Using Memory-based

16th IEEE International Conference on Robot & Human Interactive Communication August 26 ^- 29, 2007 / Jeju, Korea

Behavior Selection and Memory-based Learning for Artificial Creature Using Two-layered Confabulation

Se-Hyung Cho', Ye-Hoon Kim', In-Won Park', and Jong-Hwan Kim', Senior Member,

IEEE

'Department

of Electrical Engineering and Computer Science

KoreaAdvanced Institute of Science and Technology (KAIST) 373-1 Guseong-dong,Yuseong-gu, Daejeon305-701, Republic ofKorea,

e-mail: (shcho, yhkim, iwpark,johkim)(&rit.kaist.ac.kr

Abstract- Confabulations, where millions of items of rele- vantknowledge areapplied in parallel in the human brain, are typically employed in thinking. This paper proposes a novel behavior selection architecture and memory-based learning method for an artificial creature based on two-layered con- fabulation. A behavior is selected by considering both inter- nally generated will and the context of the external environ- ment. Proposed behavior selection using a confabulation schemeis a parallel process in which a number of behaviors are considered simultaneously. An arbitration mechanism is em- ployed to choose a properbehavior which is to be put into an action. Also memory-based behavior learning is proposed, where the memory has theselection probabilities of behaviors based on will and context. The learning module updates the contents of memory according to the user-given reward or penaltysignal.To demonstrate theeffectiveness of theproposed scheme,anartificial creatureisimplementedin the3D virtual environment such that itcaninteract withahumanbeing con- sideringits will andcontext.

I. INTRODUCTION

Many service robots and entertainment robots are already well-known to help in simple housework and to share friendship with the user. Software pet-type robots, which imitate animal's spontaneity, are developed to be mounted into actual hardware platform or electronic device, i.e.

cell-phoneorcomputer. Anartificialcreature canbe usedas anintermediate interface for interactions between human and service robot. It should hold outward appearances and knowledge including behaviorpatterns sothat itcan resem- blealivingcreatureandapproachtotheuserwithfamiliarity.

In this sense, it requires confabulation, which imitates a human thought process, and the context-awareness in be- havior selection. Inaddition, it should continuously adaptto the varying environment or to theuser's preference. Thus, the learningprocess is needed likeareal creature's adapta- tionability.

There are researches which mimic a creature's externally observed functions, and analyze a human's brain structure andthought mechanism inphysiological perspectives. Con- fabulationas athought mechanism is aprocess ofmaking a plausible 'spurious'memoryfrominexperienced factsinthe brainusing similar reminiscences inthe past. Through this process, humans can generalize the past experiences and copewith theindirectly experienced situations [1]-[3].

In this paper, a behavior selection architecture based on two-layered confabulation is proposed. The architecture is composed oftwo parallel layers in order to consider both

internallygenerated will and the context of theexternalen- vironment. Each behavior selectionlayersuggestswill-based behaviors and context-basedbehaviors, individually. Finally, an adequate behavior considering both internal state and contextis chosentoputintoanaction. Cogentconfabulation is employed to choose an adequate behavior based on co- gencyinbehavior selection. In cogentconfabulation, many conclusionsymbolsare consideredatthesametime and the bestlikely symbol is projectedas aconclusion [1].All con- texts, internal states and behaviors are represented assym- bols and theknowledge is represented by each symbol'slink inthe proposed architecture.

Furthermore,

memory-based

behavior selection is pro- posed. The memory module holds the probability informa- tion whichrepresentstheplausibility of each behavior cor- respondingto the will or context. The probability informa- tion is used in confabulation process. Since the artificial creature contains amemory module [4]-[10], behaviorscan be learned according to the changed situation. Mem- ory-based behavior learning is accomplished by two steps.

The first stepprovides all the memory contents throughan expert'shandastheir initializationonplausible behaviors of the artificialcreature. The secondstepis thememory-based reinforcement learning using user's reward or penalty to adapt to the user's preference. The effectiveness of the proposed scheme is demonstrated by implementing anarti- ficial creature in the 3D virtual environment which can in- teractwithahumanbeing considering its will andcontext.

Thispaper is organizedas follows: Section II presents es- sential terms, which are required to embody an artificial creature, aredefined including overall architecture of artifi- cial creature and the basic idea of confabulation theory.

Section III and Section IV describe the behavior selection based on two-layered confabulation and memory-based learning method, respectively. SectionV describes the ex- perimental results ofartificialcreatureinvirtual environment.

Theconcluding remarks and future works followin Section VI.

II. TWO-LAYERED CONFFABULATION ARCHITECTURE

A.Architecturefor Artificial Creature

In general, the architecture for artificial creaturemimics a decision mechanism ofthe reallivingcreature,which facesa certain situation and considers itsown desiretomake ade- cision for a proper behavior. Fig.1 shows a typical archi- tecture,whereabehavior is selectedby the behaviorsystem

WB1-3

considering the perception and motivation [11]-[24]. By using perception system,the artificialcreature findsoutthe situation ofexternal environment. Motivationsystemhasan influence on the behavior system for a proper behavior se- lection. The resulting behavior gets expressed through the motor system. Thus, it requires an adequate behavior se- lecting architecture between the perception system and the motor system.

haviorsystem shouldreflettheinenlstaterscns oi t.temt

~w

btVblb

|Syotem

Fig.IAschematicdiagramofasituated character

Toactivateabehavior

similarly

asthe real creature,the be- havior system should reflect the internalstate suchasmoti- vation, homeostasis and emotion. Also, as the environment continuously

changes,

the context of external environment should be considered in order to select an appropriate be- havior. Ifthe architecture is based onthe priority between behaviors asshowninFig.2,the behavior based ontheex- ternal environment constrains the behavior basedonthe in- ternal state [12], [13].

Thus,

it is impossibletorealize ade- liberative behavior, which considers both external and in- ternalstates atthe sametime. Consequently, anarchitecture incorporating bothinternalstateandexternal environment is necessary.

Qbhoteextlbmsedd

|

Behcavior

¹

Internal State-based Selected

Behavior Behavior

Fig.2 Conventional behavior selector basedonthe behaviorpriority

B. Two-Layered Confabulation Architecture

The proposed architecture selects a behavior based on co- gency oftwo-layered confabulations, which integrates the internal state and context. It is expected to realize more natural behavior by using this architecture rather than the simple architecture with the priority.

Intheproposed architectureasshowninFig.3,bothinternal stateandcontextinformationareused for behaviorselection, referringtothememorymodule which holds theprobability information representing the plausibility of each behavior correspondingto the will or context. The probability infor- mation is employed inconfabulation process. Since the ar- chitecture contains a memory module, behaviors can be learned accordingtothechanged situation.

Fig.3The two-layered architecture forartificial creature

Behavior selection module is divided into three sub-modules: will-based confabulation, context-based con- fabulation, and an arbiter. In the will-based confabulation sub-module, most suitable behaviors are recommended by calculating cogency using the current internal states. The context-based confabulation sub-module calculatescogency of the recommended behaviors by considering the external context. Finally, the arbiter chooses afittest behavior using the probabilities of the behaviors calculated by both will-based confabulation and context-based confabulation.

That is, the resulting behavior is being recommended through the confabulation process. Using this architecture, theartificialcreature canshowabehaviorsatisfying itsown desires andfittedtotheexternal environment.

C. Confabulation Theory

Bayesian and cogent confabulation techniques are used to represent formal logic for inductive reasoning. These two methodsreasonby using theconfidencetoall conclusions.In the inductivereasoning,iftheprobability ofa,b,c,and dare given as apartition ofa sample space S and supposethat eventEoccurs,then theconfidence ofeventEoccurring is represented by using Bayesian posterior probability,

p(elabcd).

In cogent confabulation, on the other hand, the confidence of conclusion is represented as cogency, p(abcde). Cogencycanbe calculatedusing Bayes' theorem

asfollows [1], [2]:

p(abcd e)4 =[p(abcde) p(ae)] [p(abcde) p(be)] (1)

[p(abcde

I

p(ce))] [p(abcde)

I

p(de)]

[p(a e). p(b e) p(c e) p(d e)]

First four probabilities can be approximated as a constant number in any given situations. In general, these assump- tionsareplausible approximationsasfollows:

[p(abcde)

I

p(ae)] [p(abcde)

I

p(be)].

[p(abcde)

I

p(ce)] [p(abcde)

I

p(de)]

^z K

(2) p(abcd

e)4

K [p(a e) p(b e) p(c e) p(d e)]

whereKisa constant.

Oncethefirstfourprobabilitiesareconsideredas aconstant,

having the maximum cogency is equivalent to maximizing the probability,

[p(ale) p(ble) p(cle) p(aoe)]

[1], [2].

This process is known as confabulation. In this paper, the confabulation process is applied to artificial creature's be- havior selection.

III. BEHAVIOR SELECTION A. Will-based Confabulation

In the confabulation theory, words in the lexicon are as- sumed fact symbol and a suitable word to given phrase is conclusion [1]. Inspired by this mechanism, behavior selec- tion is achieved by confabulation. The set of assumed fact correspondstotheartificial creature'swillorthecontextof the environment. And conclusion setcorrespondsto thear- tificial creature's behavior set. A lot of assumed facts of will-based confabulation are generated from the artificial creature'sinternalstatessuchasmotivation, homeostasis and emotion. The confabulation process requires the behavior probability toeach of the internalstates,which is preserved in the memory module. The cogency of will-based con- fabulationcanbecomputed usingBayes' ruleasfollows:

Ewill(b1)⁼ Ewill(b2 )-

p(w, b1) p(w2 b)

^.

P(Wm b1)

p(w1

b2) p(w2 b2) P(Wm b2)

(3)

Ewill (bl)

⁼

p(w, bl) p(w2

b,) .

p(wm bl)

where

E1ill

^isdefined as cogencyvalue(expectation) of the behaviortothe will.

bj

,j=1,2, ..., representsjth behavior in abehavior set. p(w1

bj)

is aconditional probability be- tween ith will andjth behavior. I and m represents the number of behaviors and the number of wills, respectively.

Once the cogency values between the behaviors and their will arecalculated, the behaviors with highest cogencyval- ues are recommended to the context-based confabulation.

Since most of the animals load up to four objects into working memory at one time [25], similar number of be- haviorscanbe recommended.

from the will-based confabulation.

p(ci brj)

is a condi- tional probability between ith context andjth recommended behavior. k and n represents the number of recommended behaviors and the number of contexts, respectively. The calculation is carried out for the recommended behaviors from the will-based confabulation to compute the adequacy reflecting environmental conditions.

C.Arbiter

The arbiter decides thefinalbehavior based on the results of will-based confabulation,

E1ill (bj),

and context-based con- fabulation, Econtext

(brj).

The behavior is determined bythe max-product operation as follows:

E,rbjter (bs)⁼

max[E_,,,,/ (brj ) Econtext (brj )],

j⁼

I1,

^{, k}

where bs is thefinally selected behavior.

The behavior having the highest value among the recom- mended behaviors is selectedasthefinaloutput.

IV. MEMORY-BASED BEHAVIOR LEARNING As Fig. 4 shows, the memory module consists ofcontext memoryandinternalstate memory.Redarrowsrepresentthe linkbetween the behavior and thecontextandinternalstate.

Theprobability of the behavior which is relatedtothe con- textand internalstateis savedinthememory. Forinstance, a high probability value is memorizedifeating behaviorinthe kitchen isadequate.

Memoryl

(Context) Memory2

(InternalState)

Fig.4Memorymodule for confabulation

B. Context-basedConfabulation

A lot of assumed facts of context-based confabulation are generated from the external environment suchastime, place and object.Inthispaper,when, where and what (time, place andobjects)are considered as contextsin agiven situation.

Thecogencyof context-based confabulationcanbesimilarly calculatedusing Bayes' ruleasfollows:

Econtext (brl) p(cl brl) p(c2 brl) .). p(cn brl) Econtext (br2) p(cl br2)

p(cp

br2)

^...

P(Cn br2) Econtext (brk) p(cl brk)

^*

p(c2 brk)

^*

p(cn brk)

whereEcontextisdefinedascogencyvalue(expectation) ofthe behaviortothecontext. ci,

i=1,

2, ..., n represents ithcon- text,

brj,

j=1, 2, ..., krepresentsjth recommended behavior

The memory-based learning technique guides the artificial creature's predefined behavior patterns into adaptive be- haviorsto survive in thenewenvironment. This canbe ac- complished bytwo steps. The firststep isto give all of the confabulationprobabilities byanexpert's handasshownin Fig. 5.Ifincorrectprobabilities are given, plausible behav- iorscannotbeexpected and thus it requires careful settings.

Thisstepis usedto setmemory contentsinthe initialization process.

Fig.5Initiallearningprocessbyanexpert

The secondstepis thememory-based reinforcementlearning using feedback signals from changing environmentasshown

in Fig. 6. In the proposed structure, the user allows to grant either reward or penalty by patting or hitting an artificial creature tolearnadesired behaviorat agiven situation.

Fig.6 Memory-based reinforcement learning using feedback

In orderto improve the reinforcement learning, confabula- tionprobabilitygetsincreased whenpatting input is givenat aparticular situation and viceversaforinducing hitting input asfollows:

FPt(ci bj) +8 (if input isareward) Ptemp(c

bj

)⁼ Pt(c, bj )

-85

(if input is a penalty)

Pt(ci b1) (otherwise)

wherePtemprepresents atemporal probability by interac

Pt(Cilb1)

is a conditional probabilitybetween ith contex

jth behavior, and t and a representtime and learning respectively. There is aconstraint thatsum of all prob;

tiesmustbeequaledto one.

p,,1(ci b1) ^Ptemp(c

bj

) E Ptemp(ci bj)

i=l

Bythis

technique,

confabulation

probability

is

adjusted

such that the undesired behavior can be restrained from being activated.

V. EXPERIMENTAL RESULTS

A. Objectives

The

goal

of

experiment

is to create an artificial creature which behaves asper its will considering the context in a given situation. Context awareness enables itto carry out a proper decision in anunexpected environment. To achieve this purpose and to measure the performance ofproposed architecture, the following desired scenarios amongthe be- haviors, contextand willare assumed.

i) Eating behavior would never be carried out around the toilet.

ii)Eventhoughcreaturemaynotbehungry, eating behavior would be selectedifthe food isinthesurroundings.

(6)

Ation, t and rate,

iii) Even though creature is sleepy, sleeping behavior will not selectedifthe bedis not in the surroundings.

iv) If creature is too sleepy, sleeping behavior will be se- lected at any location.

B. ExperimentalEnvironment

The interactivelearning method and the proposed behavior selection mechanism are appliedto the synthetic character, 'Rity', which is implemented by OpenGL in a 3D virtual space.Thesamplingrateofthe computational model isset to 0.1 sec. Rity has 12 DOFs, 46 sensor symbols and73 be- haviors. Itis composedof perception module, internal state module, behavior selection module, learning module and motormodule. More detailed descriptions are presented in

[12]-[17].

InternalstatesofRity aredefinedbased on those of a real dog such as motivation, homeostasis and emotion. Parameter values from(3), (4)were set asfollows: n=2, m=8,k=3,1

=40. : n = 3, m ⁼ 8, k= 3, 1= 40. The assumed facts of will-based confabulationareclassifiedinTable 1.

TABLE.1 ASSUMED FACTS OF WILL-BASED CONFABULATION

Internal Will

State

Play (wl)

Motivation Comradeship (w2) ContactComfort

(W3)

Pain

(W4)

Energy(W5) Homeostasis Fatigue(w6) Excretion(W7)

Emotion Emotion (w8)

abili- Each element of motivation and homeostasis has threestates ofhigh, mid and low. Emotion has fivestates ofhappy, sad, anger, fear, and neutral. Similarly, the assumed facts of context-based confabulationareconsideredasinTable2.

(7)

Intheexperiments, when, where and whatwereconsidered as contexts.

TART2F2 AYMl JMF.n FACT OFC.ONTFhXT-RAWXD CONFAARIT1LATION

C.Experimental Result ofBehavior Selection

Eachexperiment describes the result oftwo-steplearning.At thefirststep,allprobabilitiesareinitializedbyanexpertsuch as developer or user. Then, reinforcement learning is con- ductedby interaction betweenuserandartificialcreature.As userhitsorpatstheartificialcreature,itcangivearewardor

I YA- L. /-- 1 v IV>IL.P r \I>-,13 kJ "AIN I L vI I 1 vi

Context Phase

Morning

Time (cl) Afternoon

Time(c1) Evening

Night

Place

(C2)

Bed Room

Place(c2) ~Toilet Object (C3) ComradeFood

Toy

penalty for reinforcement learning. In otherwords, the be- havior probability is increased or decreased based on the user's stimulus. The artificial creature is rewarded (pun- ished) if the selected behavior is appropriate (inappropriate) tothegivencontext. Inordertoaccomplish the objective of context-aware artificial creature, the following four experi- ments werecarried out.

i)Iftheartificialcreature ate, at alocationnearthe toilet, it waspunished. InFig. 7,probability value of

p(toiletleat)

is plotted as the penalty is applied. Thelearningprocess onthis behaviorwascarriedout 12times for52minutes. Thegraph shows that it hasa decreasing tendency accordingto user's penalty. Since the user forbade eating behavior around the toilet, the probability of the artificial creature eating there became lower.

-+ PenaltyS3ignta]l

p(toileteat) 0. 0 2

F.⁰0Probbilty cang ^w r l i applied 0. _

0. 0 0 4 __

0.002 0.000

Fig.

8 shows behavior

frequency

before and after rein- forcement

learning

inthe

given

context. Everyresult of be- havior

frequency

was observed for 15 minutes.

Only

some

behaviors of total 40 behaviorsare

plotted

inthe

graph.

The 21st to

25th

behaviors are 'STARING', ^'EATING QUICKLY, 'EATING SLOWLY', 'IXCRETING', and 'URINATING'. Before

learning,

the artificialcreatureoften atewith the toilet in its

vicinity.

However,the

frequency

of

eating

behavior at the toilet

(22nd

and

23rd behavior)

was

reduced after reinforcement

learning.

Thus, the user can

teach

Rity

proper behaviors at a certain situation

by

rein- forcement

learning.

Fromthenext

experiment

onwards,behavior

frequency

after

learning

will be described in the

subsequent graphs,

ac-

cording

tothe

proposed two-layered

confabulation.

Fig.8Behaviorfrequencybefore and after reinforcementlearning for scenario(i)

ii) The dominant relationship between will and context is illustrated in Fig. 9. In the experimental environment, the situationwas that the creature was nothungry and context indicated that the food was in the vicinity. In this case, the creaturemadeadecisionaccordingtothe dominance of will andcontext. Thegraph shows that the eating behavior(22nd and23rdbehavior)wasexpressed by the effect of foodinthe vicinity, eventhough the creature was nothungry. Inother words, the frequency of the eating behavior was increased only ifthe context was the food in the graph. This result came from both the reinforcement learning ofuser and the proposed two-layered confabulation architecture.

Fig.9 Behavior frequency after reinforcement learning for scenario (ii)

iii) Fig. 10 shows that the behavior related to a specific context was notselected,eventhough thecreaturehasalow value of will. The 26th to 29th behaviors are 'SLEEPING', 'SNOOZING', 'DIGGING QUICKLY', and 'DIGGING SLOWLY'. The graph describes that under low states of sleepiness, thecreaturedidnotsleep, since the bedwas notin the surroundings. However, the creature slept frequently when the bedwas near.

Fig. 10 Behavior frequency after reinforcement learning for scenario (iii)

iv) The behavior, which isnotrelatedtothecontext,would be selectedifcreaturehasastrongestwill.Fig.11 shows that the sleeping behaviorwas selectedat all locations because the creature was absolutely sleepy. When the creature was onlyalittlesleepy, sleeping behaviorwasrevealedmostlyon the bed.

25

* Others [1 Food 20

15

o 10 5

(DD 11

0 10

5l

21 22 23 24 25

Behavior

35 UOthers

30 25

10 U15 10

25 26 27 28 29

Behavior 6

5

> 4

(D

UF 3 2

1

21 22 23

Behavior

24 25

80 U Bed

70 [ thers

60 50

D40 330 20 10

A LittleSleepy Very Sleepy

Fig.11Behavior frequency after reinforcement learning for scenario (iv)

VI. CONCLUSIONS

Thispaper proposed anovel two-layered confabulation ar- chitecture, which considers both internal state and context for the artificialcreature's behavior selection. Inwill-based confabulation, behaviors are selected considering the inter- nalstatessuchasmotivation, homeostasis and emotions. The selected behaviors are forwarded to the context-basedcon- fabulation to consider when, where and what. The arbiter finally decides the most fitting behavior among the sug- gested behaviors from thetwoconfabulationlayers.

A learning mechanism, which modifies the probabilities in memorymoduleusinguser'sreward andpenalty inputs,was proposedinordertotrain the artificialcreature'sbehaviors.

The artificial creature was embodied by having real crea- ture'scharacteristics using behavior sets and internalstates.

Itcouldrecognize thecontextof external environmentusing exteroception and its willwasproduced by mimickingareal creature's interoception mechanism. The experimental re- sults showed that the artificialcreature could select themost adequate behaviorto meetitsowndesires and thecontext.

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