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Agent ArchitecturesKin trc Agent v H Agent
(C)CopyrightSoftwareEngineeringDepartment
L Tn Hng
CNTT HBK H ni
2
Ni dung
1. Agent architecture (internal) l g?2. Abstract Agent-Architecture3. Deliberative Architectures (Kintrc suy din)4. Reactive Architectures (Kin trc
phn x)5. Hybrid Architectures (Kin trc lai)
Kin trc Phn lp (Layer )6. Kin trc BDI (Belief- Desire- Intention)7. Kin trc m(OAA)
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I. Kin trc Agent1.L do Kin trc Agent?
Thay thcho vic chuyn t tng, chdn thnh thc thi chng E.g. after or in the late steps of Gaia
Lm thno xy dng nhng h thngmy tnh tho mn nhng yu cu c bitbng agent theoritists
Nhng kin trc phn mm nhthno lph hp?
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2. nh ngha kin trcAgent - I
Pattie Maes
Mt phng php hc c bit xy dng agents.N ch r lm thno agent c thc tch rathnh cu trc ca 1 tp cc modules thnh phnv lm thno nhng modules c thtng tcvi nhau.Ton b tp modules v stng tc gia chngcho ta cu tr li lm thno m nhng dliucm bin and trng thi hin ti ca agent xcnh actions v nhng trng thi trong tip theoca agent.
Mt kin trc bao gm cc k thut v thut tonh trcho phng php ny.
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Definitions of AgentArchitecture - II
Kaelbling
Mt tp hp c bit cc software (or hardware)
modules, c thit kc trng bi nhnghp vi nhng mi tn ch ra d liu vdng iu khin gia cc modules
Mt cch nhn tru tng hn vi kintrc agent l mt phng php chung thit kc nhng modules ring bitcho nhng nhim v c th
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Kin trc Agent?
M t cc trng thitrong ca agent
Cu trc dliu ca n Thao tc c th thc
hin trn cc cu trc Lung iu khin gia
cc cu trc dliu.
Kin trc agent khc nhau trn nhiu khacnh khc nhau v cu trc dliu v thut tonc biu din bn trong agent
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3. Abstract AgentArch.a.Standard Agent
S = { s1, s2, ... }Tp trng thi mi trng c thcA = { a1, a2, ... }
Tp hnh vi ca Agent
Agent: Function Action: S* AAgent tp nh x cc trng thi ca mi trng vo cc hnh
ng ca agentKiu agent ny gi l standard agent.(agent chun) Mt agent chun quyt nh hnh ng thc thi ph thuc
vo history ca n, i.e., its experiences to date.
AGENT
ENVIRONMENT
Action
InputSensor
Output
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(Non-)Deterministic Behavior
Hnh vi ca Mi trng m hnh ha :
env: S x A (S)Pmeans the powerset or set of all subsets non-determistic
if(S) = {sx, sy}Khng xc nh trng thi ktip ca Agent deterministic
if(S) = {sx} Trng thi ktip xc nh v l duy nht
Interested in agents whose interactions withenvironment doesnt end (e.g. infinite)
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M hnh trong
abstract agent Sub system
L thuyt tng quan v Agent l rt hu ch, nhngn khng gip ta xy dng agent mt cch hiuqu,v khng ch ra vic lm th no thit k ranhng action ca agent
Chng ta ci tin li abstract model, bng cch chianh n thnh nhng h thng nh hn (sub-systems)(like top-down refinement in software engineering).
Vic ci tin thnh nhng sub-systems lin quan tivic la chn d liu v cc cu trc iu khin,
nhng th hp thnh agent.
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Kin trc trong (Sub-system)
The sub-system view of an agent: mt kin trc agentl m t thnh phn bn trong ca Agent: cu trc d liu ca n,
nhng hnh ng s thao tc trn cu trc d liu
dng iu khiu d liu gia cc d liu .
Thng chia chc nng quyt nh (desicion) thnhhai sub-systems:
nhn thc (perception)
hnh ng (action)
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Perception v ActionNhn thc v hnh ng
Hmsee s quan st mi trng trong khi
hm actionbiu din h thng ra quyt nhca agent.
ENVIRONMENT
seesee actionaction
AGENT
Hm see()
C thc ci t didng phn cng/mm
u ra ca hm see() lmt tp cc tri thc(percept) thu nhn
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See() v Action() Vi P l tp nhn thc u vo
See: S -> P
Action: P* -> A
Gi smi trng c hai trng thi:s1 v s2; s2 s1 nhng see(s1) = see(s1) c ngha l hai trng thi mi trng khc nhau
nhng u c cm nhn ging nhau (nhn thyging nhau)
=> thng tin thu nhn c l ging nhau
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Vi d
Cho 2 tr ng thi ca mi trng,s1 ands2, c thphn bit c cc agent nu chng map to the same
percept. V d 1 bn nhit c th phn bit c cc trng
thi ca mi trng:
x = tooCold
y = WomanIsDanger
:C ngha l trng thi mi trng cho bi tp:
S = {{x,y},{x,!y},{!x,y},{!x,!y}}
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By gi, hot ng mt cch hiu qu, b
cm nhi
t khng quan tm li
u y = truekhng? iu ny khng nh hng ti
action ca n.
V vy hm nhn thcsee s l:
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c.History
Chng ta c th thay th s tng tc caagent vi mi trng l history,
V d:.mt chui cc cp: state-action
vis0 l trng thi u tin ca mi trng
ai hnh ng ca agent khi n trng thisi.
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History of Agent
vi mi chui h c th l history ca mt agent ubt u t trng thi u tins0:
Vi i N, ai = action((s0,s1,s2,, si))
(tt c mi hnh ng u phi qua mt chui trngthi)
i Nsuch that i> 0, si env(si-1,ai-1)
(mi trng thi mi ca agent phi thuc mt tp cctrng thi c th c ca mi trng t trng thitrc v mt action c th)
N l tp s tnhin (0,1,2,)
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Characteristic behaviour
action:S* ->A , trong mt mi trng, env:S x A ->P(S), l tp tt c histories c th c ca
agent. Chng ta s biu th tp histories ca agentbng hist(agent,env).
Hai agents, agent1 and agent2, c gi l tngtc tng ng nhau i vi mt mi trng, env,iff hist(agent1 ,env) hist(agent2,env)
nu chng tng tc tng ng nhau i vi mimi trng chng c gi n gin l
behaviourally equivalent.
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b.State based Agent
Cc agent thngc d liu bntrong
D liu: thng tinv trng thi cami trng
Thng tin v qukh ca Agent
seesee
nextnextstate
actionaction
AGENT
ENVIRONMENT
thng s dng mt chui nhn thc, mt agent c trngthi trong ni ni ghi nhng thng tin v trng thi mitrng v qu kh ca chnh n
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Trng thi trongInternal State
Gi I l tp tt c cc trng thi trong c thc ca agent.
see : S -> P
action : I -> A
Hm thc hin qu trnh la chn hnh ngby gi c nh ngha nh mt nh x tcc trng thi trong ca agent n tp cchnh ng c thc thc hin:
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Next() : I x P -> I
Hm next(): hm nh x t mt trng thi trong I vtri thc thu nhn c P vo mt trng thi trongkhc I (tc l khi nhn c tri thc mi, trng thitrong ca agent thay i)
Vng lp:while(true) {
p = see(s);
i = next(i,p);
perform(action(i));
}
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Behavior Abstract AgentArch.
1. Khi to vi trng thi s02. Quan st mi trng vi trang thi s, to lp
v thu nhn tri thc bng see(s)3. Trng thi trong ca agent c thay di v
cp nht thng qua hm next(i0,see(s))4. Cc hnh ng tip theo m agent thc hin
sc la chn nhhm,action(next(i0,see(s)))
5. Hnh ng thc hin sa n 1 chu trnhmi (thu nhn tri thc), goto 2
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State-based vs standard agents
State-based agents khng hiu qu hn agentchun (standard agents), nh ngha phntrc
Thc t chng ng nht in their expressivepower
Tt c state-based agent c thc bini thnh 1 agent chun c behaviourally
equivalent.
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II.Phn loi kin trc Agent
Deliberative (Kin trc suy din)
Logic-Based ArchitecturesBelief-desire-intensionBDI(Suy lun thng minh)
Reactive (Kin trc phn x)
Hybrid (Kin trc lai) Layered architectures (Kin trc lp)
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1. Kin trc suy din
Da trn symbolic AIKin trc m qu trnh ra quyt nhc thc hin nhsuy din logic.
Cc phng php ra quyt nhLogical Reasoning
Pattern matching
Symbolic manipulation
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Kin trc suy din
Symbolic description of World
Mc ch cn t ti Tp miu t hnh ng Tm mt chui actions t
ti mc ch. Sdng thut ton n gin To khoch khng hiu qu
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Kin trc suy din
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Kin trc (BDI)Belief-Desire-Intention
Kin trc da trn qu trnh suy lun thng minh(practical reasoning) trong qu trnh ra quyt nhc tin hnh tng bc, cc hnh ng c thchin xut pht t yu cu ca hm mc tiu ra. Beliefs:biu din tp cc thng tin m agent bit v
mi trng hin ti ca n.(v c thmt vi trngthi trong),
Desires: ci xc nh ng c ca n - v d ci nang khm ph, ..
Intentions: biu din nhng quyt nh phi hnh
ng nhthno hon ton t ti desires can (committed desires)28
BDI Architecture[Brenner et al, simplified; origin Rao and Georgeff]
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BDI- beliefs = hiu bit ca agent- desires = nhng mc ch ca agent- intentions = nhng mc ch cn hon
thnh (tp con ca desires)
Extended+ goals
+ plans
interaction
knowledge base
BDI reasoner
plan, schedule, execute
actions
perception
AGENT
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Cc thnh phn ca agent BDI
Tp cc nim tin hin ti (belief): biu din tp ccthng tin m agent bit c v mi trng hin tica n.
Hm thu nhn tri thc t mi trng (belief revisionfunction) thu nhn thng tin mi, cng vi nim tin c to ra nhng hiu bit mi v mi trng
Hm sinh cc la chn (option generation function):a ra cc la chn c th c i vi agent (desire)da trn hiu bit ang c v mi trng v mongmun ca n.
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Cc tu chn hin ti (set of current options) biudin tp cc hnh ng m agent c th thchin.
Hm lc (filter function): biu din cho qu trnhcn nhc ca agent chn ra mong mun datrn nhng iu kin ang c, ang bit.
Tp cc mong mun (intention): biu din mongmun hin ti ca agent.
Hm chn hnh ng thc hin (actionselection function): xc nh hnh ng scthc hin.
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Nhng hn ch ca kin trcsuy din
Performance problems
Vn TransductionTn nhiu thi gian chuyn i tt c nhng thngtin cn thit thnh symbolic representation, c bit numi trng thay i rt nhanh.
Vn representationLm th no world-model c biu din mtcch tng trng v lm th no agent c th suydin kp thi vi s thay i thng tin
Cho nhng kt qu hu ch. Nhng kt qu sau cng c th l v dng Does not scale to real-world scenarios
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2. Kin trc phn x
L kin trc m qu trnh ra quyt nh
c ci t mt cch trc tip, tc l s cmt nh x trc tip ttnh hung ti hnhng No central symbolic representation of world
Khng suy lun phc tp
Ssuy din phc tp c thdn n khngli gii hay p ng v mt thi gian
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Kin trc phn x
Brooks:
Nhng kin trc thng minh c thc to rakhng cn symbolic (AI) representation
Behavior thng minh c thc to ra khng cnexplicit abstract symbolic reasoning (AI)mechanisms
Tnh thng minh l thuc tnh ni bt trong hthng phc tp
Effect of combined components > effect of eachcomponent times number of components
Real intelligence is situated in the real world,not in disembodied systems such as theoremprovers or expert systems
Behavior thng minh l kt qu ca vic tng tcvi mi trng
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S kin trc
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c th ca agent phn x
Tnh phn x l mt behavior based modelof activity symbol manipulation modelused in planning
Cc thnh phn ca Perception:
1. Ngngha hc ca u vo agent2. Tp kin thc cs.3. A specification of state transitions
Actions c to ra bi ngngha ca u raagent (reaction)
Tt c symbolic manipulation c thc hintrong thi gian dch
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V d
B tn nhit n gin l agent phn x:
S= {tooCold, okay}A = {heatingOn, heatingOff}
action(okay) = heatingOff
action(tooCold) = heatingOn
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V d agent phn x
Robots objective:
khm ph cc hnh tinh (v d. Mars), and moreconcretely, su tm nhng mu vt ca 1 loi c bit
1. Nu nhn ra vt cn th i hng2. Nu ang cm mu vt v ti cn cth
s nh vt mu3. Nu ang cm mu vt v cha ti cn
cth i v pha cn c4. Nu pht hin ra mu vt th cm n ln5. If true then move randomly
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u im
n gin
kinh t kim sot c kh nng tnh ton
kh nng chu li cao
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Cc vn ca agent phn x
mt lng ln thng tin cnh cn cho agent
vic hc?
c c th l th cng (handcraffed)
S pht trin mt rt nhiu thi gian
khng th xy dng mt h thng ln?
chc s dng cho nhng mc ch banu?
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Nhc im
Nu agent khng s dng m hnh ging nh mhnh ca mi trng trong n h ot ng th chng
phi c y nhng thng tin cn thit bn trong c th thc hin cc action thch hp.
Hu ht cc agent u ra quyt nh da trn ccthng tin mang tnh cc b ca ring mnh.
Cc agent u khng c kh nng hc t nhng kinhnghim gp phi cng nh nng cao kh nng ca
h thng k c hot ng trong mt thi gian di.
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3.Kin trc lai - Hybrid
Kt hp tnh phn x v tnh suy dindeliberative component: Subsystems to ranhng k honh v quyt nh s dngsymbolic reasoning
reactive component: Subsystems phn ng lis kin nhanh chng m khng cn nhngreasoning phc tp
Thnh phn phn x c quyn u tin hn thnh phnkhng phn x
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M hnh Hybrid
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Kin trc lp
Phn lp theo chiu ngang
Phn lp theo chiu dc1 chiu2 chiu
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3.1Phn lp theo chiu ngang(horizontal layering)
trong kin trc ny tt c cc thnh phn trn
cc lp
u ti
p xc tr
c ti
p t
i
u vo vu ra
mi thnh phn trn mt lp c th coi l mtagent.
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u nhc im n gin. Nu ta cn mt agent c n cch c x
khc nhau th s ci t m hnh ny. Tuy nhin lun c s trnh ginh trong vic ra quyt
nh, m bo s tng thnh ta thng a vomt hm iu khin trung tm (mediator) quytnh xem lp no ang iu khin hot ng caagent.
Gi s trong m hnh ca ta c n lp v mi lp cth thc hin m action khc nhau vy c ngha l cth c n mn kh nng tng tc ln nhau,
theo quan im thit k th y l mt vn kh v
khi hot ng c th gy ra hin tng tht c chai(bottleneck) trong qu trnh ra quyt nh46
3.2 Phn lp theo chiu dc(vertical layering)
Kin trc ch c hai thnh phn tip xc vi u vov u ra, ta c th coi nhl mt agent
n gin hn rt nhiu so vi phn lp theo chiungang.
Phn lm 2 loi: Mt chiu:
Lung iu khin ln lt i qua tng lp cho ti khiti lp cui cng s to ra hnh ng cn thc hin.
Hai chiu:
Thng tin c i theo mt chiu (ln) v iu khinc i theo chiu khc (xung).
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u /nhcim
S phc tp trong tng tc gia cc lp c gim
Lung iu khin phi i qua ton b cc lp vth nu mt lp no hot ng khng nnh s nh hng n ton b h thng
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Phn lp
Lp phn x thc thi nh lmt tp quy tc hnh ng tuthuc vo trng thi, a lasubsumption architecture.
Lpplanningto ra nhng khoch v la chn actions thc thi nhm t ti mc chca agent
Lp modellingcha nhng mu nhn bit v cc agentkhc trong mi trng.
Gia ba lp ny c s lin lc vi nhau v c gitrn vo mt framework iu khin, ci s dng nhngquy lut iu khin.
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Kin trc lpControl unit & knowledge base
Cc lp:- Thp: phn x, cao: suy lun, cao nht: a agent
communication
cooperative planning layer
sensors actuators
local planning layer
behaviour-based layer
social model
mental model
world model
knowledge base control unit
AGENT
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III. Chn mt kin trc Agent
Agent ca ti lu tr thng tinv mi trng. Da trn nhnghiu bit n to ra reasoning
v planning.
Agent ca ti quan st mitrng. N nhn ra nhng thayi ca mi trng, ci m sbt u cc hnh vi ca n.
Agent suy din Agent lai agent phn x
both
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Exercise!
Tho lun trong vi phtKin trc bn trong no l tt nht cho
Peer-to-peer project?
Deliberative ... Or ... Reactive?
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Exercise!
Tho lun trong vi pht:
Kin trc bn trong no l tt nht choWeather project?
Deliberative ... Or ... Reactive?
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