L02 Agent Architectures

8/2/2019 L02 Agent Architectures

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Agent ArchitecturesKin trc Agent v H Agent

(C)CopyrightSoftwareEngineeringDepartment

L Tn Hng

CNTT HBK H ni

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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?

Documents

L02 Agent Architectures