Neuromodulating Cognitive ArchitectureA New Step Towards Biomimetic Emotional AIJordi Vallverd uUniversitat Aut`noma de BarcelonaBarcelona, CataloniaEmail: jordi.vallverdu@uab.catMax Talanov, Radhakrishnan DelhibabuKazan Federal UniversityKazan, RussiaEmail: max.talanov,rdelhibabu@gmail.comManuel MazzaraInnopolis UniversityKazan, RussiaEmail: m.mazzara@innopolis.ruSalvatore DistefanoPolitecnico di MilanoMilano, ItalyEmail: salvatore.distefano@polimi.itAbstractThis paper introduces a newmodel of articialcognitive architecture for intelligent systems, the Neuromodu-lating Cognitive Architecture (NEUCOGAR). It is biomimeti-callyinspiredandadapts theneuromodulators roleof humanbrains intocomputational environments. This waywe aimatachieving more efcient Articial Intelligence solutions basedonthebiological inspirationofthedeepfunctioningofhumanbrain, which is highly emotional. The analysis of newdataobtainedfromneurology, psychologyandevenphilosophical oranthropological contributions of thestudyof theemotionroleinto cognitive processes allowus to generate a mapping ofmonoamineneuromodulatorsandtoapplyit tocomputationalsystem parameters. Thus we can make articial cognitive systemsthat better perform more complex tasks (regarding informationselection and discrimination, attention, innovation, creativity,)aswell asabletoshowaffordableemotional relationshipswithhuman users.KeywordsAI, AffectiveComputation, AffectiveComputing,Affective Modeling, Cognitive Architecture, Cognitive Modeling,ComputingEmotions, Machine Thinking, Model of Emotions,Model of Emotional Thinking, Neuromodulation, Neurotransmis-sion.I. INTRODUCTIONAcognitive systemis, at a biological level, a systemthat collects datafromtheenvironment andprocesses it inorder to produce an answer. According to the complexityof the data gathering, processingandrange of actions, wecantalkabout minimal or normal cognition. AmonglivingsystemswithhigherEncephalizationIndex, andbeingall ofthem mammals (humans at the top of this scale, followed bycommon bottlenose dolphins), we can nd an important char-acteristic: intensivelydevelopednervous systems withgreatbrains and complex biochemical interactions that modulate thebrain activity and, thus, generate complex and social responses.When theoreticians devoted to cognition processes startedto model them, considered symbolic processes as the highestand purest forms of cognitive processes. Consequently theformalization and discretization of human thinking because apriorityandthecomplexitiesofhumanlanguage, thenaturalvehicle of though, became the source of all analysis: grammars,logical formulation, modular understanding. Theseideasarebehind the birth of Articial Intelligence (AI), understoodclassically as a GOFAI project. Unfortunately, the complexitiesofhumanlanguageandsemanticsledAIexperts, despiteofextraordinary successes provided by several generations ofexpert systems, to a bottleneck: most important dynamical andcreativeaspectsofhumanmindscouldnotwerecapturedbytheseformal approaches, evenaftertheintroductionofnon-monotonic or fuzzy logics.At this point, a newgeneration of cognitive scientistsandroboticistsproposedaturnover: byonehand, emotionswere a fundamental part of the cognitive processes (atten-tion, motivation, strategyselection, mooddisposal, reaction,invention, among a long list); on the other hand, the intrinsicrelationship between minds and bodies led to the birth of em-bodied cognition. This allowed the emergence of a second andpowerful wave of cognitive and robotics experts lead by peoplelikeRodneyBrooks[4], [5] or JamesDeLancey(Robotics)[8], AndyClark(Philosophy) [6], AntonioVarela(Biology)or AntonioDamasio[7], Ramachandran[13] andRizzolatti(Neurology) [14]. This way, cognitive scientists like JamesGibson talked about ecological thinking (previously explainedby Tetsuro Watsuji [20] from a different disciplinary view, butbased on the same principles) and even phenomenologists likeHusserl, Merleay-Ponty or Heidegger were introduced into thedebate.In the middle of this cognitive revolution that led to embod-ied robotics, enactive cognition or morphological computationideas,emergedoneimportantdisciplineasamainreference:neurology. And neurologists pointed to the revolution of in vivoscanned brains (by EG, fMRI, for example), to the relevance ofemotional processes into the whole cognitive system processesand, even, to the explanation about the emergence of the con-sciousness (Damasio, Llin as [9]). Consequently, researchersinAI or cognitive science startedtointroduce ideas aboutemotional processing:a) Neural networks usingdrive nodes byStephenGrossberg in 1972,b) During the 1970s Japan invested into Kanseiengineering, anemotional orientedmachineap-proach,c) Duringthe1980semotional cognitivearchitec-tures like CogAff by Aaron Sloman, or the OCCmodel of Ortony on cognitive appraisal theory forcomputational models of emotion generation.d) Inthemiddleof 1990s wereborntheeldofaffectivecomputingandsocial robotics(byRos-alind Picard [12] and Cynthia Breazeal [3]),e) at 21st century beginning leading researchers likeMarvinMinsky[11] reafrmedtheinextricableconnection between emotions and cognition, ask-ing directly for the achievement of emotionalmachines.Consequently, we can afrmthat there is a huge listof bioinspiredarticial cognitivearchitectures, whichtrytoemulateat acertainlevel theroleof emotionsincognitiveprocesses.II. OUR COGNITIVE PROPOSAL: NEUCOGARHere is presentedour ownapproachtoarticial cogni-tive architectures, the Neuromodulating Cognitive Architecture(NEUCOGAR). What wesuggest istocreateamappingofmonoamine neuromodulators applied to computational systemparameters. First of all we need to state several points:1) Emotionsarenatural andnecessarymodulators, re-inforcedandmodeledat the same time bysocialexternal factors or even internal thoughts2) There several modular models: Geneva emotionwheel, Plutchicks wheel of emotions, L ovheimCube of emotion [10] all these models explain,following different strategies, which are the basicemotionsandhowdynamictransitionsamongthemare created;3) Monoamine neuromodulators are the mechanisticways by which emotional responses are triggered andmodulated within cognitive systems like humans.Our work is focused on the modeling of mapping ofimpacts of monoamine neuromodulators on human brain intocomputational processes of modern computers. We propose theapproachtocorrelatebiochemical inuenceof monoaminessuchasdopamine, serotonin, andnoradrenaline, involvedinaffectiveprocessingofcortical, limbicandothersubsystemsof human brain with a computational processes: computationalpower, memory distribution, learning, storage, decision makingthattakeplaceincomputationalsystems.Itcouldbeconsid-eredas abaseof affectivecomputationframeworkandwehope could be used in several domains.In order to achieve our results we therefore use theideasofL ovheim[10], basedonathree-dimensional modelfor emotions and monoamine neurotransmitters (serotonin,dopamine, noradrenaline). As each of these three monoaminesystemsprobablyrepresentsadifferent aspect ofemotion, ahypothetical three-dimensional space for possible combina-tions is formed. It is evolutionarily rational that the monoaminesystems are mutually orthogonal as this maximizes the amountof information that can be transmitted, however, althoughlikely, this needs tobefurther establishedempirically. Thevertexesofthemodel areaffectsasdenedbytheTomkinstheory. The psychologist Silvan Tomkins devoted his lifeto the study of emotions and developed an elaborate andcomprehensivetheoryof basicemotions. Tomkinsidentiedeight basic emotions [19], which he labeled with one word fortheemotionwhenit wasoflowintensityandanotherwordforthesameemotionatahigherintensity. Tomkinsreferredtobasicemotionsasinnateaffectswhereaffect[15], [16],[17], [18], inhis theory, stands for the strictlybiologicalportionof emotion. Accordingtohistheory, thesearetheeight basic emotions: Enjoyment/Joy, Interest/Excitement, Sur-prise, Anger/Rage, Disgust, Distress/Anguish, Fear/Terror andShame/Humiliation. Someofthemaremanagedonlybytheinternal processes of the user, but some other are intrinsicallyrelated to socially interactions that explain the way by whichare produced. From an anthropological perspective, for exam-ple, it isveryclearthat thereareculturesofshame(mostlyEastern), andcultures of guilt (mostlyWestern) (Benedict,1946) [2]. L ovheim gives extended explanation of mapping ofeachneurotransmittertogroupofemotionsthat heinheritedfrom Tomkins [15]. We placed several quotes here from [10]for the sake of justication of neurotransmitters choice.A. Monoamines Neuromodulators toComputingParametersMappingMostlylowlevel inuenceofaffectsonbrain, wecouldcall it cellular or neuronal, isdescribedaboveandprovidesfundamental basefor our affectivecomputationmodel. No-radrenaline, serotonin, dopaminedonothavedirectmappingon computational processes, for obvious reasons current com-puters do not have anything in common with biochemicalprocessesinneurons. Themost reasonablewayweobserveat the moment is to create an indirect mapping based onrole of each neuromodulator involved in human emotions.Inother wordswedrawananalogybetweencomputationalprocesses in computer and neurotransmission in brain and cor-responded biochemical inuence of neuromodulatory systemson neurons with inuence of virtual neuromodulators levels oncomputational processes of a machine. We used several workstogainproperpictureandunderstandingofneuromodulationand neuromodulatory systems. Especially interesting from ourperspectiveis[1]wheretheroleofdopamine, serotoninandtheir impact inemotional context is discussed. It couldbeconsideredasoneofbasesofourworkinneuromodulationdomain.B. MaintainingtheIntegrityof theSpecicationsEmotionalNeuromodulatorsDopamine. Inthemammalianbrain, dopamineappearstoplayamajor roleinmotor activation, appetitivemotiva-tion, rewardprocessingandcellularplasticity, andmight beimportant inemotion. Dopamineiscontainedintwomainpathways that ascendfromthemidbraintoinnervatemanycortical regions. Dopamine neurons in the monkey have beenobservedtore topredictedrewards. Moreover, dopaminereceptors are essential for the ability of prefrontal networks tohold neural representations in memory and use them to guideadaptive behavior. Therefore, dopamine plays essential roles allthe way from basic motivational systems to working memorysystems essential for linking emotion, cognition and conscious-ness. AccordingtoL ovheimcubeof emotionsdopamineisassociatedwith[r]eward, reinforcement, motivation. Fromcomputationalsystemperspectiveweinterpretdopamineim-pactlike:itplaysrolein:rewardprocessingthusindecisionmaking, workingmemory-memorydistributionandstoragein computing system, motivation - decision making.Serotonin. Serotoninhasbeenimplicatedinbehavioralstateregulationandarousal, motorpatterngeneration, sleep,learning and plasticity, food intake, mood and social behavior.The cell bodies of serotonergic systems are found in midbrainand pontine regions in the mammalian brain and have extensivedescending and ascending projections. Serotonin plays a cru-cial role in the modulation of aggression and in agonistic socialinteractionsinmanyanimals. Incrustaceans, serotoninplaysaspecicroleinsocial status andaggression; inprimates,withthesystemsexpansivedevelopment andinnervationofthe cerebral cortex, serotonin has come to play a much broaderrole in cognitive and emotional regulation, particularly controlof negative mood or affect. L ovheim associates serotonin with[s]elf condence, inner strength, and satisfaction. Thus ourinterpretation of inuence of serotonin systemlooks like:decision making of the systemis inuenced by serotonin,condenceandsatisfactionascoloringof theknowledgeisimpacted by serotonin too, this way serotonin should inuencetraining of machine and storage of the information learned.Noradrenaline. L ovheim cube of emotions emphasizes thenoradrenaline role in: Attention, vigilance, activity. Thenhedescribestheroleof noradrenaline: whilenoradrenalinehasbeencoupledtotheghtorightresponseandtostressandanxiety, andappears torepresent anaxis of activation,vigilance andattention. Robert D. Hunt describes role ofnoradrenaline (NE) and its impact on cognitive functionsnorepinephrine: NE has an emerging role in several essentialprocesses: (1) maintaining and increasing overall arousal,(2) contributing to affect regulation related to excitabilityandresponsetodanger or opportunity, and(3) contributingtomemorystorageandretrieval, especiallyaffect-relatedoremotionallyintenseevents. WhileNEhas acritical roleinemergencyresponse, it alsoassists inmaintainingbasal ortonic alertness. At a quieter moment, reading a book, studyingat night, theeffort toremainalert andstayontaskpartiallymediatedbyNE. Thenoradrenalineor it is better tocallit virtual noradrenaline could inuence modern computationalsystem like this: the attention and concentration could impactcomputing power and memory distribution between processesand threads (here we mean the operating system processes andmemory available for operating system), alertness also impactdistributionofcomputingpowerandmemoryofthesystem.The decision making is inuenced by alertness effect ofnoradrenaline reducing number of options in the observation,and possibly making system use more risky choices.III. COMPUTATIONAL SYSTEM PARAMETERSWe have introduced several computing system parametersinprevioussections.Accordingtotheirnaturewesplitthemintwogroups. Intherst most obviousgenericparametersof computing systemsuch as computing power, memorydistribution, learning and storage are included. Decision mak-ingismassivelyimpactedbyaffects(emotions)thatmadeususesecondspecial groupincluding: condence, satisfaction,motivation, number of options toprocess, tendencytouserisky choices. The computing power and memory distributionare closely related parameters that heavily inuence resourcesof a system used for current activity (cognitive process). Thestorageisinuencedbylearningformingtheinformationtostore during the training. The decision making group representsparameters and coloring (tagging, agging) of the trainedinformationexploitedduringthedecisionmakingprocesses.The condence impact on decision making is one of the mostobviousandimportant, eventheselectionoftheinformationis done takinginaccount howsystemis condent inthisinformation. Thesatisfactionisoneof most important agsinvolvedinrewardsystemusuallymakingsystemtoselectmostsatisfactory/pleasurableactions(approaches). Themoti-vationdrives systemtomakemost desirableselections andheavily inuences selections of further activities. The numberof options to process and inclination to risky actions are likedto noradrenaline stressful situation alertness; both of themmake system does quick decisions with tendency to risk.Our understanding of the role of neuromodulators is repre-sented in following mapping of neuromodulators to computingsystem parameters.Computing system parameters1. Generic:i Computing power: noradrenalineii Memory distribution (attention): noradrenalineiii Learning: serotonin, dopamineiv Storage: serotonin, dopamine2. Decision making/reward processing:i Condence: serotoninii Satisfaction: serotoniniii Motivation, wanting: dopamineiv Number of options to process: noradrenalinev Risky choices inclination: noradrenalineThe previous concepts are here briey explained:Generic:Computing power: distribution and priority of parallelprocess or load balancing, is impacted by noradrenaline: thehigher the level of noradrenaline is the more computing powermust be concentrated on current activity (neuromodulatorregulating attention).Workingmemory(shortterm)distributionandconcentra-tion is impacted by noradrenaline (attention).Learning is impacted by serotonin and dopamine:dopamine plays major role in activation of previouslyremembered patterns and serotonin in pattern generation.Storage management (long term memory) is impacted by bothby serotonin and dopamine, higher concentrations of bothneuromodulatorsmakessystembetterrememberstimulus. Ingeneral, strong emotions generate more persistent memories.Decision making:This decision making is done mainly in deliberation andlearnedreactionlayers of Model of six[11]. Parameters:condence, satisfaction, riskyare usedtohighlight actionsstored (remembered).Condence andsatisfactionof the systemis inuencedbyserotonin. Systemismoremotivatedunder theinuenceof dopamine. Systemtends to choose risky actions undertheinuenceof noradrenaline. Noradrenalinemakessystemconsider a smaller number of options in width and depth to beprocessed during deliberation.This mapping is exhaustively described in Computationalemotional thinking and virtual neurotransmitters. It could beused as a low level model of emotional processes and could beused as a basic framework for the emotion enabled systems.IV. THE TECHNICAL SPECIFICATIONS OF NEUCOGARcomputing powermemory distr.risky choicesnumber of optionsstoragelearningconfidencesatisfactionstoragelearningmotivationserotoninnoradrenalinedopaminedistress surpriseangerinterestfearjoydisgustFig. 1. Computing system emotion parametersNEUCOGAR is based on L ovheim(2012) model onneurotransmitters. InFig. 1thecubeof emotions is shownas originally introduced by Hugo L ovheimin his seminalpaperonthetopic. Theinformationonthecubeisextendedtoinclude computational parameters like computingpower,storageandmemory. Thisincrement canbeconsideredoneof the major contribution of this paper.Fig. 1representsthemissinglinkbridgingneuroscienceand psychology language and concepts to software engineerslanguageandunderstanding. This paper aims at buildingacommonunderstandingbetweenthesethreeworldsandpos-sibly foster a productive cooperation between specialist of allthese elds.A. Cognitive Architecture AnalysisTo understand the current scientic state of affairs, existingmodels and implementations in actual code, and to nd properabasefor our implementationweusedthemost traditionalway: runa comparative analysis. It worthtomentionthatwedont want tolimit ourselves withtheemotion-orientedarchitectures; we rather want to get a wide view on the currentsituationinthedomain. Weanalysed27cognitivearchitec-tures. Criteria are organized in three groups: emotional groupdepictsourinterest inemotionsimplementationincognitivearchitecture, thinking levels are the compatibility with [11], AIcomponentsgroupisusedtogainunderstandingofwidthofcoverage of AI domains by cognitive architecture. We used twoadditionalcriteriathatseemtoplayimportantroleandwerenot in previous groups: parallel processing, self-emergent/self-organized. Exhaustive analysis is available on-line.We used primitive Boolean approach to measure if compo-nent or emotional criteria are in specic cognitive architecture.Cumulative table is available on-line, it contains simple sum-mary of the Boolean criteria. According to our brief overview1of the list of architectures most interesting are: ASMO,CLARION, DUAL, H-CogAff, LIDA, Psi-Theory, Soar, So-cietyof mind(*), WASABI, EMA, Hikonen, Shanahan. H-CogAff is more of philosophical framework to build thecognitive architecture, or a meta-architecture that has the mostsignicant potential to be the most advanced at the moment andthe least limited. Homeostatic principle of Psi-Theory seems tobe ubiquitous in the psychological basis of emotions Society ofmind needs further analysis and possible update of our criteria.B. Spiking Neural Networks (SNN) AnalyisisIn order to validate the proposed model implementation isnecessary. SpikingNeural Networks(SNN)(Gerstner, 2001)[21]haveapropermechanismforneuromodulationsandarethemost suitablecandidatetovalidatetheproofofconcept.This choice has to be run based on the following criteria:I. Presence of neuromodulatory systemsa Dopamineb Serotoninc NoradrenalineII. Possibility to construct a simplied model of:a VTAb Substantia nigrac Raphe nucleid Nucleus accumbense Striatumf Hippocampusg Frontal cortexh Locus coeruleusi AmygdalaThesethreeneuromodulators wereselectedaccordingtomainrolein[10], thiswaywecouldnot underestimatetheroleof noradrenaline, dopamine, serotoninneuromodulationin the affects/emotions processing in human brain thus inarticial SNNs. Thereareseveral regions of thebrainthatplays important roleinneuromodulationandwewantedtoestimate the option and possibility to implement these regionsin computational SNN. Comprehensive description of the brainareas: Ventral tegmental area (VTA) and Substantia nigra playkeyroleinproductionof dopamineanddopaminepathway.DopamineroleisextensivelydescribedinSectionII. Raphenuclei - is a key producer of serotonin in human brain it playsmajorroleinserotoninneuromodulaton. Serotonininuenceisalsodescribedabove. Nucleusaccumbens-VTAprojectsdopamine inuence to the nucleus accumbens along withdopamine pathway thus nucleus accumbens plays crucial rolein dopamine neuromodulation: reward system, pleasure, motor,compulsion, preservation. Striatum - Substantia nigra projectsprojectsdopamineinuencetothestriatumwhichinitsturn1http://en.wikipedia.org/wiki/NEST(software)plays important role in reward processing, novelty-relateddecision-makingbehaviors, workingmemory. Hippocampuspalys mojor role is momoryfunctions of the brainandisinuenced both by serotonin and dopamine. Frontal cortex ismainly the brain zone of conscious and deliberative thinking,it plays crucial role in both serotonin and dopamine pathwaysand noradrenaline system. Locus coeruleus - is main partofnoradrenalinesystemthat producesthenoradrenalineandprojects its inuences to sveeral brain regions like: amygdala,neocortex, hypothalamus, hippocampus, striatum etc. The amy-gala - sends projections to the hypothalamus, the dorsomedialthalamus, the thalamic reticular nucleus, the nuclei of thetrigeminal nerve and the facial nerve, the ventral tegmen-tal area, thelocuscoeruleus, andthelaterodorsal tegmentalnucleus. Amygdala plays key role in emotional memories,pleasant (happiness) or unpleasant (fear, anxiety, sadness)emotions, etc.As theresult wehaveselectedtheNEST is particularSNN with in-built implementation of dopamine for neuromod-ulation. We suppose this is most close toour expectationsSNNwithpossibleimplementationoftheaffects(dopamine,serotonin, noradrenaline neuromodulation) taken in account.Our model is based on [10]s view on the affects as inbornmost primitive emotional reactions of the nervous system.ThemodelcalledCubeofemotionscreatesimportantlinkbetween psychological theory of affects [15], [16], [17], [18].This neuropsychological bridgeis usedbyus tocreateex-tendedmappingtocomputational systemparameters. Fromcrossdisciplinary perspective we have created the link betweenpsychological perspective to neurophysiological perspective tocomputational systemperspectiveviamappingof lowlevelcellular mechanisms to computatinal system parameters takingin account neuromodulators roles in inuence over humanthinking processes.V. CONCLUSIONSAccordingtothe theories andlatest results of a trans-disciplinaryviewoncognition, canbecompletelyafrmedtheembodiment ofcognitiveprocesses. Itsamatteroffactthat most abstract process performed by the brain are directlyrelated to sensorimotor processes. At the same time, themind/bodilymanagement isrelatedtoemotional signalsthatcapture, process and classify data inputs (external or internal,youneedtoremember themulti-modalityof emotions) andallowresponses.Thefundamentalroleofemotionsintocog-nitiveprocessesis, thus, amatter of fact for anyresearcherintheeld. At thesametime, weveseenthat bio-inspiredapproaches to articial cognitive systems, like affective com-puting, emotional cognitive architectures or neural networks donot provide a realistic approach to the emotional dynamic datamanagement processes. At the same time we need to admit theutilityandgoodresultsinseveraloftheseapplicationelds,relying on the old fashioned ideas of Classic AI, or GOFAI.Ourapproachisbetterforseveral reasons: a)rst ofall,becausewedonot needtorunawholebrainemulationinordertoachieveafunctional articial cognitivesystem, andthis allow us to avoid the still existing and deep uncertaintiesstill to be claried between brain structure and performance (atthis historical moment, brain researchers are at the same pointthat genetic biologists starting to decode the genome: with a lotof information, in fact too much, but without meaning); b) sec-ondly, because we are not qualitatively imitating the surface ofthe emotional syntax of human brains and bodies, but insteadof it, we are suggesting a new approach that is rooted into theneuromodulators syntax, the key piece of brain emotional andcognitive processes; c) thirdly, this functional approach allowsthegenerationofmoredynamicsystemsthat addscognitivepower beyond the basics of classic weights, thresholds or va-lences, making possible a cognitive architecture that generatesthe several faces of emotional interactions suchas moods,attitudes, emotional memories or affective states. NEUCOGARmakes possible the implementation of emotional elements intothe whole system, acting as the basic piece of regulation,and not as an attached module with emotional contents. Thisarchitecture allows to apply it smoothly from the scratch (thearticial neuron or unit of processing) to the superior layers ofspecialized cognitive processes (like memories, processors). Inthis sense, NEUCOGAR is a whole articial cognitive systemthatemulatesthebasicrunningofbodilyemotionalsystems,following a multilayered o modular architecture that is put to-gether and ruled by the same emotional ciberneuromodulators.Inthis sense, wearecreatinganewEmbodiedAI withoutreal instantiatedphisicalities. Wetakealsoinconsiderationthe fact that anylivingsystemis goal-orientedandthat itmust beimplementedintooursystembeforetoachieveanyresult. Thepoint isthat wecandecidearticiallythegoalsandthenapplytherealmechanismsofcognitiveprocessing.NEUCOGARwillbesymbioticallyusefulforAIresearchersaswell asforneurologiststhat want tochecksomeoftheirhypothesis about main chemical dynamics in data processing.A new technology for a new horizon of intelligent systems.ACKNOWLEDGMENTSPartial support for this researchwas receivedfromtheSpanishGovernments DGICYTresearchproject: FFI2011-23238, Innovation in scientic practice: cognitive approachesand their philosophical consequences.REFERENCES[1] Arbib, Michael A., and Jean-Marc Fellous (2004). Emotions: from brainto robot. Trends in cognitive sciences 8.12, pp 554-561.[2] Benedict, R. (1946). TheChrysanthemumandtheSword, HoughtonMifin Publisher, ISBN 0-395-50075-3.[3] Breazeal, C. (2002). Designing Sociable Robots, Cambridge, MA: TheMIT Press, ISBN: 9780262524315.[4] Brooks, Rodney A. (1991).Intelligencewithoutrepresentation, Arti-cial Intelligence, 47,pp. 139-159.[5] Brooks, Rodney. (1994). 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