from inter-agent to intra-agent representations

From Inter-Agent to Intra-AgentRepresentations

8 March 2014 - ICAART presentation

Giovanni Sileno [email protected] BoerTom van Engers

Leibniz Center for Law – University of Amsterdam

mapping social scenarios to agent-role descriptions

Fundamental division: “artificial” social systems norm-driven“natural” social systems norm-guided

In the latter, non-compliance (intentional or not) is systemic.

Example: Humans!

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!

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?

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

What “stories” are

Not only fictional narrations..

but also:

• personal experiences

• journalistic reports

• medical cases

• legal cases

• … and any other expert domain case!

What “stories” are

Not only fictional narrations..

but also:

• personal experiences

• journalistic reports

• medical cases

• legal cases social behaviours with

their legal interpretation

• …

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

• ...

A relevant issue


What is told in a story depends on



• expertise

• interest

• focus

• ...

of the narrator !

A relevant issue


What is read in a story depends on



• expertise

• interest

• focus

• ...

of the listener !

Our objective

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

Our objective & requirements


• bypass natural language issueswe are not targeting a story understanding

application, but a scenario acquisition tool

Our objective & requirements


• bypass natural language issueswe 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-basedmodelling

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.

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

Inter-Agent Description: MSC

Message Sequence Charts (MSC) formalize UML sequencediagrams. They are intuitive and commonly used.

Inter-Agent Description: MSC

Issues with:

• “ontological” identities

• Neglecting side-effects/control-loop

• implicit ordering

Inter-Agent Description: Topology

Identity is epistemic (assigned to message boxes)

Inter-Agent Description: Flow

Problems:

• Consecutiveness vs Consequence“the mainspring of the narrative activity is to be traced to thatvery 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 timelinesordering as events occur or how they are told/observed


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)

First steps toward an agenticcharacterization

• 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?

Agentic Characterization on MSC

Agentic Characterization on MSC

Issues:

ontological–epistemic

emission–reception

Agentic Characterization: Refinement

• Task decomposition per agent(additional independent flows)


• 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


• Target: multi-layered representation

Main component

Signal layer Message / Act

Action layer Action / Activity

Intentional layer Intention

Motivational layer Motive



Main component


Action layer Action / Activity

Intentional layer Intention

Motivational layer Motive

• Motives are reasons for action

• Obligations usually are prototypical motives.



Main component Catalyser


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

Agentic Characterization on Petri Net

• Multi-layered

• Events / Conditions places

• Synchronization on message layer

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

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.


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


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 != controlas institutions influence agents, agents influence institutions a constructivist approach toward MAS

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

from inter-agent to intra-agent representations

Education