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Towards Collaborative Creativity in
Persuasive Multi-agent Systems
Jean-Paul Calbimonte • Davide Calvaresi • Michael Schumacher
University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)
International Conference on Practical Applications
of Agents and Multi-Agent Systems PAAMS 2021
October 2021
@jpcik
2
Outline
Collaborative
Creative
Intelligent
Autonomous
Agents
Computational
Persuasion
Challenges Opportunities Impact
Road-map
3
Context: long-term patient support
Icons:
Freepik.com,
Smashicons,
Flaticon,
Eucalyp
persuasive
sustained support
• chronic diseases
• post-surgery
• cancer survivors
diagnosis
treatment
surgery
eHealth patient
support
+
persuasion
strategies
Decentralized care +
heterogeneous clinical data
4
Towards persuasive agents
Patient
τAgent
Coaching
τAgent
Aggregator
τAgent
Processing
τAgent
Patient
τAgents
Sensor τAgents
trajectory data
data acquisition
support +
recommendation
pre-processed
trajectories
predictions,
analytics,
classification
trajectory data
Calvaresi, D., Schumacher, M., Calbimonte, J.P.: Agent-based modeling for ontology-driven analysis of
patient trajectories. J. Med. Systems 44(9), 1–11 (2020)
persuasive eHealth
support ?
Creative agent
interactions?
Collaborative
agent
learning ?
5
Collaborative Creativity Model
domain
domain
individual
multi-domain
multiple fields
field
6
Challenges
1
Shared Language
Shared Knowledge
Interdisciplinarity
2
3
Collaboration & exploration
Competition
5
4
Ethics
6
7
Challenges: Shared language
❖ beyond vocabularies or abstract models
❖ include interaction protocols and patterns
❖ identify appropriate expressiveness
❖ represent high-level orthogonal concepts
❖ exchange ideas and hypotheses
Shared
understanding
domain-specific
representations
complex interaction
languages
creative conceptual
discussions
Shared language
8
Challenges: Shared Knowledge
specify different
types of knowledge
background from previous
studies / interactions
representations
of simulations
probabilistic
scenarios
validation criteria
novel ideas represented
as thought processes
mental models
not only operate
on facts
❖handle possibilities
❖possible invalid/unfeasible paths
❖inconsistencies and conflicts
9
Challenge: Interdisciplinarity
cross-fertilization and
exchange of ideas
❖reconcile expertise heterogeneity
❖integrate richness of collective diversity
❖difficulty of overcoming barriers across disciplines
❖reconcile different perspectives over the same subject
entirely different backgrounds
10
Challenge: Collaboration & exploration
❖collaboration schemes → allow sufficient freedom to innovate
❖agree on different targets → explore different approaches and variations
❖consider risky options or challenge current assumptions
❖act upon contradicting results
❖contrasting exchange of assumptions and outcomes
❖propose alternatives based on experimental hypotheses
Beyond individual agents’
goals and intentions
11
Challenge: Competition
cooperation
strategies
competing for solving
specific problems
claim their share after a
common solution was found
protect individual
interests
sharing ideas / elements that
constitute a collaborative
effort
❖temporarily withhold part of the technical details
❖exposing the general strategy
❖encourage other systems to provide feedback or
competing solutions
12
Challenge: Ethics
❖reinforce the user’s trust
❖explain how collaborative approaches emerged
❖establish transparent conditions for data use
❖transparency characterizing collaborative endeavors
❖influence of one party over the other participating entities
13
Collaborative agent creativity cycle
Problem
acceptance
Problem
finding
Preparation
Incubation
Illumination
Verification
Modification
Solution
?
?
?

P

S
14
Opportunities &
Building blocks
1
Persuasive multi-
agent systems
Collective computational
creativity
Agent Simulation
2
3
Domain models &
Knowledge Graphs
4
15
Persuasive multi-agent systems
MAS
❖ rigid persuasion strategies
❖ highly specialized
❖ difficult to be generalized
❖ unable to cope with new
user-scenarios
collective creativity
paradigm
identify shortcomings in their own strategy
brainstorm on potential ideas that contribute to other’s
persuasion assumptions
challenged to question their own facts
collective creativity dynamics, e.g. argumentation
support behavioral change
exploiting contextual knowledge
and persuasion goals
?
?
?
16
Collective computational creativity
❖models of computational creativity
❖creative machines
❖collaborate with
❖mimic,
❖inspire
❖improvisational computational creativity
❖introduce intentional creativity in virtual agencies
collective
computational
creativity
humans

P
17
Agent simulation
❖ Undertake or verify reasonable and possibly untenable plans and
theories
❖ Verify reasonably via simulations
❖ Verify the feasibility, cost, and sustainability of given solutions
❖ Generate pools of simulations to validate or refute each other
standing
❖ Extract unexpected outcomes
18
Domain models & Knowledge graphs
structure and link data according to models that
accurately reflect a particular domain
enable logical reasoning to infer implicit information and
answer queries through structured sub-graph matching
❖cross-domain transformational creativity language
❖able to exchange different hypotheses and engage in brainstorming sessions
❖extending domain models beyond current limits and assumptions
❖protocol to pass from the stages of incubation, illumination, and verification
Knowledge
Graphs
19
Impact & Applications
Combination strategies
Exploration strategies
Paradigm shifts
20
Combination strategies
.
combination of
previous knowledge
smoking cessation
dietary eHealth applications
music recommendation
mental health
behavior change assisted
by a multi-agent system
❖co-creation schemes
❖persuasion strategy goals mutually agreed upon.
❖collective interactions with other agents help to revise and enrich
strategies
different multi-agent
systems
existing evidence across
multiple domains
21
Exploration strategies
exploration of entirely new ideas
among collaborative agents
❖questioning certain limits of current strategies
❖exploration through simulation
❖risk assessment
❖exploration agreement and execution
❖evaluation of exploratory outcomes
22
Paradigm shifts
paradigm shifts in the way
a problem is addressed
❖questioning assumptions and fundamental decisions
❖different type of solution that can be further explored
and validated
❖propose a different formulation of the problem
❖include exploratory and combinatorial strategies
23
Road-map
Collective Creativity Language
• common language to exchange ideas and
hypotheses
• high and in-depth level
• semantically rich interconnected knowledge
• formalization:
• agent combination of ideas
• exploration protocols
• brainstorming sessions
• exchange of hypotheses
• verification procedures
Combination
• how agents may share and evaluate
each other’s strategies
• comparison and homogenization of
agent proposals
• alignment of knowledge and goals
• meaningful co-created ideas
• criteria that focus on the effectiveness
and novelty
24
Road-map
Exploration
• exploratory analysis of collaborative
creations
• data-driven simulation and dynamic
evaluation of divergence
• predictions from multiple agents
• when to stop scrutinizing and probing?
• limitations of relying only on simulations
• human-agent exploration scenarios
Transformation
• change over time & context change
• application domain evolution
• study of collective knowledge models
• social representation topic or
application-wise
• specification of risks
• computational representation of
hypotheses and assumptions
25
Road-map
Ethical Creativity
• risks of inducing or exerting influence
• avoid inappropriate manipulations or undesired effects
• transparency and accountability mechanisms
• multi-agent explainability
• generation of explicable representations of the creative process
• comprehensible outcomes for end-users
• data (re)use: privacy and justification
Questions?
Jean-Paul Calbimonte
University of Applied Sciences and Arts
Western Switzerland
HES-SO Valais-Wallis
@jpcik

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Towards Collaborative Creativity in Persuasive Multi-agent Systems

  • 1. Towards Collaborative Creativity in Persuasive Multi-agent Systems Jean-Paul Calbimonte • Davide Calvaresi • Michael Schumacher University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) International Conference on Practical Applications of Agents and Multi-Agent Systems PAAMS 2021 October 2021 @jpcik
  • 3. 3 Context: long-term patient support Icons: Freepik.com, Smashicons, Flaticon, Eucalyp persuasive sustained support • chronic diseases • post-surgery • cancer survivors diagnosis treatment surgery eHealth patient support + persuasion strategies Decentralized care + heterogeneous clinical data
  • 4. 4 Towards persuasive agents Patient τAgent Coaching τAgent Aggregator τAgent Processing τAgent Patient τAgents Sensor τAgents trajectory data data acquisition support + recommendation pre-processed trajectories predictions, analytics, classification trajectory data Calvaresi, D., Schumacher, M., Calbimonte, J.P.: Agent-based modeling for ontology-driven analysis of patient trajectories. J. Med. Systems 44(9), 1–11 (2020) persuasive eHealth support ? Creative agent interactions? Collaborative agent learning ?
  • 7. 7 Challenges: Shared language ❖ beyond vocabularies or abstract models ❖ include interaction protocols and patterns ❖ identify appropriate expressiveness ❖ represent high-level orthogonal concepts ❖ exchange ideas and hypotheses Shared understanding domain-specific representations complex interaction languages creative conceptual discussions Shared language
  • 8. 8 Challenges: Shared Knowledge specify different types of knowledge background from previous studies / interactions representations of simulations probabilistic scenarios validation criteria novel ideas represented as thought processes mental models not only operate on facts ❖handle possibilities ❖possible invalid/unfeasible paths ❖inconsistencies and conflicts
  • 9. 9 Challenge: Interdisciplinarity cross-fertilization and exchange of ideas ❖reconcile expertise heterogeneity ❖integrate richness of collective diversity ❖difficulty of overcoming barriers across disciplines ❖reconcile different perspectives over the same subject entirely different backgrounds
  • 10. 10 Challenge: Collaboration & exploration ❖collaboration schemes → allow sufficient freedom to innovate ❖agree on different targets → explore different approaches and variations ❖consider risky options or challenge current assumptions ❖act upon contradicting results ❖contrasting exchange of assumptions and outcomes ❖propose alternatives based on experimental hypotheses Beyond individual agents’ goals and intentions
  • 11. 11 Challenge: Competition cooperation strategies competing for solving specific problems claim their share after a common solution was found protect individual interests sharing ideas / elements that constitute a collaborative effort ❖temporarily withhold part of the technical details ❖exposing the general strategy ❖encourage other systems to provide feedback or competing solutions
  • 12. 12 Challenge: Ethics ❖reinforce the user’s trust ❖explain how collaborative approaches emerged ❖establish transparent conditions for data use ❖transparency characterizing collaborative endeavors ❖influence of one party over the other participating entities
  • 13. 13 Collaborative agent creativity cycle Problem acceptance Problem finding Preparation Incubation Illumination Verification Modification Solution ? ? ?  P  S
  • 14. 14 Opportunities & Building blocks 1 Persuasive multi- agent systems Collective computational creativity Agent Simulation 2 3 Domain models & Knowledge Graphs 4
  • 15. 15 Persuasive multi-agent systems MAS ❖ rigid persuasion strategies ❖ highly specialized ❖ difficult to be generalized ❖ unable to cope with new user-scenarios collective creativity paradigm identify shortcomings in their own strategy brainstorm on potential ideas that contribute to other’s persuasion assumptions challenged to question their own facts collective creativity dynamics, e.g. argumentation support behavioral change exploiting contextual knowledge and persuasion goals ? ? ?
  • 16. 16 Collective computational creativity ❖models of computational creativity ❖creative machines ❖collaborate with ❖mimic, ❖inspire ❖improvisational computational creativity ❖introduce intentional creativity in virtual agencies collective computational creativity humans  P
  • 17. 17 Agent simulation ❖ Undertake or verify reasonable and possibly untenable plans and theories ❖ Verify reasonably via simulations ❖ Verify the feasibility, cost, and sustainability of given solutions ❖ Generate pools of simulations to validate or refute each other standing ❖ Extract unexpected outcomes
  • 18. 18 Domain models & Knowledge graphs structure and link data according to models that accurately reflect a particular domain enable logical reasoning to infer implicit information and answer queries through structured sub-graph matching ❖cross-domain transformational creativity language ❖able to exchange different hypotheses and engage in brainstorming sessions ❖extending domain models beyond current limits and assumptions ❖protocol to pass from the stages of incubation, illumination, and verification Knowledge Graphs
  • 19. 19 Impact & Applications Combination strategies Exploration strategies Paradigm shifts
  • 20. 20 Combination strategies . combination of previous knowledge smoking cessation dietary eHealth applications music recommendation mental health behavior change assisted by a multi-agent system ❖co-creation schemes ❖persuasion strategy goals mutually agreed upon. ❖collective interactions with other agents help to revise and enrich strategies different multi-agent systems existing evidence across multiple domains
  • 21. 21 Exploration strategies exploration of entirely new ideas among collaborative agents ❖questioning certain limits of current strategies ❖exploration through simulation ❖risk assessment ❖exploration agreement and execution ❖evaluation of exploratory outcomes
  • 22. 22 Paradigm shifts paradigm shifts in the way a problem is addressed ❖questioning assumptions and fundamental decisions ❖different type of solution that can be further explored and validated ❖propose a different formulation of the problem ❖include exploratory and combinatorial strategies
  • 23. 23 Road-map Collective Creativity Language • common language to exchange ideas and hypotheses • high and in-depth level • semantically rich interconnected knowledge • formalization: • agent combination of ideas • exploration protocols • brainstorming sessions • exchange of hypotheses • verification procedures Combination • how agents may share and evaluate each other’s strategies • comparison and homogenization of agent proposals • alignment of knowledge and goals • meaningful co-created ideas • criteria that focus on the effectiveness and novelty
  • 24. 24 Road-map Exploration • exploratory analysis of collaborative creations • data-driven simulation and dynamic evaluation of divergence • predictions from multiple agents • when to stop scrutinizing and probing? • limitations of relying only on simulations • human-agent exploration scenarios Transformation • change over time & context change • application domain evolution • study of collective knowledge models • social representation topic or application-wise • specification of risks • computational representation of hypotheses and assumptions
  • 25. 25 Road-map Ethical Creativity • risks of inducing or exerting influence • avoid inappropriate manipulations or undesired effects • transparency and accountability mechanisms • multi-agent explainability • generation of explicable representations of the creative process • comprehensible outcomes for end-users • data (re)use: privacy and justification
  • 26. Questions? Jean-Paul Calbimonte University of Applied Sciences and Arts Western Switzerland HES-SO Valais-Wallis @jpcik