T0. Multiagent Systems and Electronic Institutions

IIIA-CSIC

Multiagent Systems
EASSS 2012. Carles Sierra.

and
Electronic Institutions

Carles Sierra
IIIA - CSIC
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Contents

1) Introduction
2) Communication
3) Architecture
4) Organisation (Electronic Institutions)

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1) Introduction
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Vision
• Five ongoing trends in computing:
– ubiquity;

– interconnection;
– intelligence;
– delegation; and
– human-orientation.

• Programming has progressed through:
– sub-routines;
– procedures & functions;
– abstract data types;
– objects;

to AGENTS

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Spacecraft control
When a space probe makes its long flight from Earth to the outer
planets, a ground crew is usually required to continually track its

progress, and decide how to deal with unexpected eventualities.
This is costly and, if decisions are required quickly, it is simply
impracticable. For these reasons, organisations like NASA are
seriously investigating the possibility of making probes more
autonomous -giving them richer decision making capabilities and
responsibilities.

This is not science fiction: NASA’s DS1 was doing this!
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Internet agents

Searching the internet for the answer to a specific query can be a long and
tedious process. So, why not allow a computer program -an agent- do
searches for us? the agent would be typically be given a query that
would require synthesizing pieces of information from various different
internet information sources. Failure would occur when a particular
resource was unavailable, (perhaps due to network failure), or where
results could not be obtained.

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What is an agent?
A computer system capable of autonomos action in some environment.

AGENT
INPUT OUTPUT

ENVIRONMENT

Trivial agents: thermostat, Unix daemon (e.g. biff)
An agent must be capable of flexible autonomous action:
– Reactive: continuous interaction with the environment reacting to changes
occurring in it.
– pro-active: to take the initiative by generating and pursuing goals
– Social: to interact with other agents via some agent communication
language
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Reactivity
• If the environment of a program is guaranteed to be fixed the program

doesn’t need to worry about its success or failure -the program
executes blindly (v.g. a compiler).

• The real world is not like this: things change, information is incomplete.
Most interesting environments are dynamic.

• Software is hard to build for dynamic domains: programs must take into
account the possibility of failure -ask itself whether it is worth executing!

• A reactive system is one that maintains an ongoing interaction with its
environment, and responds to changes that occur in it (in time for the
response to be useful).

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Proactiveness

• Reacting to an environment is easy (v.g. stimulus -> response rules).

• We generally want agents to do things for us -> goal-directed
behaviour.

• Pro-activeness = generating and attempting to achieve goals; not
driven solely by events; taking the initiative.

• Recognising opportunities.
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Social ability

• The real world is a multi-agent environment: we cannot go around
attempting to achieve goals without taking others into account.

• Some goals can only be achieved with the cooperation of others.

• Similarly for many computer environments: witness the internet.

• Social ability in agents is the ability to interact with other agents (and
possibly humans) via some kind of agent-communication language,
and perhaps cooperate with others.

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Other properties

• Sometimes other properties are required:

– Mobility: the ability of an agent to move around an electronic network;
– Veracity: an agent will not knowingly communicate false information;
– Benevolence: agents do not have conflicting goals, and that every
agent will therefore always try to do what is asked for.
– Rationality: agent will act in order to achieve its goals, and will not act
in such a way as to prevent its goals being achieved -at least insofar
as its beliefs permit.
– Learning/adaptation: agents improve performance over time.
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Agents as intentional systems
• When explaining human activity it is often useful to describe it as for instance:
Carme took her umbrella because she believed it was going to rain

Carles worked hard because he wanted to finish the paper on time
• Human behaviour is predicted and explained through the attribution of attitudes,
such as believing, wanting, hoping, fearing, …These attitudes are called
intentional notions.
• Daniel Dennett coined the term intentional system to describe entities ‘whose
behaviour can be predicted by the method of attributing belief, desires anb
rational acumen’. Dennet identifies different grades of intentional system:
‘A first-order intentional system has beliefs and desires (etc.) but no beliefs and
desires about beliefs and desires … A second-order intentional system is more
sophisticated; it has beliefs and desires (and no doubt other intentional states)
about beliefs and desires (and other intentional states) - both those of others and
its own’.
• Is it legitimate or useful to attribute beliefs, desires and so on, to a computer
system?

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Intentional stance
• Almost every object can be described by the intentional stance:
‘It is perfectly coherent to treat a light switch as a (very cooperative) agent with the

capability of transmitting current at will, who invariably transmits current when it
believes that we want it transmitted and not otherwise; flicking the switch is simply
our way of communicating our desires’ (Yoav Shoham)
• Most adults would consider this description as absurd. Why?
• The answer seems to be that while the description is consistent
… it does not buy us anything, since we essentially understand the mechanism
sufficiently to have a simpler mechanism description of its behaviour. (Yoav
Shoham)
• Put crudely, the more we know about a system, the less we need to rely on
animistic, intentional explanations of its behaviour.
• But with very complex systems, a mechanistic, explanation of its behaviour may
not be practicable.
As computer systems become ever more complex, we need more powerful
abstractions and metaphors to explain their operation -low level explanations
become impractical. The intentional stance is such abstraction.
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Intentional systems
• Intentional notions are (further) abstraction tools as have been in the past:
procedural abstraction, abstract data types, object.

So why not use the intentional stance as an abstraction tool in computing -to
explain, understand, and, crucially, program computer systems.
• Helps in characterising agents
– Provides us with a familiar non-technical way of understanding and
explaining agents.
• Helps in nested representations
– Provides us with a means to include representations of other systems.
Which is considered to be essential for agents to cooperate.
• Leads to a new paradigm: post-declarative systems
– Procedural programming: How to do something.
– Declarative programming: What to do. But how as well.
– Agent programming: What to do. No buts. Theory of agency determines the
control.

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IIIA-CSIC Exemple 1: MASFIT

MASFIT
Real Auction house

Auction Real world
Electronic Software
Buyer Agents
Institution

Virtual FM+
world
Real Auction house Human buyers

Auction Real world
Software

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Exemple 2: Entertainment

• 3D animation system for generating crowd related visual effects for film and
television.
• Used in the epic battle sequences for The Lord Of The Rings film trilogy
AGENTS OF DESTRUCTION:
Thousands of digitally created fighters clash
with humanity in the Battle of Helm’s Deep.
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Massive. Build the agent

Massive agents begin life rendered as
three-dimensional characters, not the stick
figures of older software. Each body part
has a size and a weight that obeys the laws
of physics.

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Massive. Inset Motion

Movement data gleaned from human
actors performing in a motion-capture
studio is loaded into the Massive program
and associated with a skeleton.
The programmer can fine-tune the agents'
movements using the controls on the
bottom of the screen.
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Massive. Create the brain

The core of every agent is its brain, a network
of thousands of interconnected logic nodes.
One group of nodes might represent balance,
another the ability to tell friend from foe. The
agent's brain relies on fuzzy logic.

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Massive. Replicate

When a variety of agents have been developed,
copies are created from each blueprint, then the
copies are tweaked to give each fighter a unique mix
of various characteristics—height, aggressiveness,
even dirtiness. The 50,000 characters in the scene
will act as individuals. They are placed into a
battlefield grid for testing.
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Massive attack

The simulations begin. Since agents are
programmed with tendencies, not specific tasks
to accomplish, there is no guarantee they will
behave as the director needs them to. Only
through a trial-and-error process, in which the
characters' programs are constantly adjusted, is
the battle scene finalized.

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NEGO+EI Tutorial. Course. Carles Sierra.

2) Communication
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Speech acts

• Most treatments of communication in MAS take inspiration on speech
act theory

• Speech act theories are pragmatic theories of language, i.e. Theories
of language use: they attempt to account for how language is used by
people every day to achieve their goals and intentions

• Origin: Austin’s 1962 book, how to do things with words.

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Speech acts
• Austin noticed that some utterances are rather like ‘physical actions’
that appear to change the state of the world.

• Paradigm examples are:
– Declaring war
– Christening
– ‘I now pronounce you man and wife’

• But more generally, everything we utter is uttered with the intention of
satisfying some goal or intention.

• A speech act theory is a theory of how utterances are used to achieve
intentions.
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Searle

• Identified different types of speech act:
– Representatives: such as informing, e.g., ‘it is raining’

– Directives: attempts to get the hearer to do something, e.g.,
‘please make the tea’

– Commisives: which commit the speaker to doing something, e.g., ‘I
promise to …’

– Expressives: a speaker expresses a mental state, e.g., ‘thank you!’

– Declarations: such as declaring war or christening.

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Components
• In general, a speech act can be seen as having two

components:
– A performative verb: (e.g. request, inform, …)
– Propositional contents: (e.g. ‘the door is closed’)

• Examples
– Performative = request
Content = ‘the door is closed’
Act = ‘please close the door’
– Performative = inform
Act =‘the door is closed!’
– Performative = inquire
Act = ‘is the door closed?’
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Plan-based semantics

• Cohen and Perrault (1979) used the precondition-delete-
add formalism from planning to give semantics

• Request(s,h,f)
pre
– (s believe (h can do f)) AND
(s believe (h believe (h can do f)))
– (s believe (s want f))
post
(h believe (s believe (s wants f)))

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KQML+KIF
• KQML = Outer language defining performatives.
– Ask-if (‘is it true that …’)
– Perform (‘please perform the following action …’)
– Tell (‘it is true that …’)
– Reply (‘the answer is …)

• KIF = inner language for content.

• Examples:
(ask-if (> (size chip1) (size chip2)))
(reply true)
(inform (= (size chip1) 20))
(inform (= (size chip2) 18))
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FIPA ACL

• 20 performatives

• Similar structure to KQML
(inform
:sender agent1
:receiver agent5
:content (price good200 150)
:language s1
:ontology trade)

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Inform and request
• ‘inform’ and ‘request’ are the two basic performatives, the rest are macros

• Meaning:
– Precondition: what must be true for the speech act to succeed
– Rational effect: what the sender hopes to bring about

• Inform: content is a statement, precondition is that sender holds that the
content is true, intends the recipient to believe it and does not believe that
the recipient is aware of whether the content is true or not.

• Request: content is an action, precondition is that sender intends the action
to be performed, believes recipient is capable of doing it and does not
believe that receiver already intends to perform it.
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Social commitments semantics

• Criticism to the mentalist approach.

• Semantics of illocutions are given by the social commitments/
expectations they create.

• Illocutions make the state of commitments evolve.

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3) Architecture
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Agent Architectures
• Three types:

– 1956-1985 symbolic agents
– 1985-present reactive agents
– 1990-present hybrid agents

• Agent architectures are:
A particular methodology for building agents. It specifies how … the agent
can be decomposed into the construction of a set of component
modules and how these modules should be made to interact. The total
set of modules and their interactions has to provide an answer to the
question of how the sensor data and the current internal state of the
agent determine the actions … and future internal state of the agent. An
architecture encompasses techniques and algorithms that supports this
methodology (Pattie Maes)
A particular collection of software (or hardware) modules, typically
designated by boxes with arrows indicating the data and control flow
among the modules. A more abstract view of an architecture is as a
general methodology to designing particular modular decompositions
for particular tasks. (Kaelbling).

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Symbolic agents
• They contain an explicitly represented symbolic model of the world
• They make decisions (for example about what actions to perform) via
symbolic reasoning.
They face two key problems:
– The transduction problem. Translate the real world into an
accurate representation in time for it to be useful
– The representation/reasoning problem. How to represent
information about real world entities and processes, and how to get
agents to reason with this information in time for the results to be
useful.
• These problems are far from being solved
• Underlying problem: symbol manipulation algorithms are usually highly
intractable.
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Deductive reasoning agents
• Idea: Use logic to encode a theory stating the best action to perform in

any situation:
! is the theory (usually a set of rules)
" a logical database that describes the current state of the world
Ac the set of actions the agent can perform
" |#! $ actions means that $ can be proved " using !.

%try to find an action explicitly prescribed
for each a in Ac do
if " |#! Do(a) then return a endif
endfor
%try to find an action not excluded
for each a in Ac do
if " |#/! ∼Do(a) then return a endif
endfor

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Example
• The vacuum world. Goal is the robot top clear up all dirt.
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Agent architecture
• 3 predicates

In(x,y) Agent is at (x,y)
Dirt(x,y) there is dirt at (x,y)
Facing(d) the agent is facing direction d
• 3 actions
Ac = {turn, forward, suck} %turn means turn right
• Rules ! for determining what to do:
In(0,0) and Facing(north) and ~Dirt(0,0) then Do(forward)
In(0,1) and Facing(north) and ~Dirt(0,1) then Do(forward)
In(0,2) and Facing(north) and ~Dirt(0,2) then Do(turn)
In(0,2) and Facing(east) then Do(forward)
And so on!
• With these rules and other obvious ones the robot will clean it up.

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Problems
• How to convert a video stream into Dirt(0,1)?

• Decision making assumes a static environment

• Decision making using first order logic is undecidable

• Even when using propositional logic decision making in the worst case
means NP-completeness.

• Typical solutions
– Weaken the logic
– Use symbolic, non-logical representations
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Agent0

• Shoham’s notion of agent oriented programming (AOP)

• AOP is a ‘new programming paradigm, based on a societal view of
computation’.

• The key idea of AOP is that of directly programming agents in terms of
intentional notions like belief, commitment and intention

• Motivation: in the same way that we use the intentional stance to
describe humans, it might be useful to use the intentional stance to
program machines.

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AOP
• 3 components for an AOP
– A logic for specifying agents and describing their mental
states
– An interpreted programming language for programming
agents
– An ‘agentification’ process for converting ‘neutral
applications’ (e.g. Databases) into agents

• Results were reported just on the first two components.

• Relation between the first two = semantics.
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Agent0

• Extension to LISP

• Each agent has
– A set of capabilities
– A set of initial beliefs
– A set of initial commitments, and
– A set of commitment rules

• The key element that determines how the agent acts is the
commitment rule set.

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Commitment rules

• Each commitment rule has
– A message condition
– A mental condition, and
– An action
• On each ‘agent cycle’
– The message condition is matched against the received messages
– The mental condition is matched against the beliefs
– If the rule fires the agent becomes committed to the action (it is
added to the agent’s commitment set)
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Commitment rules

• Actions may be
– Private: an internally executed computation, or
– Communicative: sending messages

• Messages are constrained to be one of
– ‘requests’ to commit to action
– ‘unrequests’ to refrain from actions
– ‘informs’ which pass on information

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Agent0 Control

Initialize Messages in

Update beliefs Beliefs

Update commitments Commitments

Abilities
Execute
Messages out
Internal actions
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A commitment rule

Commit(
( agent, REQUEST, DO(time, action)
), ;;; msg condition
( B [now, friend agent] AND
CAN(self, action) AND
NOT [time, CMT(self, anyaction)]
), ;;;mental condition
self,
DO(time, action)
)

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Reactive architectures

• Symbolic AI has many unsolved problems basically on complexity

• Many researchers have argued about the viability of the whole
approach (Chapman)

• Although they share a common belief that mainstream AI is in some
sense wrong they have very different approaches
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Brooks-behaviour languages

• Three theses:
– Intelligent behaviour can be generated without explicit
representations of the kind that symbolic AI proposes
– Intelligent behaviour can be generated without explicit abstract
reasoning of the kind that symbolic AI proposes.
– Intelligence is an emergent property of certain complex systems.

• Two ideas inspiring him:
– Situatedness and embodiment: ‘real’ intelligence is situated in the
world, not in disembodied systems like theorem provers of KBS.
– Intelligence and emergence: ‘intelligent’ behaviour arises as a
result of the interaction with its environment.

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Subsumption architecture
• Hierarchy of task-accomplishing behaviours

• Each behaviour is a simple rule-like structure

• Behaviours compete with others to control the agent

• Lower layers represent more primitive kinds of behaviour (such as
avoiding obstacles) and have preference over higher levels

• The resulting system in extremely simple in terms of the amount of
computation to perform

• Some of the robots do impressive tasks from the perspective of
symbolic AI
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Mars explorer system

• Steel’s system uses the subsumption architecture and obtains nearly
optimal behaviour
– The objective is to explore a distant planet, and in particular, to
collect sample of a precious rock. The location of the samples is not
known in advance, but it is known that they tend to cluster
• If detect an obstacle then change direction
• If carrying samples and at the base then drop samples
• If carrying samples and not at the base then travel up gradient
• If detect a sample then pick sample up
• If true then move randomly

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Hybrid architectures

• The compromised position, two subsystems:
– A deliberative one, containing a symbolic world model
– A reactive one which is capable of reacting without complex
reasoning

• Often the reactive has priority

• Architectures tend to be layered with higher layers dealing
with information at increasing levels of abstraction
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Layers

Action
output

Layern Layern Layern
... ... ...
Perceptual
input Layeri Action Layeri
output
Layeri
... ... ...
Layer1 Layer1 Layer1

Perceptual Perceptual Action
input input output

Vertical layering Horizontal layering
Horizontal layering
One pass control Two pass control

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4) Organisation
(Electronic Institutions)
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EI: Motivation

There are situations where individuals interact in ways that involve
Commitment, delegation, repetition, liability and risk.

These situations involve participants that are:
Autonomous, heterogeneous, independent, not-benevolent,
not-reliable, liable.

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EI: Motivation
These situations are not uncommon: Markets, medical services, armies and

many more.

It is usual to resort to trusted third parties whose aim is to make those
interactions effective by establishing and enforcing conventions that
standardize interactions, allocate risks, establish safeguards and guarantee
that certain intended actions actually take place and unwanted situations
are prevented.

These functions have been the basis for the development of many
traditional institutions.

They are even more necessary when interaction may involve software
agents.

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Introduction

! Open multi-agent systems characteristics:

• populated by heterogeneous agents, developed by different people,
using different languages and architectures

• self-interested agents

• participants change over time and are unknown in advance

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Introduction

! With the expansion of Internet open multi agent systems represent the
most important area of application of multi agent systems.

! Research issue: need for appropriate methodologies and software
tools which give support to the analysis, design, and development of
open systems.

! Goal: design and development of open multi agent systems.

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Introduction

! Human institutions have been working successfully for a long time.

! Institutions are created to achieve some goals following determined
procedures.

! The institution is the responsible of define the rules of the game, to
enforce them and impose the penalties in case of violation.

! Examples: auction houses, parliaments, stock exchange markets,.…

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Approach
AGENT1

ENVIRONMENT
AGENT2

AGENT3

NORMS

AGENT1

ELECTRONIC AGENT2
INSTITUTION

AGENT3

Institutions in the sense proposed by North “… set of artificial
constraints that articulate agent interactions”.

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Approach

! Electronic institutions development can be divided into two basic
steps:
• Formal specification of institutional rules.
• Execution via an infrastructure that mediates agents’ interactions
while enforcing the institutional rules.

! The formal specification focuses on macro-level (rules) aspects of
agents, not in their micro-level (players) aspects.

! The infrastructure is required to be of general purpose (can interpret
any formal specification).

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Electronic Institution Specification with
ISLANDER

" Network of protocols
" Multi-agent protocols
" Norms
" Agent Roles
"Common Ontology and
language

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Electronic Institution Components
PERFORMATIVE STRUCTURE SCENE
(NETWORK OF PROTOCOLS) (MULTI-AGENT PROTOCOL)

AGENT ROLES

NORMS
Buyers’ Payment

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NEGO+EI Tutorial. Course. Carles Sierra. The (“Hello World”) Chat Example

! A simple institution where agents interact simulating a chat.

! Each agent owns a main topic and a list of interested subtopics.

! Agents create a chat room per main topic.

! They can join the scenes created by other agents

! The institution keeps track of active chat rooms to provide information
to agents.

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Dialogic Framework Components

! Common ontology

! Valid communication language expressions
• List of illocutionary particles
• Content language

! Roles that agents can play
• Internal Roles
• External Roles

! Role relationships

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NEGO+EI Tutorial. Course. Carles Sierra. Roles

! Each role defines a pattern of behaviour within the institution (actions
associated to roles).
! Agents can play multiple roles at the same time
! Agents can change their roles.
! Two types of roles:
• Internal: played by the staff agents to which the institution
delegates its services and tasks.
• External: played by external agents.
! Role relationships:
• Static incompatibility (ssd)
• Dynamic incompatibility (dsd)
• Hierarchy (sub)

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Chat Roles

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NEGO+EI Tutorial. Course. Carles Sierra. IIIA-CSIC NEGO+EI Tutorial. Course. Carles Sierra. IIIA-CSIC
Chat Ontology

Chat Dialogic Framework

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Communication Language
! CL expressions are formulae of the form (i (!i ri) " #

$) where:
• i is an illocutionary particle (e.g. request, inform);
• !i can be either an agent variable or an agent identifier;
• ri can be either a role variable or a role identifier;
• " represents the addressee(s) of the message and can be:
- (!k rk) the message is addressed to a single agent.
- rk the message is addressed to all the agents playing role rk.
- “all” the message is addressed to all the agents in the scene.
• # is an expression in the content language.
• $ can be either a time variable or a time-stamp

! (request (?x guest) (!y staff) (login ?user ?email))

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Roles
Communication Language
(request (?x guest) (!y staff) (login ?user ?email))

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Scenes

! Specification level
• A scene is a pattern of multi-agent interaction.
• Scene protocol specified by a finite state oriented graph where the
nodes represent the different states and oriented arcs are labelled
with illocution schemes or timeouts.

! Execution level
• Agents may join or leave scenes.
• Each scene keeps the context of its multi-agent interaction.
• A scene can be multiply executed and played by different groups of
agents.

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Guest admission scene

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Guest admission scene. State
information

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Guest admission scene. Illocutions

1. (request (?x guest) (?y staff) login(?user ?email)) )

2. (inform (!y staff) (!x guest) accept()) )

3. (failure (!y staff) (!x guest) deny(?code)) )

4. (request (?x guest) (!y staff) login(?user ?email)) )

5. (inform (!y staff) (all guest) close()) )

6. (inform (?y staff) (all guest) close()) )
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Scenes

! Illocution schemes: at least the terms referring to agents and time are
variables.

! Semantics of variable occurrences:
• ?x: variable x can be bound to a new value.
• !x: variable x must be substituted by its last bound value.
Example:
(request (?x guest) (!y staff) login(?user ?email)) )

! Context of a conversation captured on a list of variables’ bindings.

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1. (request (?x guest) (?y staff) login(?user ?email)))
6. (inform (?y staff) (all guest) close()) )

! Two agents in the scene: John as guest and Mike as staff
! Agent John utters an illocution:
(request (John guest) (Mike Staff) login(John john@hotmail.com) )

! The illocution matches arc 1 and the scene evolves to W1.
! Substitutions:
[?x/John, ?y/Mike, ?user/John, ?email/john@hotmail.com]

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2. (inform (!y staff) (!x guest) accept()) )
3. (failure (!y staff) (!x guest) deny(?code)) )

! Former bindings:
[?x/John, ?y/Mike, ?user/John, ?email/john@hotmail.com]

! Only illocutions matching the following schemes will be valid:
(inform (Mike staff) (John guest) accept()) )
(failure (Mike staff) (John guest) deny(?code)) )

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Scenes Constraints

! Constraints capture how past actions in a scene affect its future
evolution:
• restricting the valid values for a variable
• restricting the paths that a conversation can follow

! Examples:
• A buyer can only submit a single bid at auction time.
• A buyer must submit a bid greater than the last one.
• An auctioneer can not declare a winner if two buyers have
submitted a bid at the higher value.
• An agent can not repeat an offer during a negotiation process.

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Scene
! Example

• Illocution scheme:
commit((?y buyer) (!x auctioneer) bid(!good_id, ?price))

?price % (0.+&)

0 +&

• Constraint:
(> ?price !starting_price)
?price % (!starting_price.+&)

0 !starting_price +&

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Variable Occurrences

! ?x: binding occurrence
! !x: stands for the last binding of variable x.
! !x (wi wj): stands for the bindings of variable x the last time that the
conversation evolve from wi wj.

! !x (wi wj i): stands for the bindings of variable x the last i times that
the conversation evolve from wi wj.

! !x (wi wj *): stands for the bindings of variable x all the times that the
conversation evolve from wi wj.

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Example: Vickrey auction

1 (inform (?x auctioneer) (all buyer) startauction(?a) )
2 (inform (!x auctioneer) (all buyer) startround(?good ?price ?bidding_time) )
3 (inform (!x auctioneer) (all buyer) offer(!good !price) )
4 (request (?y buyer) (!x auctioneer) bid(!good ?bid_price) )
5 [!bidding_time] )
6 (inform (!x auctioneer) (all buyer) sold(!good ?sold_price ?buyer_id) )
8 (inform (!x auctioneer) (all buyer) close() )
7 (inform (!x auctioneer) (all buyer) withdrawn(!good) )
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Constraints

2 (inform (!x auctioneer) (all buyer) startround(?good
?price ?bidding_time) )
5 [!bidding_time]
6 (inform (!x auctioneer) (all buyer)
sold(!good ?sold_price ?buyer_id) )

Constraint :

?y ! !y (w3 w4)
?bid_price > !price

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Constraints

2 (inform (!x auctioneer) (all buyer) startround(?good
?price ?bidding_time) )
5 [!bidding_time]
6 (inform (!x auctioneer) (all buyer)
sold(!good ?sold_price ?buyer_id) )
Constraint :

| !y (w3 w4)| = 0

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Performative Structure
! Complex activities can be specified by establishing relationships

among scenes that define:

• causal dependency (e.g. a guest agent must go through the
admission scene before going to the chat rooms)
• synchronisation points (e.g. synchronise a buyer and a seller
before starting a negotiation scene)
• parallelisation mechanisms (e.g. a guest agent can go to
multiple chat rooms)
• choice points (e.g. a buyer leaving an admission scene can
choose which auction scene to join).
• the role flow policy.

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Performative Structure
! Performative structures as networks of scenes.

! Transitions to link scenes.

! Arcs connecting scenes and transitions labelled with constraints and
roles.

! Agents moving from a transition to a scene may join one, some or all
current executions of the target scene(s) or start new executions.

! The specification allows to express that simultaneous executions of a
scene may occur.

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Chat Performative Structure

Scene

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Agent

And transition: synchronisation and parallelisation point

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And transition: synchronisation and parallelisation point

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Or transition: choice point

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Or transition: choice point

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XOr transition: exclusive choice point

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XOr transition: exclusive choice point

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Arcs connecting transitions to scenes determine whether agents
join one, some or all current executions of the target scene(s) or
whether new executions are started.

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Norms
! Norms define the consequences of agents actions within the institution.

! Such consequences are captured as obligations.
• Obl(x,',s): meaning that agent x is obliged to do ' in scene s.

! Norms are a special types of rules specified by three elements:
• Antecedent: the actions that provoke the activation of the norm and
boolean expressions over illocution scheme variables.
• Defeasible antecedent: the actions that agents must carry out in
order to fulfil the obligations.
• Consequent: the set of obligations

! Actions expressed as pairs of scene and illocution schema.

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Norms

Electronic Institutions Definition

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Electronic Institutions
Development Environment
http://e-institutions.iiia.csic.es

EIDE TEAM
Josep Ll. Arcos, David de la Cruz,Marc Esteva, Andrés García, Pablo
Noriega, Bruno Rosell, Juan A. Rodríguez-Aguilar, Carles Sierra,
Wamberto Vasconcelos
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Conclusions
IIIA-CSIC

! Engineering open multi-agent systems is a highly complex task.

! MAS decouple the internal AI in the agents and the communication
infrastructure.

! MAS is a technology that generalises other distributed approaches:
GRID, P2P, CLOUD.

! Electronic institutions reduce the programming complexity by introducing
normative (regulatory) environments.

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T0. Multiagent Systems and Electronic Institutions

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