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1. Il ruolo dei grafi
nell’AI Conversazionale Ibrida

2. The team
Dr.MariaDiMaro–Post-docincomputationallinguistics,expertiseinCommonGround
managementandClarificationRequests
MartinaDiBratto–PhDStudentincomputationallinguistics,expertiseinargumentation-based
dialogue
SabrinaMennella–PhDStudentincomputationallinguistics,expertiseincommonsense
representation
RobertoBasileGiannini–Masterstudentincomputerscience,workingonInfluenceDiagramsfor
ConversationManagement
DaniloEsposito–Masterstudentincomputerscience,workingoninstructionssequencegeneration
basedoncommonsensereasoning
MarcoGalantino–bachelorstudentincomputerscience,workingonexpressivespeechsynthesis
YegorNapadystyy–bachelorstudentincomputerscience,workingonNvidiaAudio2FaceREST
control

3. Modellinglanguagevs modelling
communication
LargeLanguageModelscancaptureconversationdynamicsbutnotthemotivesbehindthem
Beingbasedonmachinelearning,theycaptureregularitiesandcorrelationsbutcannotmodel
causation
Predictingthemostprobableutterance,givenwhatpeopleusuallysay,andpresentingitastheAI’s
positioniswrong
Choosingtotalk(ornottotalk)servespurposes:languageisnotanabstractsequenceofsymbolsto
bepredicted
Beingabletoconsider,ifnotunderstand,theconsequencesofitsownactionsisanecessarystepfor
machinestoproducelanguagewithactualintentsandavoidharmfulcontent
MachineLearningisonlyoneofmanypowerfultoolstounderstandlanguageandhumanbehaviour
ingeneral

4. Theoretical
Model
theoretical
framework built
upon the
observation of
communicative
behaviourpatterns
Evaluation
on the field
deployment of the
computational
model in human
centered tasks for
evaluation purposes
Computational
Model
formal
representation of
the theoretical
model
03
01 02
Modellinglanguagevs modelling
communication

5. It is a tale told by an idiot,
full of sound and fury,
signifying nothing.
Macbeth

7. Dialogue
system

8. Dialogueflow
ASR LLM TTS
ASR
Common
Ground
Representation
Decision
Making
LLM TTS
GenerativeAI isgood for Natural Language Generationbutnotfor
DialogueManagement

9. Illusionof intelligence
Braitenberg, V. (1986). Vehicles: Experiments in synthetic psychology. MIT press
Simplewiredvehiclescanleadhumansto
attributeintelligentbehaviourtomachineswhile
onlyreactivecapabilitiesaregiven
MachineLearningisverygoodatcleaningdata
byrecognisingunderlyingpatternsinsidenoisy
channels.
AreNeuralNetworksverycomplexBraitenberg
vehicles?Mayappeartobeintelligentbuttruly
nothaveactualintentionality
[…]whenwelookatthesemachinesorvehiclesasiftheywereanimals
inanaturalenvironment...wewillbetempted,then,tousepsychological
languageindescribingtheirbehavior.Andyetweknowverywellthat
thereisnothinginthesevehiclesthatwehavenotputinourselves

10. Austin, J. L. (1962). Speech acts.

11. Austin, J. L. (1962). Speech acts.

12. Conflict Search Graph
Di Maro, M., Origlia, A., & Cutugno, F. (2021). Cutting melted butter? Common Ground inconsistencies management in dialogue systems using graph databases. IJCoL. Italian Journal of Computational Linguistics, 7(7-1, 2), 157-190.

13. Bastian

14. The ladderof causation
Pearl, J., & Mackenzie, D. (2018).The book of why: the new science of cause and effect. Basic books.
Understandingone’sownroleintheworldgives
meaningtoproducinglinguisticacts
Machinelearningbasedagentsarepassive
observersoftheworldandcanonlyreacttoit,not
actinit
Modellingtheeffectthatactingintheworld
provides,includingspeaking,andconfrontingthe
consequencesallowstogeneratelanguageforits
intendedpurpose
Retrospectivereasoningisafundamentalability
thatreliesoncausalmodellingcapabilities

15. Doing
Tosaysomethingistodosomething;orinwhichbysayingorin
sayingsomthingwearedoingsomething
Austin(LanguagePhilosophy-1962)
Thefirstcognitiveability,seeingorobservation,isthe
detectionofregularitiesinourenvironment,anditissharedby
manyanimalsaswellasearlyhumansbeforetheCognitive
Revolution.
Thesecondability,doing,standsforpredictingtheeffect(s)of
deliberatealterationsoftheenvironment,andchoosingamong
thesealterationstoproduceadesiredoutcome.
Usageoftools,providedtheyaredesignedforapurposeand
notjustpickedupbyaccidentorcopiedfromone’sancestors,
couldbetakenasasignofreachingthissecondlevel.
Pearl(ComputerScience-2018)

16. Language asa tool
I havea dream
But it is not this day
When diplomacy ends, war begins
The first step in the development of taste is to be
willing to credit your own opinion
Blessed are those who are persecuted because of
righteousness
Does he look like a bitch?

17. Machine learning
Decisionmaking
Counterfactual
reasoning

18. The roleof graphs
Goodtorepresentdataina
humaninterpretableway
Backedbyconsiderablemath
Canbeprojectedinnumerical
spacesforthemachinetowork
with
Amodernwaytorecover
inferenceengines
Support grounding and
explainability

19. Networkanalysisalgorithmshelpstudying
theproblemfromamathematicalpointof
view.
TheHyperlink-InducedTopicSearch
(HITS)algorithmscoresnodesintermsof
Authority:howimportantthenodeisinthe
network
Hub:howimportantarethenode’s
relationships
Paglieri, F., & Castelfranchi, C. (2005). Revising beliefs through arguments: Bridging the gap between argumentation and belief revision in mas. Argumentation in multi-agent systems: First international workshop
Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5), 604-632.
The roleof graphs

20. Textembeddingsarenumericvectorsthat
capturethegeneraltopicsfoundintextual
content(movieplots)
Nodeembeddingsarenumericvectorsthat
capturetheroleofthenodeinthenetwork,
intermsofneighbourhood
Weightedembeddingscanbecomputed
usingtextsimilarities
Chen, H., Sultan, S. F., Tian, Y., Chen, M., & Skiena, S. (2019). Fast and accurate network embeddings via very sparse random projection. In Proceedings of the 28th ACM international conference on information and knowledge
management(pp. 399-408).
The roleof graphs

21. Interpretability:agraphisuninterpretableifanyofthegraphpatternsdescribingeachofthe
foreseencommunicationproblemsisactivated.Inthesecases,aclarificationrequestis
produced;
Completeness:aninterpretablegraphisincompleteifinformationneededtorespondtothe
userintentismissing.Inthesecases,arequestforinformationisproduced;
Coherence:acompletegraphisincoherentiflogicalconflictsarefoundinthebeliefgraph.In
thesecases,theadequatedisambiguationquestionisproduced;
Stability:acoherentgraphisunstableifthereareopenissues,likeunansweredquestions.In
thesecases,aquestionansweringstrategyisactivated;
Desirability:astablegraphisundesirableifitdoesnotexhibitthegoalpattern.Inthesecases,
themostusefuldialogmovetocreatethegoalpatternisproduced,likeanexplorationor
exploitationmove.
The roleof graphs

22. Communicationasa
graphstabilisationgame

23. Example

24. Decisionmaking
Bayesianmodelstakeinto
accountallthedatathatis
neededtotakeadecision
Canconsidermachinelearning
performanceontestsetsas
priors
Separatepriorsfromevidence
Considertheutilityofgraph
configurationsgiventhe
possibilitytotakedecisions
Wheredo the models come from?

25. Decisionmaking
Relevantsubgraph
extraction

26. Why(not) to speak?
Desire
ThegraphrepresentingthesituationIampartofisnotwhatIwantittobe.Whatisthe
linguisticactthatmaximisestheprobabilitythatthedesiredgraphconfigurationwillappear?
Competence
Iamnotabletoconfidentlypredictwhatisgoingtohappengiventhecurrentgraph
configuration.CanIaskaquestiontocollectmoreinformationandimprovemyprediction
capabilities?
Curiosity
Thecurrentgraphconfigurationprobablycontainsmissinginformation.CanIaskaquestionto
completeit?
Hypothesizing
ThingswentdifferentlyfromwhatIexpected.Whatwouldhavebeenthegraphconfigurationif
Ididsomethingdifferent?

27. Illocutionarystrength
Problem
complexity
InterpretabilityIncompleteness Incoherence Instability Undesirability
Unfilledcore
FE
Perception
Syntactic
Lexical
Reference
Unfillednon-core
FE
Information
optionality
Belief
strenght
Answer
complexity
StructuredQA
Entropy
Deliberate
Information
sharing
Explanation
RAG
Neutralconflict
CS conflict
-IAR conflict
+IAR conflict
Illocutionaryforces

28. BehaviourTrees

29. FANTASIA
Origlia, A., Cutugno, F., Rodà, A., Cosi, P., & Zmarich, C. (2019). FANTASIA: a framework for advanced natural tools and applications in social, interactive approaches. Multimedia Tools and Applications, 78(10), 13613-13648.
Origlia, A., Di Bratto, M., Di Maro, M., & Mennella, S. (2022). DevelopingEmbodiedConversationalAgents in the UnrealEngine: The FANTASIA Plugin. InProceedingsof the 30th ACM International Conference on
Multimedia(pp. 6950-6951).

30. The Metafamily–Maya
2017 –2023
Spatially aware presentations

31. The Metafamily–Bastian
2020 –2023
Common ground inconsistencies

32. The Metafamily–Jason
2021 –2023
User profiling and recommendation

34. Desideratum SymbolicAIStatistical AIMarkov
Random
Fields
Embedding Logical
Neural
Networks
Neuralnets asuniversalsolvers X X X
Allowspecialisedsub-units X X X X
Meta-learning/Multi-task X - X
Modular X - - X
Can use priorknowledge X X X
True reasoning X - - X
Variables X X X X
Symbol manipulation X ---
Genericmodels X X X X X
Causality - - ---
Planning capabilities X X -
Perception/reasoningblending - - X
PerformtrueNLU with novel
interpretationgeneration
-
Acquireknowledge throughNatural
Language
---
Learnwith lessdata ---

35. Conclusions
ModernAI providesgreattools to project physicalobservationsin n-dimensionalnumericalspaces, which
can representthe mindof a machine
A machine can onlycommunicatewith intentionifitisgiventhe chance to exploreand learnaboutthe
consequencesof the action of speaking
Psychologicaltheories aboutmotivationand emotionprovidedrivers for the machine to evaluatewhat
speakingor notspeakingimplies
Researchistoomuchorientedatdepletingthe mine of everytechnologythatispresented. Lotsof papers
showingthe applicationsof new models and littleresearchaboutthe reasonswhythesemodels work
A commercial pushtowardsbrute force approachesispresent: onlythe big players havethe strengthso
democratizationisa problem
Computer science ismaybethe mostintrospectiveamonghardsciences. Developingmachines thattalk
can help model intelligence and build tools to stimulatethoughtthroughunbiaseddialogues