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

Behavior Selection and Memory-based Learning for Artificial CreatureUsing 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 ScienceKorea Advanced Institute of Science and Technology (KAIST)

373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea,e-mail: (shcho, yhkim, iwpark, johkim)(&rit.kaist.ac.kr

Abstract- Confabulations, where millions of items of rele-vant knowledge are applied in parallel in the human brain, aretypically employed in thinking. This paper proposes a novelbehavior selection architecture and memory-based learningmethod 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 confabulationscheme is a parallel process in which a number of behaviors areconsidered simultaneously. An arbitration mechanism is em-ployed to choose a proper behavior which is to be put into anaction. Also memory-based behavior learning is proposed,where the memory has the selection probabilities of behaviorsbased on will and context. The learning module updates thecontents of memory according to the user-given reward orpenalty signal. To demonstrate the effectiveness of the proposedscheme, an artificial creature is implemented in the 3D virtualenvironment such that it can interact with a human being con-sidering its will and context.

I. INTRODUCTION

Many service robots and entertainment robots are alreadywell-known to help in simple housework and to sharefriendship with the user. Software pet-type robots, whichimitate animal's spontaneity, are developed to be mountedinto actual hardware platform or electronic device, i.e.cell-phone or computer. An artificial creature can be used asan intermediate interface for interactions between human andservice robot. It should hold outward appearances andknowledge including behavior patterns so that it can resem-ble a living creature and approach to the user with familiarity.In this sense, it requires confabulation, which imitates ahuman thought process, and the context-awareness in be-havior selection. In addition, it should continuously adapt tothe varying environment or to the user's preference. Thus,the learning process is needed like a real creature's adapta-tion ability.There are researches which mimic a creature's externallyobserved functions, and analyze a human's brain structureand thought mechanism in physiological perspectives. Con-fabulation as a thought mechanism is a process of making aplausible 'spurious' memory from inexperienced facts in thebrain using similar reminiscences in the past. Through thisprocess, humans can generalize the past experiences andcope with the indirectly experienced situations [1]-[3].In this paper, a behavior selection architecture based ontwo-layered confabulation is proposed. The architecture iscomposed of two parallel layers in order to consider both

internally generated will and the context of the external en-vironment. Each behavior selection layer suggests will-basedbehaviors and context-based behaviors, individually. Finally,an adequate behavior considering both internal state andcontext is chosen to put into an action. Cogent confabulationis employed to choose an adequate behavior based on co-gency in behavior selection. In cogent confabulation, manyconclusion symbols are considered at the same time and thebest likely symbol is projected as a conclusion [1]. All con-texts, internal states and behaviors are represented as sym-bols and the knowledge is represented by each symbol's linkin the proposed architecture.

Furthermore, memory-based behavior selection is pro-posed. The memory module holds the probability informa-tion which represents the plausibility of each behavior cor-responding to the will or context. The probability informa-tion is used in confabulation process. Since the artificialcreature contains a memory module [4]-[10], behaviors canbe learned according to the changed situation. Mem-ory-based behavior learning is accomplished by two steps.The first step provides all the memory contents through anexpert's hand as their initialization on plausible behaviors ofthe artificial creature. The second step is the memory-basedreinforcement learning using user's reward or penalty toadapt to the user's preference. The effectiveness of theproposed scheme is demonstrated by implementing an arti-ficial creature in the 3D virtual environment which can in-teract with a human being considering its will and context.This paper is organized as follows: Section II presents es-sential terms, which are required to embody an artificialcreature, are defined including overall architecture of artifi-cial creature and the basic idea of confabulation theory.Section III and Section IV describe the behavior selectionbased on two-layered confabulation and memory-basedlearning method, respectively. Section V describes the ex-perimental results of artificial creature in virtual environment.The concluding remarks and future works follow in SectionVI.

II. TWO-LAYERED CONFFABULATION ARCHITECTURE

A. Architecturefor Artificial Creature

In general, the architecture for artificial creature mimics adecision mechanism ofthe real living creature, which faces acertain situation and considers its own desire to make a de-cision for a proper behavior. Fig. 1 shows a typical archi-tecture, where a behavior is selected by the behavior system

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considering the perception and motivation [11]-[24]. Byusing perception system, the artificial creature finds out thesituation of external environment. Motivation system has aninfluence on the behavior system for a proper behavior se-lection. The resulting behavior gets expressed through themotor system. Thus, it requires an adequate behavior se-lecting architecture between the perception system and themotor system.

haviorsystem should reflettheinenlstaterscns oi

~wt.temt btVblb|Syotem

Fig. I A schematic diagram of a situated character

To activate a behavior similarly as the real creature, the be-havior system should reflect the internal state such as moti-vation, homeostasis and emotion. Also, as the environmentcontinuously changes, the context of external environmentshould be considered in order to select an appropriate be-havior. If the architecture is based on the priority betweenbehaviors as shown in Fig. 2, the behavior based on the ex-ternal environment constrains the behavior based on the in-ternal state [12], [13]. Thus, it is impossible to realize a de-liberative behavior, which considers both external and in-ternal states at the same time. Consequently, an architectureincorporating both internal state and external environment isnecessary.

Qbhoteextlbmsedd| Behcavior 1

Internal State-based SelectedBehavior Behavior

Fig.2 Conventional behavior selector based on the behavior priority

B. Two-Layered Confabulation Architecture

The proposed architecture selects a behavior based on co-gency of two-layered confabulations, which integrates theinternal state and context. It is expected to realize morenatural behavior by using this architecture rather than thesimple architecture with the priority.In the proposed architecture as shown in Fig. 3, both internalstate and context information are used for behavior selection,referring to the memory module which holds the probabilityinformation representing the plausibility of each behaviorcorresponding to the will or context. The probability infor-mation is employed in confabulation process. Since the ar-chitecture contains a memory module, behaviors can belearned according to the changed situation.

Fig.3 The two-layered architecture for artificial creature

Behavior selection module is divided into threesub-modules: will-based confabulation, context-based con-fabulation, and an arbiter. In the will-based confabulationsub-module, most suitable behaviors are recommended bycalculating cogency using the current internal states. Thecontext-based confabulation sub-module calculates cogencyof the recommended behaviors by considering the externalcontext. Finally, the arbiter chooses a fittest behavior usingthe probabilities of the behaviors calculated by bothwill-based confabulation and context-based confabulation.That is, the resulting behavior is being recommendedthrough the confabulation process. Using this architecture,the artificial creature can show a behavior satisfying its owndesires and fitted to the external environment.

C. Confabulation Theory

Bayesian and cogent confabulation techniques are used torepresent formal logic for inductive reasoning. These twomethods reason by using the confidence to all conclusions. Inthe inductive reasoning, ifthe probability of a, b, c, and d aregiven as a partition of a sample space S and suppose thatevent E occurs, then the confidence of event E occurring isrepresented by using Bayesian posterior probability,p(elabcd). In cogent confabulation, on the other hand, theconfidence of conclusion is represented as cogency,p(abcde). Cogency can be calculated using Bayes' theoremas follows [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 constantnumber in any given situations. In general, these assump-tions are plausible approximations as follows:

[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)]where K is a constant.Once the first four probabilities are considered as a constant,

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having the maximum cogency is equivalent to maximizingthe probability, [p(ale) p(ble) p(cle) p(aoe)] [1], [2].This process is known as confabulation. In this paper, theconfabulation 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 isconclusion [1]. Inspired by this mechanism, behavior selec-tion is achieved by confabulation. The set of assumed factcorresponds to the artificial creature's will or the context ofthe environment. And conclusion set corresponds to the ar-tificial creature's behavior set. A lot of assumed facts ofwill-based confabulation are generated from the artificialcreature's internal states such as motivation, homeostasis andemotion. The confabulation process requires the behaviorprobability to each of the internal states, which is preservedin the memory module. The cogency of will-based con-fabulation can be computed using Bayes' rule as follows:

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 is defined as cogency value (expectation) of thebehavior to the will. bj ,j=1,2, ..., representsjth behaviorin a behavior set. p(w1 bj) is a conditional probability be-

tween ith will and jth behavior. I and m represents thenumber of behaviors and the number of wills, respectively.Once the cogency values between the behaviors and theirwill are calculated, the behaviors with highest cogency val-ues are recommended to the context-based confabulation.Since most of the animals load up to four objects intoworking memory at one time [25], similar number of be-haviors can be recommended.

from the will-based confabulation. p(ci brj) is a condi-

tional probability between ith context andjth recommendedbehavior. k and n represents the number of recommendedbehaviors and the number of contexts, respectively. Thecalculation is carried out for the recommended behaviorsfrom the will-based confabulation to compute the adequacyreflecting environmental conditions.

C. Arbiter

The arbiter decides the final behavior based on the results ofwill-based confabulation, E1ill (bj), and context-based con-fabulation, Econtext (brj). The behavior is determined by themax-product operation as follows:

E,rbjter (bs) = max[E_,,,,/ (brj ) Econtext (brj )], j = I1, , kwhere bs is the finally selected behavior.The behavior having the highest value among the recom-mended behaviors is selected as the final output.

IV. MEMORY-BASED BEHAVIOR LEARNING

As Fig. 4 shows, the memory module consists of contextmemory and internal state memory. Red arrows represent thelink between the behavior and the context and internal state.The probability of the behavior which is related to the con-text and internal state is saved in the memory. For instance, ahigh probability value is memorized if eating behavior in thekitchen is adequate.

Memoryl(Context) Memory2

(Internal State)

Fig.4 Memory module for confabulation

B. Context-based Confabulation

A lot of assumed facts of context-based confabulation aregenerated from the external environment such as time, placeand object. In this paper, when, where and what (time, placeand objects) are considered as contexts in a given situation.The cogency of context-based confabulation can be similarlycalculated using Bayes' rule as follows:

Econtext (brl) p(cl brl) p(c2 brl).). p(cn brl)Econtext (br2) p(cl br2) p(cpbr2) ... P(Cn br2)

Econtext (brk) p(cl brk) * p(c2 brk) * p(cn brk)where Econtext is defined as cogency value (expectation) ofthebehavior to the context. ci, i=1, 2, ..., n represents ith con-text, brj,j= 1, 2, ..., k represents jth recommended behavior

The memory-based learning technique guides the artificialcreature's predefined behavior patterns into adaptive be-haviors to survive in the new environment. This can be ac-complished by two steps. The first step is to give all of theconfabulation probabilities by an expert's hand as shown inFig. 5. If incorrect probabilities are given, plausible behav-iors cannot be expected and thus it requires careful settings.This step is used to set memory contents in the initializationprocess.

Fig.5 Initial learning process by an expert

The second step is the memory-based reinforcement learningusing feedback signals from changing environment as shown

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in Fig. 6. In the proposed structure, the user allows to granteither reward or penalty by patting or hitting an artificialcreature to learn a desired behavior at a given situation.

Fig.6 Memory-based reinforcement learning using feedback

In order to improve the reinforcement learning, confabula-tion probability gets increased when patting input is given ata particular situation and vice versa for inducing hitting inputas follows:

FPt (ci bj ) + 8 (if input is a reward)Ptemp (c bj ) = Pt (c, bj ) -85 (if input is a penalty)

Pt (ci b1) (otherwise)

where Ptemp represents a temporal probability by interacPt(Cilb1) is a conditional probability between ith contexjth behavior, and t and a represent time and learningrespectively. There is a constraint that sum of all prob;ties must be equaled to one.

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

E Ptemp (ci bj)i=l

By this technique, confabulation probability is adjusted suchthat the undesired behavior can be restrained from beingactivated.

V. EXPERIMENTAL RESULTS

A. Objectives

The goal of experiment is to create an artificial creaturewhich behaves as per its will considering the context in agiven situation. Context awareness enables it to carry out aproper decision in an unexpected environment. To achievethis purpose and to measure the performance of proposedarchitecture, the following desired scenarios among the be-haviors, context and will are assumed.i) Eating behavior would never be carried out around thetoilet.ii) Even though creature may not be hungry, eating behaviorwould be selected if the food is in the surroundings.

(6)

Ation,t andrate,

iii) Even though creature is sleepy, sleeping behavior will notselected if the bed is not in the surroundings.iv) If creature is too sleepy, sleeping behavior will be se-lected at any location.

B. Experimental Environment

The interactive learning method and the proposed behaviorselection mechanism are applied to the synthetic character,'Rity', which is implemented by OpenGL in a 3D virtualspace. The sampling rate ofthe computational model is set to0.1 sec. Rity has 12 DOFs, 46 sensor symbols and 73 be-haviors. It is composed of perception module, internal statemodule, behavior selection module, learning module andmotor module. More detailed descriptions are presented in[12]-[17].Internal states ofRity are defined based on those of a real dogsuch as motivation, homeostasis and emotion. Parametervalues from (3), (4) were set as follows: n = 2, m = 8, k= 3, 1= 40. : n = 3, m = 8, k= 3, 1= 40. The assumed facts ofwill-based confabulation are classified in Table 1.

TABLE. 1 ASSUMED FACTS OF WILL-BASED CONFABULATION

Internal WillState

Play (wl)Motivation Comradeship (w2)

Contact Comfort (W3)Pain (W4)

Energy (W5)Homeostasis Fatigue (w6)

Excretion (W7)Emotion Emotion (w8)

abili- Each element ofmotivation and homeostasis has three statesof high, mid and low. Emotion has five states of happy, sad,anger, fear, and neutral. Similarly, the assumed facts ofcontext-based confabulation are considered as in Table 2.

(7) In the experiments, when, where and what were consideredas contexts.

TART 2F2 AYMl JMF.n FACT OF C.ONTFhXT-RAWXD CONFAARIT1LATION

C. Experimental Result ofBehavior Selection

Each experiment describes the result of two-step learning. Atthe first step, all probabilities are initialized by an expert suchas developer or user. Then, reinforcement learning is con-ducted by interaction between user and artificial creature. Asuser hits or pats the artificial creature, it can give a reward or

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

Context PhaseMorning

Time (cl) AfternoonTime (c1) Evening

Night

Place (C2) Bed RoomPlace(c2) ~Toilet

FoodObject (C3) Comrade

Toy

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penalty for reinforcement learning. In other words, the be-havior probability is increased or decreased based on theuser's stimulus. The artificial creature is rewarded (pun-ished) if the selected behavior is appropriate (inappropriate)to the given context. In order to accomplish the objective ofcontext-aware artificial creature, the following four experi-ments were carried out.i) If the artificial creature ate, at a location near the toilet, itwas punished. In Fig. 7, probability value ofp(toiletleat) isplotted as the penalty is applied. The learning process on thisbehavior was carried out 12 times for 52 minutes. The graphshows that it has a decreasing tendency according to user'spenalty. Since the user forbade eating behavior around thetoilet, the probability of the artificial creature eating therebecame lower.

-+ Penalty S3ignta]lp(toilet eat)

0. 0 2

F. 00Probbilty cang w r l i applied0. _

0. 0 0 4 __

0.002

0.000

Fig. 8 shows behavior frequency before and after rein-forcement learning in the given context. Every result of be-havior frequency was observed for 15 minutes. Only somebehaviors of total 40 behaviors are plotted in the graph. The21st to 25th behaviors are 'STARING', 'EATINGQUICKLY, 'EATING SLOWLY', 'IXCRETING', and'URINATING'. Before learning, the artificial creature oftenate with the toilet in its vicinity. However, the frequency ofeating behavior at the toilet (22nd and 23rd behavior) wasreduced after reinforcement learning. Thus, the user canteach Rity proper behaviors at a certain situation by rein-forcement learning.From the next experiment onwards, behavior frequency afterlearning will be described in the subsequent graphs, ac-

cording to the proposed two-layered confabulation.

Fig.8 Behavior frequency before and after reinforcement learningfor scenario (i)

ii) The dominant relationship between will and context isillustrated in Fig. 9. In the experimental environment, thesituation was that the creature was not hungry and contextindicated that the food was in the vicinity. In this case, thecreature made a decision according to the dominance of willand context. The graph shows that the eating behavior (22ndand 23rd behavior) was expressed by the effect of food in thevicinity, even though the creature was not hungry. In otherwords, the frequency of the eating behavior was increasedonly if the context was the food in the graph. This resultcame from both the reinforcement learning of user and theproposed two-layered confabulation architecture.

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

iii) Fig. 10 shows that the behavior related to a specificcontext was not selected, even though the creature has a lowvalue of will. The 26th to 29th behaviors are 'SLEEPING','SNOOZING', 'DIGGING QUICKLY', and 'DIGGINGSLOWLY'. The graph describes that under low states ofsleepiness, the creature did not sleep, since the bed was not inthe surroundings. However, the creature slept frequentlywhen the bed was near.

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

iv) The behavior, which is not related to the context, wouldbe selected if creature has a strongest will. Fig. 11 shows thatthe sleeping behavior was selected at all locations becausethe creature was absolutely sleepy. When the creature wasonly a little sleepy, sleeping behavior was revealed mostly onthe bed.

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21 22 23 24 25Behavior

35U Others

30

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25 26 27 28 29Behavior

6

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21 22 23Behavior

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80U Bed

70 [ thers

60

50

D40

330

20

1 0

A Little Sleepy Very Sleepy

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

VI. CONCLUSIONS

This paper proposed a novel two-layered confabulation ar-chitecture, which considers both internal state and contextfor the artificial creature's behavior selection. In will-basedconfabulation, behaviors are selected considering the inter-nal states such as motivation, homeostasis and emotions. Theselected behaviors are forwarded to the context-based con-fabulation to consider when, where and what. The arbiterfinally decides the most fitting behavior among the sug-gested behaviors from the two confabulation layers.A learning mechanism, which modifies the probabilities inmemory module using user's reward and penalty inputs, wasproposed in order to train the artificial creature's behaviors.The artificial creature was embodied by having real crea-ture's characteristics using behavior sets and internal states.It could recognize the context of external environment usingexteroception and its will was produced by mimicking a realcreature's interoception mechanism. The experimental re-sults showed that the artificial creature could select the mostadequate behavior to meet its own desires and the context.

REFERENCES

[1] Robert Hecht-Nielsen, Cogent Confabulation, Neural Networks 18 pp.111-115, 2005

[2] Robert Hecht-Nielsen, The Mechanism of Thought, 2006 Int. JointConference on Neural Networks, July 16-21, 2006

[3] Robert Hecht-Nielsen, A Theory of Cerebral Cortex, ComputationalNeurobiology Institute for Neural Computation, December 22, 2004

[4] N.-S. Kuppuswamy, S.-H. Cho and J.-H. Kim, "A Cognitive ControlArchitecture for an Artificial Creature using Episodic Memory,"SICE-ICASE International Joint Conference 2006, Busan, Korea, pp.3104-3110, Oct. 2006

[5] K. Kawamura, D.C. Noelle, K.A. Hambuchen, and T.E. Rogers, "Amulti-agent approach to self-reflection for cognitive robots", in Proc.of 11th Int'l Conf. on Advanced Robotics, Coimbra, Portugal, June 30- July 3, 2003, pp. 568-575, 2003.

[6] K. Kawamura, R.A. Peters II, D.M. Wilkes, W.A. Alford, and T.E.Rogers, "ISAC: foundations in human-humanoid interaction," IEEEIntelligent Systems, July/August 2000, pp. 38-45, 2000.

[7] K. Kawamura, W. Dodd, P. Ratanaswasd, and R.A. Gutierrez, "De-velopment of a Robot with a Sense of Self', Proc. of the 6th IEEE Intl.Symp. on Computational Intelligence in Robotics and Automation(CIRA), Espoo, Finland, June, 2005

[9] P. Ratanaswasd, W. Dodd, K. Kawamura, and D. Noelle, "Modularbehavior control for a cognitive robot," 12th Int'l Conf. on AdvancedRobotics (ICAR), Seattle WA, July 18-20, 2005, in review.

[10] R. O'Reilly, T. S. Braver, and J. D. Cohen, "A biologically basedcomputational model of working memory", Models of WorkingMemory: Mechanisms of Active Maintenance and Executive Control,(A. Miyake and P. Shah, Eds.) Cambridge; Cambridge UniversityPress, 1999.

[11] S.-Y. Yoon, B. M. Blumberg, and G. E. Schneider, "Motivation drivenlearning for interactive synthetic characters," in Proceedings ofAutonomous Agents, pp. 365-372, 2000.

[12] Y.-D. Kim, J.-H. Kim and Y.-J. Kim, "Behavior Selection and Learn-ing for Synthetic Character," in Proc. of the Congress on EvolutionaryComputation, Portland, USA, pp. 898-903, Jun. 2004

[13] J.-H. Kim, K.-H. Lee, and Y.9. Kim, "The Origin of Artificial Species:Genetic Robot," International Journal of Control, Automation, andSystems, vol. 3, no. 4, pp. 564-570, Dec. 2005

[14] J.-H. Kim, K.-H. Lee, Y.-D. Kim and I.-W. Park, "Genetic Represen-tation for Evolvable Artificial Creature," in Proc. of IEEE Congress onEvolutionary Computation, Vancouver, Canada, pp. 6838-6843, Jul.2006

[15] K.-H. Lee, C.-H.Lee, H.-L. Choi, M.-J. Tahk and J.-H. Kim, "Char-acterization of Artificial Creature using Multi-objective Optimiza-tion," in Proc. of Congress on Evolutionary Computation, May. 2002

[16] C.-H. Lee, Y.-J. Kim and J.-H. Kim,, "Characterization of ArtificialCreature using Multi-objective Optimization," in Proc. of the 2002FIRA Robot World Congress, May. 2002

[17] Y.-D. Kim, Y.-J. Kim, J.-H. Kim and Jong-Rak Lim, "Implementationof Artifcial Creature based on Interactive Learning," in Proc. of the2002 FIRA Robot World Congress, May. 2002

[18] D. A.Norman and T. Shllice, "Attention to action: willed and automaticcontrol of action," Plenum Press, New York, 1980

[19] Yi Zhang, Attention Switching During Interruptions, Proc. ofthe ThirdInternational Conference on Machine Learning and Cybernetics,Shanghai, 26-29 August 2004

[20] C. Breazeal, A motivational system for regulating human robot inter-action, in Proceedings of the National Conference on Artificial Intel-ligence, Madison, WI, 1998, pp. 54-61.

[21] C. Breazeal, A. Edsinger, P. Fitzpatrick, B. Scassellati, Active visionsystems for sociable robots, IEEE Transactions on Systems, Man andCybernetics 31 (5) (2001).

[22] C. Breazeal, Designing Sociable Robots, MIT Press, Cambridge, MA,2002.

[23] C. Breazeal, Toward sociable robots, Robotics and Autonomous Sys-tems 42 (2003) 167-175.

[24] Cynthia Breazel, "Robot in Society: Friend or Appliance?", workshopon emotion-based agent architectures, pp 18-26, 1999

[25] N. Cowan, "The magical number 4 in short-term memory: A recon-sideration of mental storage capacity" Behavioral and Brain Sciences,24(1), 2001, pp. 87-185.

[8] M. Skubic, D. Noelle, M. Wilkes, K. Kawamura, and J.M. Keller, "Abiologically inspired adaptive working memory for robots," AAAI FallSymp., Workshop on the Intersection of Cognitive Science and Ro-botics: From Interfaces to Intelligence, Washington DC, October 2004.

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