Distributed Air Traffic Control II Explorations in a Test Bed

Distributed Air Traffic Control

II: Explorations in a Test Bed

N ICHOLAS V. F INDLER1AND R ON L O2

A BSTRACT: Th s s the second of a two-part paper deal ng w th d str buted plann ng

for A

i r Traff

i

c Control. Three d

i

fferent organ

i

zat

i

onal structures have been

i

mplemented: the Local, Central

i

zed Arch

i

tecture, and the Locat

i

on Centered, Cooperat

i

ve Plann

i

ng System w

i

th one- and two-level Coord

i

nator-Coworker H erarch es. We present an n t al, s mpl f ed analys s of the speedup obta nable by us

i

ng the latter two organ

i

zat

i

onal structures. The veh

i

cle of our emp

i

r

i

cal stud

i

es, the D

i

str

i

buted A

i

r Traff

i

c Control Test Bed

i

s then

i

ntroduced. We d

i

scuss the des

i

gn and the results of a ser

i

es of exper

i

ments performed. We compare

i

n the test bed performance measures of the three systems us

i

ng the respect

i

ve organ

i

zat

i

onal structures. The compar

i

sons are made at d

i

fferent levels of traff

i

c dens

i

ty and problem s

i

ze,

i

n terms of commun

i

cat

i

on overhead and process ng t me needed for plann ng.

Keywords: D

i

str

i

buted Plann

i

ng and Problem Solv

i

ng; A

i

r Traff

i

c Control; System Recovery w

i

th Graceful Degradat

i

on; Locat

i

on Centered, Cooperat

i

ve Plann

i

ng System; Coord

i

nator-Coworker Control Structure; D

i

str

i

buted Scratch Pads.

List of Acronyms: ACT: A

i

r Traff

i

c Control, CCH: Coord

i

nator-Coworker H

i

erarchy, DAI: D

i

str

i

buted Art

i

f

i

c

i

al Intell

i

gence, DATC: D

i

str

i

buted A

i

r Traff

i

c Control, DPS: D

i

str

i

buted Plann

i

ng System, LCA: Local, Central

i

zed Arch

i

tecture, LCCPS: Locat on-Centered, Cooperat ve Plann ng System.

I NTRODU CTION

We present

i

n the second part of th

i

s paper the results of a ser

i

es of exper

i

ments compar

i

ng d

i

fferent organ

i

zat

i

onal structures: the Local, Central

i

zed Arch

i

tecture (LCA), and the Locat

i

on Centered, Cooperat

i

ve Plann

i

ng System (LCCPS) w

i

th one- and two-level Coord

i

nator-Coworker H

i

erarch

i

es (CCH). The latter two were

i

mplemented to control a D

i

str

i

buted Plann

i

ng System (DPS) for A

i

r Traff

i

c Control (ATC). The Coord

i

nator-Coworker parad

i

gm, d

i

scussed

i

n Part I of th

i

s paper,

i

s used as a gener

i

c metho

d of control. W

e have compared the respect

i

ve performances of the organ

i

zat

i

onal structures

i

n s

i

mulat

i

ng the A

i

r Traff

i

c Control env

i

ronment

i

n the D

i

str

i

buted A

i

r Traff

i

c Control (DATC) testbed. The compar

i

sons were made at d

i

fferent levels of traff

i

c dens

i

ty and problem s

i

ze n terms of commun cat on overhead and process ng t me needed for plann ng. (Note that the Local, Central

i

zed Arch

i

tecture can be v

i

ewed as a Coord

i

nator-Coworker

1Research Professor of Computer Science and Director of the Artificial Intelligence Laboratory, Computer Science Department, Arizona State U niversity, Tempe, AZ 85287-

5406

2At present Staff Member at the AT&T Bell Laboratories, Crawfords Corner Rd. Holmdel,

NJ 07733.

2

H erarchy of zero level. Further, the reason for stopp ng at two levels n the DATC

search tree i s that w i th the traff i c dens i ty preva i l i ng i n our exper i

ments, there was no need for ntroduc ng a flex bly chang ng number of levels.)

P R O C E S S I N G T I M E N EEDED FOR THE I N C R E M E N T A L S H A L L O W P L A N N I N G P ROCESS IN THE T HREE O RGANIZATIONAL S TRUCTURES S TUDIED

Incremental Shallow Plann i ng has been used to resolve potent i al i nc i dents i n the LCCPS. The process generates a DATC search tree. We have allowed n such trees up to two levels of branches. All branches need to be explored before the best plan-segment can be chosen. We call the development of each of such branches a task . A task lev el corresponds to the level of the branches the part cular task s responsi ble to develop. Let N 1 and N 2 be the number of the f i

rst- and second-level tasks, respect vely. The process ng t me needed to generate the DATC search tree w i th the Local, Central i zed Arch i

tecture can be calculated as

N 1N 2Σ t(T i ) + Σ t(T i,j )(1)

i=1 j=1where t(T i ) s the t me needed to complete task T i , and T i,j s the j -th level-two subtask of the level-one task T i .Let us now assume that there i s an unl i m i ted number of processors ava i lable.The process ng t me needed to generate the DATC search tree for LCCPS w th (1)

one- and (2) two-level CCH can be put as

N 2

Max { t(T i ) + Σ t(T i,j ) }

(2) j=1and Max { t(T i ) + Max {t(T i,j )}}

(3)

respect i vely. Here n i s the number of level-two subtasks of the level-one task T i .

W i th an unl i m i ted number of processors, relat i vely more parallel act i v i t i es i n the Coord i nator-Coworker structure become poss i ble w i th a two-level CCH, as can be seen i n the above formulae. Let us accept for the t i me be i ng the follow i ng s i mpl i fy i ng assumpt i ons:

?

the execut on t me for every task s dent cally equal to t ,

?the number of level-two subtasks of each level-one tasks i s equal to n (.e.,N 2 = n*N 1),

?the t i me needed for the coord i nator-select i on process i s negl i g i ble,

?the message-transm i ss i on-t i me for task-ass i gnment and result-report i ng requ i res negl i g i ble t i me, and

?every message i s responded to i mmed i ately after reach i ng i ts dest i nat i on.

3In th s case, the process ng t me needed for plann ng and the speedup obta ned by usi ng one- and two-level CCH i s shown i n Table 1.Archi tecture Processi ng Ti me Needed Speed-Ups Obtai ned LCA (N 1 + N 2)*t - LCCPS-1 ((N 1/N 2)+1*t N 1 - 1LCCPS-2 2*t ((N 1+N 2)/2) - 1Table 1 — Process ng t mes needed for plann ng and the speed-ups obta ned w th the three organ i zat i onal structures i n the i deal i st i c case when the s i mpl i fy i ng assumpt ons descr bed n the text hold However, n real l fe, none of the above assumpt ons hold exactly. Due to these compl i cat i ons, we cannot formulate analyt i cally the exact durat i on of the plann i ng processes. Therefore, we have des i gned and i mplemented a ser i es of exper i ments i n the DATC testbed. We have wanted to ga i n i n i t a better understand i ng i n a quant i tat i ve manner of the i mpact of each contr i but i ng factor to the overall effect i veness of the three organ i zat i onal structures stud i ed.A TESTBED TO STUDY DISTRIBUTED PLANNING IN THE AIR TRAFFIC CONTROL ENVIRONMENT

The DATC testbed i s wr i tten i n VAX LISP, a full vers i on of Common LISP,runn ng on a VAX-11/780 under the VMS operat

ng system. (In further stud es, we i ntend to use a cluster of workstat i ons wh i ch was not ava i lable at the t i me the work reported here was performed.) The ult i mate goal of creat i ng a testbed i s to prov i de a powerful and effect i ve s i mulat i on env i ronment for emp i r i cal stud i es on ATC wh i ch i nvolve several d i fferent chosen organizational structures for d i str i buted plann i ng.

It i s useful to cons i der mult i ple a i rcraft behav i or at two levels. The lower level i s nav i gat i onally or i ented; the h i gher level i s concerned w i th i nc i dent el i m i nat i on and i s strongly i nfluenced by the i mposed organ i zat i onal structure. For i nstance, when an a i rcraft dec i des to descend from a certa i n alt i tude, i t follows certa i n act i ons — a lower-level behav i or. A h i gher-level behav i or i s exh i b i ted when an a i rcraft detects a potent i al i nc i dent. Such h i gh-level behav i or character i zes the i nteract i ons between the a i rcraft and the consequences of such i nteract i ons. It also controls the d i str i buted plann i ng processes w i th i n the g i ven structure, such as the respons i b i l i ty delegated to some spec i f i c part i c i pat i ng a i rcraft.It i s plaus i ble to assume that every a i rcraft i

n the testbed uses the same planner to control i ts fl i ght and to ensure i ts nav i gat i onal safety. Th i s planner w i ll be called the d i str i buted planner. It i s also assumed that each a i rcraft i s able to perform sens i ng, look-ahead, potent i al i nc i dent detect i on, plan generat i on and execut i on, commun i cat i on, and i f appropr i ate, negot i at i on. Furthermore, all a i rcraft are categor i zed i

nto one of three d i fferent classes — namely, superjet (short for superson i

c jets), jet an

d p r o p

e l l e r . The classes have d i fferent

4performance character i st i cs, such as cl i mb speed, max i mum alt i tude, max i mum speed, etc. S i m i larly, the runway status has d i fferent mean i ngs w i th the three classes of a i rcraft, wh i ch i s cons i dered i n the Process Control Structure. Every a rcraft s supposed to have a flight plan and knows the a priori fl ght plan of each part i c i pat i ng a i rcraft. The a i rcraft attempt to follow the i r fl i ght plans as f i led.Due to i nteract i on w i th other a i rcraft and unforeseen c i rcumstances, they must

dev i ate from the i r fl i ght plans at t i mes. In do i ng that, they execute commands generated by vari ous planni ng processes. These command are unknown to the others unless they are requested through commun cat on.

Each a i rcraft undergoes var i ous phases dur ng ts fl ght. The phases are take-off, ascend i ng, cru i s i ng, descend i ng, hold i ng, approach preparat i on, approach i ng,and land ng. For each phase, the a rcraft has a temporary goal (e.g., ascend to a spec f ed alt tude, cru se at a g ven speed, etc.) and some expected behav or.There are three types of worlds n the testbed. The Real World (RW) reflects the s i tuat i on i n the global a i rspace conta i n i ng all the part i c i pat i ng a

i rcraft and also mon i tors the performance of an organ i zat i onal structure. The assoc i ated knowledge base i s rather r i ch. It i ncludes all a i rcraft's fl i ght parameters, fl i ght plans and the set of commands that can be ssued. Note that no nc dents occur n th i s world. (They must be detected and resolved by the a i rcraft i nvolved, i n advance.)Every a i rcraft ma i nta i ns the i

mage of a Simulated World (SW), wh i ch reflects i ts surround i ng env i ronment on i ts radar scope. It i s obta i ned by extract i ng the relevant i nformat i on from the Real World d i rectly.Each ai

rcraft creates and uses vari ous Extrapolated Worlds (EW) to ?detect d i screpanc i es between Extrapolated World and Real World s i tuat i

ons at the appropr i ate t i me po i nt,

?look ahead, and

?detect potent i al i nc i dents.

The Extrapolated World prov i des the a i rcraft an 'estimated future state' b y extrapolat i ng from the current s i tuat i on over t i me. (The extrapolat i on of the fl i ght paths can be both strai ght and curved li ne.)

The testbed can be run n e ther graph c or non-graph c mode. There are three worlds i n the graph i c mode. A three-d i mens i onal perspect i ve v i ew i s g i ven of the a i rspace from a part i cular pos i t i on.

In the Real World mode of presentat i on, the b i gger, square-shaped v i ewport i s used to show the Real World. The i mage consi sts of a gri d whi ch li es at the ground level, the mode of presentat on — Real World (RW) — s nd cated at the upper left corner, world t i me at the upper r i ght corner, and a set of a i rcraft i n the a i rspace.Each a i rcraft i s shown by a symbol appropr i ate to i ts type. A l i ne, cons i st i ng of exclamat i on marks, connects the a i rcraft and the gr i d. The po i nt where th i s l i ne i ntersects the gr i d shows the exact X and Y coord i nates of the a i

rcraft. Next to

5

th i s po i nt, there are three p i eces of i nformat i on: the a i rcraft ID, the current alt tude n feet d v ded by 100 (rounded), and the current speed n knots per hour

(rounded). All act ve a rcraft's IDs, the phase they are n, and the r current alt tude and speed ('a' and 's', respect vely) are shown n the trac ng (smaller, rectangular)v ewport.

In the S i mulated World mode of presentat i on, the S i mulated World i s d i splayed i n bas i cally the same format, w i th three except i ons. F i rst, the a i rcraft generat i ng the SW, i s shown i n the upper left corner along w i th the mode of presentat i on.Second, the a i rspace i s l i m i ted to the radar range of that a i rcraft. Th i rd, the

trac i ng v i ewport shows the current phase of the target a i rcraft,

i ts surround i ng a i rcraft and status i nformat i on i nd i cat i ng whether i t cont i nues the look-ahead or starts a new look-ahead process. In the s i tuat i on dep i cted, the a i rcraft i n quest i on does not have a plan yet and, therefore, t needs to look ahead a full Look-Ahead T i me per i od.In the Extrapolated World mode of presentat i on, the Extrapolated World i s

d splayed aga n n about th

e same format. The only d fference s that the a rspace

i s s i gn i f i cantly larger for the purposes of extrapolat i on.The testbed i s i mplemented on a un i processor. The d i ff i culty of s i mulat i ng the d i str i buted plann i ng process i s overcome ma i nly by the message passing capab i l i t i es prov i ded i n the testbed. S i m i larly to the objects i n an object-or i ented programm i ng env i ronment, each a i rcraft i n the testbed i s capable of send i ng and

rece i v i ng messages, and i s respons i ble for i ts own behav i or. D i fferent modules of the a i rcraft kernel (see F i gure 1 of Part I) are act i vated when a message i s rece i ved. These modules i n turn may tr i gger the commun i cat i on-un i t of the kernel to send further messages that w i ll act i vate the modules of other a i rcraft's kernel.

The handl i ng of t i me i n our un i processor-based testbed i s cr i t i cal. The DATC

testbed has to

?record the durat i on of each act i v i ty i n terms of actual (real) t i

me,

?ma i nta i n the correct current Real World t i me from the perspect i ve of each a i rcraft, and

?i ntegrate the above two funct i ons i n the s i mulat i on of mult i ple a i rcraft act i v i t i es.

The testbed prov i des several fac i l i t i es for the above. A funct i on called processor-time returns the CPU t i me elapsed s i nce the testbed started. Know i ng the beg i nn i ng and end i ng processor t i me of a part i cular act i v i ty, one can calculate i ts durat i on.

The testbed also ma i nta i ns a so-called aircraft world time for each a rcraft. It i s the Real World t i me from i ts perspect i ve, and i s recalculated and ass i gned to the respect ve a rcraft at each of the follow ng testbed stages :

?the Real World has just been updated,

?the a i rcraft i n quest i on has just f i n i shed i ts current "percept i

on phase",

6

?the a rcraft has just processed a message.

Before an a rcraft perce ves or processes a message, the Real World s updated

appropr

i ately to reflect the correct s

i

tuat

i

on. After the a

i

rcraft has f

i

n

i

shed

i

ts

current percept

i on phase or the process

i

ng of a message, a new a

i

rcraft world

t i me

i

s ass

i

gned to

i

t. Th

i

s reflects the t

i

me taken for the perce

i

v

i

ng or message

process ng act v ty, and helps n the bookkeep ng of actual t mes.

When an a

i rcraft sends a message, a t i m e-s t a m p

i

s calculated and

i

s

assoc

i ated w

i

th the message,

i

nd

i

cat

i

ng

i

ts send

i

ng t

i

me. Its value

i

s the sum of

the current a

i rcraft world t

i

me of the sender and the durat

i

on of the current

a i rcraft act

i

v

i

ty so far.

T HE D ESIGN OF THE T ESTBED AND THE R ESU LTS OF E MPIRICAL I NVESTIGATIONS

The compar

i son between the performances of the three arch

i

tectures stud

i

ed

i

s

i n terms of commun

i

cat

i

on overhead and process

i

ng t

i

me needed for the plann

i

ng process, at d

i

fferent levels of traff

i

c dens

i

ty and problem s

i

ze. We have prepared 18 scenar i os wh i ch d i ffer from each other e i ther i n the number of part i c i pat i ng a

i

rcraft (traff

i

c dens

i

ty) or

i

n the number of branches of the DATC search tree (problem s ze) or n the number of levels of CCH (arch tecture). We have employed three d

i

fferent traff

i

c dens

i

t

i

es, two problem s

i

zes and three arch

i

tectures. The follow

i

ng convent

i

on

i

s used for referenc

i

ng a scenar

i

o. The reference cons

i

sts of s

i

x characters

i

n the form:

DdSsAa

The cap

i

tal letters D, S, A are used to

i

nd

i

cate traff

i

c dens

i

ty, problem s

i

ze and arch

i

tecture, respect

i

vely. The lower case letters d, s and a stand for numbers. Table 2 shows the possi ble values and thei r meani ng.

i

i

i

i

Table 2 — The meani ng and possi ble values of the parameters

d, s and a accord ng to the scenar o reference convent on The problem of s

i

ze one (S1) means that there ex

i

st two level-one and four level-two tasks dur

i

ng the

i

nc

i

dent resolut

i

on process. (Note that there are at most four tasks ava

i

lable

i

n th

i

s problem. Th

i

s

i

s because we allow level-two branches to be developed only after all level-one branches are explored.) The

7problem of s ze two (S2) means that there ex i st three level-one and s i x level-two tasks dur ng the nc dent resolut on process. (The above cho ces were made after a careful cons i derat i on of comput i ng t i me and memory requ i

rements. In future stud i es, us i ng a cluster of workstat i ons, such l i m i tat i ons w i ll be of much less concern.)We assume a baud rate (the number of b ts per second that can be transm tted)of 9600 and that i t requ i res 10 b i ts to transm i

t a character. Thus, the transm ss on t me needed for a message m i s equal to (message-length(

m )*10)/9600We defi ne message-delay-in-response as the di fference between the message-response-time and the message-receipt-time .

When the number of relevant a rcraft ncreases, t should have a pos t ve effect on systems based on the arch tectures w th a one- or two-level CCH (A1 and A2,respect i vely). Namely, the extra processors work i ng i n these cases speed up the plann ng process by mak ng better use of the ava lable process ng power. The value of the speedup i n us i ng the respect i ve arch i tectures A1 and A2 can be calculated accord i ng to the follow i ng formulae:

(t(P A0)/t(P A1)) - 1(5)(t(P A0)/t(P A2)) - 1 (6)

Here t(P A0), t(P A1) and t(P A2) are the t mes needed for the Incremental Shallow Plann i ng process i n the Local, Central i zed Arch i tecture (A 0) and i n the LCCPS arch i tectures w i th one- and two-level CCH, respect i vely.The result of the compar i sons, i nvolv i ng test runs of 18 scenar i os, shows that

the arch i tectures A1 and A2, n general, are more eff c ent than A0. However, for two scenar i os (D 2S 1A 1 and D 2S 2A 1), each w i th two part i c i pat i ng a i rcraft, the arch i tecture A 1 d i d not fully ut i l i ze the ava i lable processors. More i mportantly and surpr i s i ngly, arch i tecture A 2 d i d not perform better than arch i tecture A 1 i n almost every scenar i o except when only two a i rcraft part i c i pate. Clearly, the management of the ava lable processors plays an mportant role. In the trace dump of the s i mulat i

on outputs generated by the DATC testbed, we have found that the delay-i n-response to messages i s the major cause for th i s phenomenon, wh i ch i s expla i ned next.

An a i rcraft can process the messages rece i ved only after hav i ng f i n i shed what i t i s currently do i ng (execut i ng a task, process i ng a message, etc.). Thus, i f some i mportant "upstream" plann i ng act i v i t i es depend on the process i ng of such messages, the whole plann i ng process i s ser i ously affected. We have found two types of s i tuat i ons contr i but i ng to extended delays-i n-response. F i rst, when a coord i nator i s execut i ng a task, all task-request i ng messages must be temporar i ly suspended. Th i s places the task requesters (the ava i lable processors) i n a wa i t i ng state. Second, the conf i rmat i on process of a coord i nator w i

ll also come to a halt

8 when the nom nee s execut ng a task. Th s also slows down the plann ng process cons derably.

In order to m

i n

i

m

i

ze the probab

i

l

i

ty of occurrence of the f

i

rst type of

s i tuat

i

on, we have added more

i

ntell

i

gence to the a

i

rcraft processors. We have

der

i ved several heur

i

st

i

cs for the coord

i

nators to follow. One such heur

i

st

i

cs for a

level-zero coord

i nator

i

s: do not request a task unless all the other a

i

rcraft have

been assi gned tasks.

Note that th

i s heur

i

st

i

c cannot be appl

i

ed to the resolut

i

on of f

i

rst-level

i nc

i

dents s

i

nce several coord

i

nators would then be wa

i

t

i

ng for the same reason at the same t

i

me, result

i

ng

i

n deadlocks. We not

i

ce when the number of relevant a

i

rcraft

i

s larger than the number of f

i

rst-level branches, some or all of the level-one coord

i

nators must wa

i

t so that other ava

i

lable processors may be able to respond as well. The number of level-one coord

i

nators to wa

i

t

i

s the least of the number of f

i

rst-level branches and the d

i

fference between the number of the relevant a

i

rcraft and the number of f

i

rst-level branches. Let us call th

i

s m

i

n

i

mum value n. We have caused the level-zero coord

i

nators to requ

i

re n level-one coord nators to wa t. However, such wa t s only temporary and s n order to avo d deadlocks. Its durat

i

on should also be relat

i

vely short for reasons of eff

i

c

i

ency.

We have found no obv ous cure for the second type of s tuat on contr but ng to extended delays n response. We could e ther let the processors be nterrupt ble or el

i

m

i

nate the nom

i

nat

i

on process needed for the resolut

i

on of level-one

i

nc

i

dents. We have chosen the latter method

i

n our stud

i

es because the number of coord nators requ red for the two problem s zes are small (3 for S1 and 4 for S2). We have thus mod f ed the algor thm so that when a coworker detects a new level-one

i

nc

i

dent,

i

t becomes a self-appo

i

nted coord

i

nator for that

i

nc

i

dent

i

n the arch

i

tecture w

i

th a two-level CCH.

We can also not

i

ce two m

i

nor causes for some

i

neff

i

c

i

ency

i

n plann

i

ng. F

i

rst, when a coworker wants to request a task from a group of coord nators at a certa n level

i

t works for,

i

t uses a scheme of equal-pr

i

or

i

ty

i

n select

i

ng the task-source to request from. If the coworker processor

i

s also

i

n

i

ts own task-sources-l

i

st (.e., the processor has the role of both the coord nator and the coworker), t may end up w

i

th request

i

ng a task from another coord

i

nator. Th

i

s results

i

n the generat

i

on and process

i

ng of external messages w

i

th task-requests and task-ass

i

gnments and,

i

n turn, decreases the average processor ut

i

l

i

zat

i

on, slow

i

ng down the plann ng process tself. We have, therefore, added a rule for the coworker n request ng tasks; namely, always request a task from tself whenever there s a cho

i

ce (

i

nternal messages take no transm

i

ss

i

on t

i

me).

The other m

i

nor cause for

i

neff

i

c

i

ency

i

n plann

i

ng we have found

i

s that even when a g

i

ven coord

i

nator knows that

i

t has no other task to be executed,

i

t w

i

ll not automat

i

cally not

i

fy the other a

i

rcraft about th

i

s s

i

tuat

i

on. Th

i

s may create many cycles of task-request and no-task-ava

i

lable messages,

i

ncreas

i

ng the

i

dle t

i

me of the task requesters. We have solved th

i

s problem by mak

i

ng the

9

coord

i nator not

i

fy the other a

rcraft as soon as t s go

i

ng to ass

i

gn the l a s t

unass

i gned task ("no-other-task-ava lable" message).

We have made the appropr ate mod f cat ons and run 12 out of the 18 scenar os

aga

i n (the scenar os nvolv ng arch tecture A0 are not affected by the added

heur st cs). We have found that by a careful message pass ng management w th the

arch

i tectures A1 and A2, the average delay-n-response s lower and the relevant

a i rcraft become a more effect ve and eff c ent team for problem solv

i

ng. Tables 3

through 6 show the correspond ng results.

In Table 3, we f

i nd that the total number of external messages generated and

processed

i s lower for the arch

i

tecture A2.

Table 3 — The average delay-n-response of a s ngle message

for scenar os nvolv ng A1 and A2

Table 4 — The length of the ncremental shallow plann ng

process for scenar os nvolv ng A1 and A2

The average delay-n-response (n seconds) of a s ngle message s lower n the second group of test runs than i n the fi rst one.

Table 5 — The percentage of speedup obtai ned by usi ng

one- and two-level CCH for scenar os

10W i th the program mod

f cat ons descr bed, the process

ng t me (n seconds)requ i red for the Incremental Shallow Plann ng Process reduces drast cally. Such mprovements are shown to be even more deci si ve i n Table 6. Furthermore, because of the careful sequenc ng of messages that conta n task requests, the processor ut i l i zat i on rate also i ncreases.

Table 6 — The average degree of processor ut l zat on dur ng the incremental shallow planning process over all relevant aircraft Table 4 to 6 are causally related to each other. As noted before, by a careful message pass i ng management for the arch i tectures A1 and A2, the average delay-i

n-response to a s i ngle message over all scenar i os w i ll be shorter. Th i s has been shown by the data collected n the second group of test runs. We have found the average degree of processor ut l zat on and the percentage of speedup to be h gher over all scenar os. The length of the ncremental shallow plann ng process has also become much lower. F gure 4 shows the percentage of speedup n a graph cal form for problem s zes S1 and S2, respect vely.D2D3D4112D2D3D4

F I

G . 4 — The percentage of speedup obta i ned by us i

ng a one- and two-level Coord i nator-Coworker H i erarchy (A 1 and A 2) at three d i fferent a i r traff i c dens i t i es (D1, D2 and D3) for problems of si ze 1 (S1) and si ze 2 (S2).

From the above two d agrams, we judge A1 to be a better choi ce i f the number of f i rst-level branches i s equal to the number of relevant a i rcraft (as i n scenar i os D2S1 and D3S2). The add i t i onal message pass i ng act i v i ty i n connect i ng level-two tasks w i th level-one coworkers has a negat i ve effect because there are no extra processors ava i lable for tak i ng level-two tasks. When the number of f i rst-level branches s less than the number of relevant a rcraft by two , A2 becomes a better cho i ce (as i n scenar i o D 4S 1) because the extra processors employed i n the plann i ng process can now take care of the add i t i onal message pass i ng act i v i ty.However, i f the d i fference between the number of level-one branches and the number of relevant a i rcraft i s only o n e (as

i n scenar i os D 3S 1 and D 4S 2), the add i t i onal message pass i ng act i v i ty reduces the overall system performance.

F i nally, i f the number of f i rst-level branches i s greater than the number of relevant a i rcraft, A 2 i s a better cho i ce (as i n scenar i o D 2S 2) because A 2 i s a more flex i ble control structure than A1 — the processors can change roles more freely i n i t.The overall conclus ons are ?i t i s feas i ble to use LCCPS for d i str i buted A i r Traff i c Control,

?by a careful message pass i ng management for arch i tectures w i th a one- or two-level CCH, the relevant a i rcraft become an effect i ve and eff i c i ent problem solv ng team, and ?wh i ch the better arch i tecture i s depends on the d i

fference between the

number of relevant a i rcraft and the number of f i rst-level branches i n the DATC search tree 3.Unfortunately, due to comput i ng bottlenecks, we have not able to collect s i mulat i on data i nvolv i ng a h i gher number of part i c i pat i ng a i rcraft and to prov i de more i nformat i on about the th i rd statement above. (For some of the most demand ng scenar os, we have used almost 9 megabytes of core memory, and over

1400 L sp funct ons have been employed.) It s, however, qu te certa n that when a cluster of workstat i ons i s used for s i m i lar stud i es, the computat i onal bottlenecks would d sappear and s tuat ons w th much h gher traff c dens t es can be analyzed.C ONCLU SIONS

The research reported i n th i s two-part paper covers the des i gn and i mplementat i on of a DPS, the Location Centered, Cooperativ e Planning System (LCCPS), for DATC. The results of exper i ments character i ze the effect i veness of d i fferent organ i zat i onal structures. The part i t i on i ng of nodes i s demand-dr i ven.Groups of a i rcraft, i dent i f i ed w i th the i r locat i on, are organ i zed accord i ng to a spec i f i c structure to resolve potent i al confl i cts — hence the name Locat i on Centered, Cooperat i ve Plann i ng System. The DATC testbed i s used not only to d i splay the lower-level nav i gat i onal aspects of fly i ng but also to perform the 3In more general terms, each first-level branch is created to solve a partitioned subproblem. Thus, with a given architecture and a certain number of processors,partitioning of the given problem also plays an important role for efficient group p l a n n i n g.

control act

i v

i

ty for d

i

str

i

buted plann

i

ng. Cruc

i

al fac

i

l

i

t

i

es have been

i

dent

i

f

i

ed

and

i mplemented to make the testbed as user-fr

i

endly as poss

i

ble. The fac

i

l

i

t

i

es

ava lable n t can also be employed n bu ld ng a general-purpose testbed for DAI research.

We have developed organ

i zat

i

onal structures w

i

th one- and two-level

Coord

i nator-Coworker H

i

erarchy. The

i

r performance has been compared w

i

th that

of the Local Central

i zed Arch

i

tecture (wh

i

ch can be v

i

ewed as a zero-level

Coord

i nator-Coworker H

i

erarchy). The compar

i

son

i

s

i

n terms of commun

i

cat

i

on

overhead and the effect

i veness of confl

i

ct resolut

i

ons. It has been shown

emp

i r

i

cally that one- and two-level Coord

i

nator-Coworker H

i

erarch

i

es prov

i

de

i

ncreased process

i

ng eff

i

c

i

ency and capab

i

l

i

t

i

es,

i

mproved flex

i

b

i

l

i

ty and rel

i

ab

i

l

i

ty, and lower process

i

ng costs.

In summary, the results of our stud

i

es fall

i

nto the follow

i

ng three categor

i

es of ach

i

evement:

?the des

i

gn and

i

mplementat

i

on of mechan

i

sms to deal w

i

th the problems of connect

i

on, commun

i

cat

i

on, uncerta

i

nty and coherence,

?the des gn and mplementat on of a general-purpose testbed for DAI research,?the qual

i

tat

i

ve and quant

i

tat

i

ve evaluat

i

on of the feas

i

b

i

l

i

ty of delegat

i

ng plann

i

ng respons

i

b

i

l

i

t

i

es to processors on each

i

nd

i

v

i

dual a

i

rcraft

i

n a DATC reg

i

me.

The stud

i

es have

i

mproved our understand

i

ng of D

i

str

i

buted Art

i

f

i

c

i

al Intell gence, n general, and have demonstrated the mportance of DAI n the world of DATC, n part cular.

F

i

nally, the follow

i

ng future research d

i

rect

i

ons may be po

i

nted out:?add ng a pr or ty level to each message so that more urgent messages can be processed earl

i

er,

?add ng more ntell gence to the Message Process ng Module so that messages result ng n h gher product v ty of the whole system can be responded to earl er,?enabl ng some of the plann ng processes to be nterrupted so that messages can be responded faster, wh ch n turn mproves processor management,?study

i

ng the effects of no

i

se

i

n message transm

i

ss

i

on,

?study

i

ng the effects of lett

i

ng coord

i

nators choose an organ

i

zat

i

onal structure

i

n wh

i

ch the number of levels of the Coord

i

nator-Coworker h

i

erarchy depends on the si tuati on, and

?

i

mplement

i

ng a mult

i

processor vers

i

on of the DATC testbed for s

i

mulat

i

on purposes.

These stud

i

es w

i

ll have to be done

i

n us

i

ng a cluster of workstat

i

ons to el

i

m

i

nate computat

i

onal bottlenecks. It

i

s hoped that our present results w

i

ll pave the way to future mplementat ons of a LCCPS for A r Traff c Control.

A PPENDIX I. R EFERENCES

Cammarata, S., McArthur, D., and Steeb, R. (1985). "Strateg i es of cooperat i on i n d i str i buted problem solv i ng." Proc. of the Ninth IJCAI Conf., Los Angeles, CA, 767-770.

Chambers, A. B., and Nagel, D. C. (1985). "P i lots of the future: Human or computers?" Comm. of the ACM , 28, 1187-1199.

Ch i en, R. T. (1982). "Art

i f i c i al Intell i gence and human error prevent i on study i n ATC systems." Final Report , Department of Transportat i on and Federal Av i at i on Adm i n i strat i on, DOT-FA79WA-4360.Cork i

ll, D. D., and Lesser, V. R. (1983). "The use of meta-level control for coord i nat i on i n a d i str i buted problem solv i ng network." Proc. of the Eighth IJCAI Conf., Karlsruhe, West Germany, 748-756.

Dav i s, R., and Sm i th, R. G. (1983). "Negot i at i on as a metaphor for d i str i buted

problem solv i ng." Artificial Intelligence . 20, 63-109.F i ndler, N. V., and Lo, R. (1986). "D i str i buted plann i ng i n a i r traff i c control."Journal of Parallel and Distributed Computing . 3, 411-431.Hunt, V. R., and Zellweger, A. (1987). "Strateg i es for future a i r traff i c control systems." IEEE Computer , 20, 19-32.Lesser, V. R., and Cork i ll, D. D. (1981). "Funct i onally accurate, cooperat i ve d i str i buted systems." IEEE Trans. on Systems, Man and Cybernetics , SMC-11, 81-96.

McArthur, D. R., Steeb, R., and Cammarata, S. (1980). "A framework for d str buted

problem solv ng." Proc. of the AAAI Conf., Stanford, CA, 181-184.Sm i th, R. G. (1977). "A framework for D i str i buted Problem Solv i

ng." Proc. of the Fifth IJCAI Conf., Cambr dge, MA, 836-841.

Steeb, R., Cammarata, S., Hayes-Roth, F. A., Thorndyke, P. W., and Wesson, R. B.(1981). "D i str i buted i ntell i gence for fleet control." R e p o r t R-2728-ARPA, The RAND Corp., Santa Monica, CA.

Swetram, G. F. Jr. (1981). "A prel i m i nary character i zat i on of the AERA man-machi ne i nterface for ATC computer replacement." MITRE Corp. Working Paper , WP-81W00135, McLean, VA.

Thorndyke, P. W., McArthur, D., and Cammarata, S. (1981). "Autop lot a d str buted planner for ai r fleet control." Proc. of the Sev enth IJCAI Conf., Vancouver, Canada,171-177.

Wesson, R. B. (1977). "Plann ng n the world of the a r traff c controller." Proc. of the Fifth IJCAI Conf., Cambr dge, MA, 473-479.

Wi ener, E. L. (1985). "Beyond the steri le cockpi t." Human Factors , 27, 75-90.

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