1. From Inter-Agent to Intra-Agent
Representations
8 March 2014 - ICAART presentation
Giovanni Sileno g.sileno@uva.nl
Alexander Boer
Tom van Engers
Leibniz Center for Law – University of Amsterdam
mapping social scenarios to agent-role descriptions

2. Fundamental division:
“artificial” social systems  norm-driven
“natural” social systems  norm-guided
In the latter, non-compliance (intentional or not) is
systemic.
Example: Humans!

3. In “natural” social systems, agents do not have a blue-
print describing the “implemented” behaviour of other
components.
Still, social agents need to understand/interpret, to an
adequate extent, how the others behave!

4. Where knowledge comes from?
In “natural” social systems, agents do not have a blue-
print describing the “implemented” behaviour of other
components.
Still, social agents need to understand/interpret, to an
adequate extent, how the others behave!
How we transmit knowledge about people’s behaviour?

5. The social function of “stories”
“Many different root metaphors have been put
forth to represent the essential nature of human
beings: homo faber, homo economous, homo
politicus, [...], “rational man”. I now propose
homo narrans to be added to the list.”
Fischer [1984], Narration as a Human Communication Paradigm
Stories are “constituents of human memory,
knowledge, and social communication”
Schank and Abelson [1995], Knowledge and memory: the real story

6. What “stories” are
Not only fictional narrations..
but also:
• personal experiences
• journalistic reports
• medical cases
• legal cases
• … and any other expert domain case!

7. What “stories” are
Not only fictional narrations..
but also:
• personal experiences
• journalistic reports
• medical cases
• legal cases  social behaviours with
their legal interpretation
• …

8. A relevant issue
Unfortunately, stories, by their own nature, are
partial (ill-defined) representations.
What is in a story depends on
• default assumptions
• common-sense knowledge
• expertise
• interest
• focus
• ...

9. A relevant issue
Unfortunately, stories, by their own nature, are
partial (ill-defined) representations.
What is told in a story depends on
• default assumptions
• common-sense knowledge
• expertise
• interest
• focus
• ...
of the narrator !

10. A relevant issue
Unfortunately, stories, by their own nature, are
partial (ill-defined) representations.
What is read in a story depends on
• default assumptions
• common-sense knowledge
• expertise
• interest
• focus
• ...
of the listener !

11. Our objective
We look for a methodology to acquire the
systemic knowledge of the narrator, concerning a
given social scenario, in a computational form.

12. Our objective & requirements
We look for a methodology to acquire the
systemic knowledge of the narrator, concerning a
given social scenario, in a computational form.
• bypass natural language issues
we are not targeting a story understanding
application, but a scenario acquisition tool

13. Our objective & requirements
We look for a methodology to acquire the
systemic knowledge of the narrator, concerning a
given social scenario, in a computational form.
• bypass natural language issues
we are not targeting a story understanding
application, but a scenario acquisition tool
• target non-IT experts (in principle)
we will refer mostly to diagrams, or programming
based on high level and “intuitive” languages
 affinity with scenario-based
modelling

14. A very “simple” story
A seller makes an offer, about a certain good,
for a certain amount of money. A buyer
accepts. The buyer pays. The seller delivers.

15. Outline of the methodology
1. start from an inter-agent description
2. enrich it with intentional/institutional factors
3. synthetize it in intra-agent models

16. Inter-Agent Description: MSC
Message Sequence Charts (MSC) formalize UML sequence
diagrams. They are intuitive and commonly used.

17. Inter-Agent Description: MSC
Issues with:
• “ontological” identities
• Neglecting side-effects/control-loop
• implicit ordering

18. Inter-Agent Description: Topology
Identity is epistemic
(assigned to message boxes)

19. Inter-Agent Description: Flow
Problems:
• Consecutiveness vs Consequence
“the mainspring of the narrative activity is to be traced to that
very confusion between consecutiveness and consequence,
what-comes-after being read in a narrative as what-is-
caused-by”, Barthes [1968]
• Story-relative vs Discourse-relative timelines
ordering as events occur or how they are told/observed

20. Inter-Agent Description: Flow
Three levels of constraints on events:
• dependencies (logic or causal)
• relative/absolute time indexing
• discourse ordering
The first is by far the most important to our scope.
An important step is the recognition of sub-
systems operating concurrently (e.g. agents,
cognitive modules)

21. Inter-Agent Description: Flow

22. Outline of the methodology
1. start from an inter-agent description
2. enrich it with intentional/institutional factors
3. synthetize it in intra-agent models

23. First steps toward an agentic
characterization
• Intentional characterizations
 what the agents want?
• Hidden acts
 is there something else that they perform?
(e.g. evaluations, information retrieval)
• Critical conditions
 is there any condition necessary for the performance
and the effectiveness of an action?

24. Agentic Characterization on MSC

25. Agentic Characterization on MSC
Issues:
ontological–
epistemic
emission–
reception

26. Agentic Characterization: Refinement
• Task decomposition per agent
(additional independent flows)

27. Agentic Characterization: Refinement
• Task decomposition per agent
(additional independent flows)
• Communication synchronization
(chaining task decompositions with the story)
• Pragmatic interpretation of messages
(via Speech act theory), e.g. a promise is a
commitment and generates an obligation

28. Agentic Characterization: Refinement
• Target: multi-layered representation
Main component
Signal layer Message / Act
Action layer Action / Activity
Intentional layer Intention
Motivational layer Motive

29. Agentic Characterization: Refinement
• Target: multi-layered representation
Main component
Signal layer Message / Act
Action layer Action / Activity
Intentional layer Intention
Motivational layer Motive
• Motives are reasons for action
• Obligations usually are prototypical motives.

30. Agentic Characterization: Refinement
• Target: multi-layered representation
Main component Catalyser
Signal layer Message / Act
Action layer Action / Activity Disposition
Intentional layer Intention Affordance
Motivational layer Motive Motivation
• Affordance: perceived contextual power
• Disposition: actual contextual power
 also for the institutional domain

31. Agentic Characterization on Petri Net
• Multi-layered
• Events / Conditions places
• Synchronization on message layer

32. Outline of the methodology
1. start from an inter-agent description
2. enrich it with intentional/institutional factors
3. synthetize it in intra-agent models

33. Intra-agent synthesis
in AgentSpeak(L)/Jason (example)
+!pay_to(Amount, Agent)
: owning(Money) & Money >= Amount
<- .send(w, achieve, pay_to(Amount, Agent));
+paid_to(Amount, Agent).
• Such scripts give only internal and epistemic perspective.
• Synchronization and ontological factors
to be implemented in environment w

34. Conclusions & further developments
Multiple interpretations of the same story are
possible. As far as they produce the correct
messages they are valid models.
Each representation (MSC, Topology, Petri Nets or
AgentSpeak(L)/Jason scripts) have its own
pro/cons. An adequate integrated environment
should allow to pass from one to the other.
Necessity of defining operators of “distance” and
“subsumption” to compare/integrate stories.

35. Conclusions & further developments
Scenarios acquired through this methodology can
be collected, furnishing a deep model of a social
setting  model-based diagnosis
Alternatively, they can be executed on a simulation
engine, in order to test new policies/regulations
 environmental models for a design tool

36. Conclusions & further developments
Multi-Agent Systems research and practice usually target
“artificial” social systems.
The closure of the system comes by design or as strict
assumption
 basis for all analytical tools
guidance != control
as institutions influence agents, agents influence institutions
 a constructivist approach toward MAS

37. Conclusions & further developments
Multi-Agent Systems research and practice usually target
“artificial” social systems.
The closure of the system comes by design or as strict
assumption
 basis for all analytical tools
guidance != control
as institutions influence agents, agents influence institutions
 a constructivist approach toward MAS

39. Intra-agent synthesis
in AgentSpeak(L)/Jason (example)
+!accept(offer(Good, Amount)[source(Seller)])
<- .send(Seller, tell, accept(offer(Good, Amount)));
+obl(pay_to(Amount, Seller)).
+obl(pay_to(Amount, Agent))
<- !pay_to(Amount, Agent);
-obl(pay_to(Amount, Agent)).
+!pay_to(Amount, Agent)
: owning(Money) & Money >= Amount
<- .send(w, achieve, pay_to(Amount, Agent));
+paid_to(Amount, Agent).
• Such scripts give only internal and epistemic perspective.
• Synchronization and ontological factors
to be implemented in environment w