g that the initial c o n d i t i o n s r e q u i r e d to p r o c e s s a job have been satisfied. W i t h i n the scheduler, The EXECUTOR implicitly assigns jobs to machine or workstations. This is a c c o m p l i s h e d by u s i n g meta rules which d y n a m i c a l l y c r e a t e nodes for each joe processing sequence based on the s c h e d u l i n g h e u r i s t i c to be discussed. At the end of a schedule session with NEXPERT, the r e s u l t s are w r i t t e n intc D B A S E data files.
Sj - j , j
(AA/, (A, L)) .................. (3b)
is a p l a n w h o s e job c o n f i g u r a t i o n code r e q u i r e s p e r f o r m i n g task set L for job a to be realized. Conceptually, the scheduler looks at the p l a n e n v i r o n m e n t as follows: T - T~,%
where T I resources
. . . . . T. . . . . . . . . . . . . . . . . . . . .
is
the
task,
PLANNING
IN
THH
TOP
a
set
of
A - At, ~ ..... A m .................... (5)
and A w T, w h e r e w implies that A is not n e c e s s a r y less than T. If t h e r e exists r e s o u r c e s d e f i n e d by
META
and
(4)
a set
of
assigned
DOMAIN
The TOP s y s t e m r e a s o n s dynamically and deliberates over cases before r e a c h i n g a decision. The flow mechanist inherent in the N E X P E R T p r o v i d e s the TOP s y s t e m the a b i l i t y to a l t e r c u r r e n t plans and r e s p o n d s to a c h a n g i n g environment (such as m a c h i n e failures) by replanning u s i n g v i r t u a l p l a n d a t a b a s e (VPD)-stored d u r i n g a consultation.
a set of A i can be humans, A= can be robots, or generically, agents wlth d i f f e r e n t skill levels. The plan p r o b l e m is r e p r e s e n t e d as a context m e c h a n i s m p r i m i t i v e d e f i n e d as •
P:
J
{3z, ~ I zes z - r k} . . . . . . . . . . . . . . . .
•
(6)
E q u a t i o n 6 says that in the context p l a n P, t h e r e exist some resources(e) Z in the a v a i l a b l e r e s o u r c e pool B such that the imminent set of tasks T k can be a s s i g n e d to Z.
NTUEN et al.: Multiagent Task Planning
In the plan synthesis, a concept is declared abstractly such that a condition that needs task execution can be instatiated with a transparent plan. Thus, accordingly, (See, e.g.; Sacerdoti 74) a concept can become a context during instatiation. This makes it easy for replanning and scheduling when necessary. ~WR~QLB
G E N E R A T I O N IN T O P
TOP generates a schedule by reasoning about a known plan. The plan generated by the planner consists of unscheduled Jobs with tagged expected processing time for the jobs. The scheduler reads the plan file as its input and at the same time reads the resource file (see Fig. 2). The scheduling of each job in time-phase starts after the scheduler has adaptively posted a constraint on each job based on the resource needs. Constratint posting and resolution are set up under NEXPERT objects as dynamic node rules [Wang, 91]. As in the planning phase, schedule generation is by creating contexts. A context mechanism creates a hierarchy knowledge about a job by instant tracking of agendas using both forward and backward propagation. This hierarchy of knowledge provides a domain for constraint posting and constrains satisfaction checks during task assignment.
GENERATED ATF CONFIGURATION
-•
~ U IN S CHEDL;LEDJOB LIST
CONSTRAIP4 T POSTi~;G y J O B SCHEDULE AGENDA
PLAN REQUEST WINDOW JO8 SCHEDULING PLAN
LIST GENERAT/ON
Fig.~L : An Architecture Generation.
SCHEDULE LIST
for plan.td-Schedute
T h e TOP scheduler has fourteen such rules that control dynamic job scheduling policies. These rules are set up as "s working group". If, say, a job requires three operators, the dynamic variable "wpg3" will be set to a true value. The inference engine will use this truth value to dynamically create a working node for each operator. With this, it is possible to keep track of each of the personnel's status (idle or busy) during a particular instance. (Exhibit-1 shows a sample of dynamic node creation).
Rxhlblt
1 8tlple
155
Dynut*o Node C r e a t i o n
Hypothes|8 nodetyl=el
VariabLe t4~3
Group q)l, t4)2,14x1
nodet~ nodetype5 r~det~ nodet~4~7 nodetype8
.peZc q)gla ~lb wpglc t~ocil Mprb wprc che chb chg rb
.p2oml ~pl
nodety1~9
nodetypel0 nodetypell nodletypel 2 nodetype13 nodewpe14
In
q~3 wpl, rb Mp2,rb ~ , rb chl ch2 chl ,ch2 rb
In addition, a new object node will be created whenever the job or subtask has been assigned. The newly created node, "NEWJOB" inherits the property of a job class which includes job-name, subtask, work station number, start-time, endtime, and personnel. At the completion of dynamic constraint posting and the resoultion phase, a temporal job sequence is generated and placed in a job sequence agenda based on availability of resources needed for the job execution. B ~ M P L B TOP APPLIC/~TION
The result of the Planner is a database (Joblist.dbf) written to a Dbase III file. This database is used As the input to TOP Scheduler. The file contains the job identification number (ID), the jobname, the station at which the job is to be performed, the expected time to perform a task under (a) human (TIME1) or (b) (TIME2). The starttime and endtime fields are slots to be completed after scheduling. The data in Table-2 shows an example Joblist.dbf: Table-2: 10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
S a m p l e O u t p u t P r o m The P l a n n e r
TASKk~QdE STAT|Ofl TINE1 Tnk'l 1 5.00 Task'2 2 4.00 Task-2 3 4.00 Tuk-4 3 4.00 Tuk-5 4 4.00 Tuk-6 4 4.00 Task-7 5 5.00 TNk-8 6 4.00 Tuk-9 7 4.00 Tuk-10 7 4.00 Task- 11 8 4.00 Task-12 9 5.00 Task-13 0 3.00 Task- 14 0 2.00 Task-15 0 3.00
TINE2 4.00 3.00 3.00 4.00 4.00 &.O0 4.00 4.00 3.00 4.00 3.00 4.00 2.00 2.00 3.00
STARTTINE ENTINE 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00" 0.00 0.00
At the end of the scheduling session, TOP proceduces two output files Jobdone.bdf and Finjob.dbf respectively. The Jobdone file gives information on random dynamic node (job) creation during a schedule generation. A typical content of jobdone file is given below in Table-3:
156
Proceedings of the 14th Annual Conference on Computers and Industrial Engineering
Table-3. sample TOP Schedule Output For Random Dynamio Node Prooesslng Record# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
16
17 18 19 20
N~ STATION PERSONNEL1 PERSON 10 STARTT! chl ch2 0 ntujobl 0 t~ 0 rmujob2 0 rb t~ 0 neuj oh3 0 wpl wp2 0 newj oh4 9 chl ch2 3 neujob5 0 w~ rb 5 newjo~ I newjob7 6 wpl Wl:~ 5 chl oh2 8 newjob8 5 up1 wp2 9 neujob9 4 wp3 rb 10 neujoblO 1 ¢hl ch2 11 newjob11 5 newjoblZ 1 .1:,2 wr~ 15 wpl rb 15 newjob13 9 wp2 wp3 2O newjob14 9 ~pl ~ 2 25 newjob15 8 ~ol wp2 29 newj~16 7 ~2 =p3 33 newjob17 7 ~ol ~o2 3r he, job18 3 wp2 ~ 41 newj ob19 3 wpl ~ 45 neuj oh20 2
6.
M. Stefik, Planning with Constraints (MOLGEN: Part i), Artificial Intelligence, Vol. 16, (1981), p. 111-140.
7.
S. Vere, Planning in Time: Windows and Durations for Activities and Goals, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 5, (1983), p. 246-267.
8.
Yue-Min Wang, An Expert System for Scheduling Aircraft Turnaround Functions. M.S.I.E. thesis submitted to Department of Industrial Engineering NC A&T State University, December 1991.
9.
D.E. Wilkings, Practical Planning: Extending The Classical AI Planning Paradigm. San Mateo, CA: Morgan Kaufman Pub. Inc., (1988)
ENDTINE 3.00 2.00 3.00
5.00
8.00 10.00 9.00
11.00
13.00 15.00 16o00 20.00 20.00
25.00 29.00 33.00 37.00 41.00
45.00 49. O0
The output in Table-3 can be reproduced in a formatted form by the user listing Finjob.dbf (i.e. finish job file). C0NCLUS I O N
A new planning architecture based on d e l i b e r a t i o n behavior has been presented. The TOP system is developed and prototyped for multiagent environments where c o l l a b o r a t i o n and cooperation may be required during a conjunctive task execution. With a proper application environment d e f i n i t i o n (manufacturing job scheduling, project management network, or aircraft refuelling tasks), the TOP system can respond to a new environment without knowledge degeneration. Its ability to reconfigure and replan, and heuristically reschedule its behavior avoids the so called nonsolvable hard p r o b l e m w h i c h are classically inherent in most scheduling problems. REFERENCES
1.
J.F. Allen, Recognizing Intentions From Natural Language utterances. In M. Brady and R.C. Berwick, Eds. Computational Models of Discourse. MIT Press, Cambridge, MA, (1983), P. 140-149.
2.
B. H a y e s - R o t h and F. Hayes-Roth, Modeling Planning As An Incremental Opportunistic Process. IJCAI-79, (1979), p. 375-383.
3.
E. D. Sacerdot i, Planning In a Hierarchy Of A b s t r a c t i o n Spaces. Artificial Intelligence, Vol. 5, (1974), p. 115-135.
4.
A. Newell and H.A. Simon, GPS, A Program That Simulates Human Thought. In E.A. Feigenbaum and J. Feldman, Eds., Computers and Thought, New York: McGraw-Hill, (1963).
5.
C.L. Sidner, Plan Parsing For Intended Response Recognition In Discourse. Computational Intelligence, Vol. (1) , 1985, p. 230-241.
0360-8352/92 $5.00+0.00 Copyright © 1992 Pergamon Pr~s lad
THE TOP I R C H I T E C T U R B F O R M U L T I A G E N T T ~ K PLANNING ~ SCHEDULING
Celestine A. Ntuen & E.H. Park Industrial Engineering Department NC A&T State University, Greensboro, NC Y-M Wang Moses Cone Hospital, Greensboro,
NC
William P. Byrd Department of Industrial Engineering University of Florida, Gainsville
ABSTRACT
shell and its knowledge organization allows for indirect consultation at This paper describes a planner known various levels of plan abstraction. as TOP. TOP is an acronym for Task THE TOP ByBTEMI~RCHITRCTUPJ Oriented Planner. The planning concept in TOP extends the methods of the GPS In the TOP domain, the planner is means-ends-analysis and NOAH *s least commitment approach to include developed under the ARTFUL TM library, the deliberation. The TOP has been scheduler is developed using NEXPERT TM implemented in two environments - in expert system shell, and the interface aircraft turnaround function scheduling between the planner and scheduler is a complied DBASE III TM Plus and constraint-directed microblock world under environment. In the TOP domain, involving teleoperation. knowledge about a particular job configuration is prepared by the planner. INTRODUCTION The plan for a particular task is passed Multiagent systems consist of to the scheduler who prepares a detailed assignment and time-wlndow humans, computers, and robots which task Similar to the blackboard collaborate to perform several tasks for scheduling. architecture, information migrates a well-defined goal. between the planner and the scheduler as Planning a multiagent system intercellular units based on the levels requires tasks which are complex because of abstractions set up by the planner. of the human-machine requirements that In the planning model, TOP allows must be addressed. In a human system, these issues can well be approached from the user to interact with menus which are The start screen behavioral models. On the other hand, transparently defined. "mechanical" systems such as a robot can is controlled by the pseudo code: be planned algorithmically exploiting the DESKTOP() && stertup sateen available computational techniques. Help_code = "INTRO" && Intro help Classical planning paradigms such as soreen GPS [Newell & Simon 63 ], ABSTRIPTS choice = 2 .T. && Here soles the [Sacerdoti 74], SIPE [Wilkins 88], NOAH DO WHILE [Sefik 80] and DEVISER [Vere 68] are very program... h e l p _ c o d e = "MAIHMENU" difficult to be implemented for D O _ I T ( c h o i c e , oues, msgs, udfm) multiagent planning. Either they have to IF VERIFY ( "Leave this p r o g r ~ and be embellished with new planning r e t u r n to DOS") knowledge or cannot be used at all. ENDIF The current focus of research effort &a ... There goes the among artificial intelligence (AI) ENDDO community working in planning is to p r o g r a m address the problem of planning for 8 1 G N O F F ( e x s _ n a m e , exe_deso, l o g o _ l i n e ) multiagents. Such techniques as Information on a plan is displayed intention inferring [Allen 83], and actor This enables the user synthesis [Sidner, 85] have been on a plan window. to interactively validate an existing investigated. configuration code or replan a new one. plan is written into a DBASE The TOP architecture is designed to Each An be open-ended. TOP uses the principles database file known as JOBLIST.DBF. of collaboration, cooperation, and example plan for configuration code for deliberation to dynamically reconstruct producing a nylon coil tubing is shown plans and allows plans from various below: agents to interact opportunistically [Hayes-Roth & Hayes-Roth, 79]. The TOP is developed with NEXPERT rM expert system
153
Proceedings of the 14th Annual Conference on Computers and Industrial Engineering
154
Configcode Station9 Station8 Station7 Station6
Coil Schedule Heating Forming Cooling Finishing
plan is b a s e d on job This d a t a b a s e d e f i n e d by the user. As shown in Fig.
sequence
1, job s c h e d u l i n g
A job by itself m a y consist of many tasks, and a task may consist of subtasks, etc.. Thus, a plan with a set of c o n f i g u r a t i o n (or job flow matrix) forms a c o m p l i c a t e d set of c o m b i n a t o r i c a l problem. M a t h e m a t i c a l l y , if we define a plan P, job flow s e q u e n c e C, a job set JS and the w o r k stations L, then P can be d e s c r i b e d as: Note the C c o n s i s t s of a single element C P - £(C,
JS,
L) . . . . . . . . . . . . . . . . . . . .
(i)
= (c=1). That is, one c o n f i g u r a t i o n code is a l l o w e d in a plan.
l
J S - {an: n - 1,2 ..... N} ............ (2) w h e r e a . is job set, n is the total number6T jobs in a configuration. For e x a m p l e the c o n f i g u r a t i o n code;
I
i
JS
-AAI
- {Heat,Form,
Cool,Finish}
T . . . . .
£XECUTON
i
!
@
. . . . . . . .
L - {Sj:
i
Fig. 1 : T O P Architecture
- 1,2 .... J} ........ (3)
w h e r e sj is an integer v a r i a b l e that t a k e s on the v a l u e of j, for j=l, ... J, and J is the n u m b e r of w o r k stations. For such a representation, a JS plan can be d e s c r i b e d u n i q u e l y by a single or a c o m b i n a t i o n of t h r e e w o r d grammar. For example, P-
takes p l a c e u p o n the c o m p l e t i o n of a tas~ p l a n n i n g and a s s i g n m e n t of the jobs ir JOBLIST database under resourcG constraints. The TOP s c h e d u l e r is ar expert system written in N E X T P E R T T~ environment. The s c h e d u l e r starts by r e a d i n g the plan d a t a b a s e as initial N E X P E R T facts. The k n o w l e d g e p r o c e s s i n g is triggered by the EXECUTOR after v e r i f y i n g that the initial c o n d i t i o n s r e q u i r e d to p r o c e s s a job have been satisfied. W i t h i n the scheduler, The EXECUTOR implicitly assigns jobs to machine or workstations. This is a c c o m p l i s h e d by u s i n g meta rules which d y n a m i c a l l y c r e a t e nodes for each joe processing sequence based on the s c h e d u l i n g h e u r i s t i c to be discussed. At the end of a schedule session with NEXPERT, the r e s u l t s are w r i t t e n intc D B A S E data files.
Sj - j , j
(AA/, (A, L)) .................. (3b)
is a p l a n w h o s e job c o n f i g u r a t i o n code r e q u i r e s p e r f o r m i n g task set L for job a to be realized. Conceptually, the scheduler looks at the p l a n e n v i r o n m e n t as follows: T - T~,%
where T I resources
. . . . . T. . . . . . . . . . . . . . . . . . . . .
is
the
task,
PLANNING
IN
THH
TOP
a
set
of
A - At, ~ ..... A m .................... (5)
and A w T, w h e r e w implies that A is not n e c e s s a r y less than T. If t h e r e exists r e s o u r c e s d e f i n e d by
META
and
(4)
a set
of
assigned
DOMAIN
The TOP s y s t e m r e a s o n s dynamically and deliberates over cases before r e a c h i n g a decision. The flow mechanist inherent in the N E X P E R T p r o v i d e s the TOP s y s t e m the a b i l i t y to a l t e r c u r r e n t plans and r e s p o n d s to a c h a n g i n g environment (such as m a c h i n e failures) by replanning u s i n g v i r t u a l p l a n d a t a b a s e (VPD)-stored d u r i n g a consultation.
a set of A i can be humans, A= can be robots, or generically, agents wlth d i f f e r e n t skill levels. The plan p r o b l e m is r e p r e s e n t e d as a context m e c h a n i s m p r i m i t i v e d e f i n e d as •
P:
J
{3z, ~ I zes z - r k} . . . . . . . . . . . . . . . .
•
(6)
E q u a t i o n 6 says that in the context p l a n P, t h e r e exist some resources(e) Z in the a v a i l a b l e r e s o u r c e pool B such that the imminent set of tasks T k can be a s s i g n e d to Z.
NTUEN et al.: Multiagent Task Planning
In the plan synthesis, a concept is declared abstractly such that a condition that needs task execution can be instatiated with a transparent plan. Thus, accordingly, (See, e.g.; Sacerdoti 74) a concept can become a context during instatiation. This makes it easy for replanning and scheduling when necessary. ~WR~QLB
G E N E R A T I O N IN T O P
TOP generates a schedule by reasoning about a known plan. The plan generated by the planner consists of unscheduled Jobs with tagged expected processing time for the jobs. The scheduler reads the plan file as its input and at the same time reads the resource file (see Fig. 2). The scheduling of each job in time-phase starts after the scheduler has adaptively posted a constraint on each job based on the resource needs. Constratint posting and resolution are set up under NEXPERT objects as dynamic node rules [Wang, 91]. As in the planning phase, schedule generation is by creating contexts. A context mechanism creates a hierarchy knowledge about a job by instant tracking of agendas using both forward and backward propagation. This hierarchy of knowledge provides a domain for constraint posting and constrains satisfaction checks during task assignment.
GENERATED ATF CONFIGURATION
-•
~ U IN S CHEDL;LEDJOB LIST
CONSTRAIP4 T POSTi~;G y J O B SCHEDULE AGENDA
PLAN REQUEST WINDOW JO8 SCHEDULING PLAN
LIST GENERAT/ON
Fig.~L : An Architecture Generation.
SCHEDULE LIST
for plan.td-Schedute
T h e TOP scheduler has fourteen such rules that control dynamic job scheduling policies. These rules are set up as "s working group". If, say, a job requires three operators, the dynamic variable "wpg3" will be set to a true value. The inference engine will use this truth value to dynamically create a working node for each operator. With this, it is possible to keep track of each of the personnel's status (idle or busy) during a particular instance. (Exhibit-1 shows a sample of dynamic node creation).
Rxhlblt
1 8tlple
155
Dynut*o Node C r e a t i o n
Hypothes|8 nodetyl=el
VariabLe t4~3
Group q)l, t4)2,14x1
nodet~ nodetype5 r~det~ nodet~4~7 nodetype8
.peZc q)gla ~lb wpglc t~ocil Mprb wprc che chb chg rb
.p2oml ~pl
nodety1~9
nodetypel0 nodetypell nodletypel 2 nodetype13 nodewpe14
In
q~3 wpl, rb Mp2,rb ~ , rb chl ch2 chl ,ch2 rb
In addition, a new object node will be created whenever the job or subtask has been assigned. The newly created node, "NEWJOB" inherits the property of a job class which includes job-name, subtask, work station number, start-time, endtime, and personnel. At the completion of dynamic constraint posting and the resoultion phase, a temporal job sequence is generated and placed in a job sequence agenda based on availability of resources needed for the job execution. B ~ M P L B TOP APPLIC/~TION
The result of the Planner is a database (Joblist.dbf) written to a Dbase III file. This database is used As the input to TOP Scheduler. The file contains the job identification number (ID), the jobname, the station at which the job is to be performed, the expected time to perform a task under (a) human (TIME1) or (b) (TIME2). The starttime and endtime fields are slots to be completed after scheduling. The data in Table-2 shows an example Joblist.dbf: Table-2: 10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
S a m p l e O u t p u t P r o m The P l a n n e r
TASKk~QdE STAT|Ofl TINE1 Tnk'l 1 5.00 Task'2 2 4.00 Task-2 3 4.00 Tuk-4 3 4.00 Tuk-5 4 4.00 Tuk-6 4 4.00 Task-7 5 5.00 TNk-8 6 4.00 Tuk-9 7 4.00 Tuk-10 7 4.00 Task- 11 8 4.00 Task-12 9 5.00 Task-13 0 3.00 Task- 14 0 2.00 Task-15 0 3.00
TINE2 4.00 3.00 3.00 4.00 4.00 &.O0 4.00 4.00 3.00 4.00 3.00 4.00 2.00 2.00 3.00
STARTTINE ENTINE 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00" 0.00 0.00
At the end of the scheduling session, TOP proceduces two output files Jobdone.bdf and Finjob.dbf respectively. The Jobdone file gives information on random dynamic node (job) creation during a schedule generation. A typical content of jobdone file is given below in Table-3:
156
Proceedings of the 14th Annual Conference on Computers and Industrial Engineering
Table-3. sample TOP Schedule Output For Random Dynamio Node Prooesslng Record# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
16
17 18 19 20
N~ STATION PERSONNEL1 PERSON 10 STARTT! chl ch2 0 ntujobl 0 t~ 0 rmujob2 0 rb t~ 0 neuj oh3 0 wpl wp2 0 newj oh4 9 chl ch2 3 neujob5 0 w~ rb 5 newjo~ I newjob7 6 wpl Wl:~ 5 chl oh2 8 newjob8 5 up1 wp2 9 neujob9 4 wp3 rb 10 neujoblO 1 ¢hl ch2 11 newjob11 5 newjoblZ 1 .1:,2 wr~ 15 wpl rb 15 newjob13 9 wp2 wp3 2O newjob14 9 ~pl ~ 2 25 newjob15 8 ~ol wp2 29 newj~16 7 ~2 =p3 33 newjob17 7 ~ol ~o2 3r he, job18 3 wp2 ~ 41 newj ob19 3 wpl ~ 45 neuj oh20 2
6.
M. Stefik, Planning with Constraints (MOLGEN: Part i), Artificial Intelligence, Vol. 16, (1981), p. 111-140.
7.
S. Vere, Planning in Time: Windows and Durations for Activities and Goals, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 5, (1983), p. 246-267.
8.
Yue-Min Wang, An Expert System for Scheduling Aircraft Turnaround Functions. M.S.I.E. thesis submitted to Department of Industrial Engineering NC A&T State University, December 1991.
9.
D.E. Wilkings, Practical Planning: Extending The Classical AI Planning Paradigm. San Mateo, CA: Morgan Kaufman Pub. Inc., (1988)
ENDTINE 3.00 2.00 3.00
5.00
8.00 10.00 9.00
11.00
13.00 15.00 16o00 20.00 20.00
25.00 29.00 33.00 37.00 41.00
45.00 49. O0
The output in Table-3 can be reproduced in a formatted form by the user listing Finjob.dbf (i.e. finish job file). C0NCLUS I O N
A new planning architecture based on d e l i b e r a t i o n behavior has been presented. The TOP system is developed and prototyped for multiagent environments where c o l l a b o r a t i o n and cooperation may be required during a conjunctive task execution. With a proper application environment d e f i n i t i o n (manufacturing job scheduling, project management network, or aircraft refuelling tasks), the TOP system can respond to a new environment without knowledge degeneration. Its ability to reconfigure and replan, and heuristically reschedule its behavior avoids the so called nonsolvable hard p r o b l e m w h i c h are classically inherent in most scheduling problems. REFERENCES
1.
J.F. Allen, Recognizing Intentions From Natural Language utterances. In M. Brady and R.C. Berwick, Eds. Computational Models of Discourse. MIT Press, Cambridge, MA, (1983), P. 140-149.
2.
B. H a y e s - R o t h and F. Hayes-Roth, Modeling Planning As An Incremental Opportunistic Process. IJCAI-79, (1979), p. 375-383.
3.
E. D. Sacerdot i, Planning In a Hierarchy Of A b s t r a c t i o n Spaces. Artificial Intelligence, Vol. 5, (1974), p. 115-135.
4.
A. Newell and H.A. Simon, GPS, A Program That Simulates Human Thought. In E.A. Feigenbaum and J. Feldman, Eds., Computers and Thought, New York: McGraw-Hill, (1963).
5.
C.L. Sidner, Plan Parsing For Intended Response Recognition In Discourse. Computational Intelligence, Vol. (1) , 1985, p. 230-241.