Introduction to agents and multi-agent systems

LECTURE 1:
INTRODUCTION TO
AGENTS AND MAS

Artificial Intelligence II – Multi-Agent Systems –
Introduction to Multi-Agent Systems
URV, Winter-Spring 2010

Outline of the lecture

Main trends in Computer Science
Agents and multi-agent systems
Viewpoints on agent technology
Agent technology roadmap
Challenges on agent technology
Objections to MAS

Important aspects in Computer Science
Five ongoing trends are marking the history
of computing:
ubiquity
interconnection
intelligence
delegation
human-orientation in programming methodologies

Ubiquity
The continuous reduction in cost of
computing capability has made it possible to
introduce processing power into places and
devices that would have once been
uneconomic / unimaginable [e.g. Nike+]
As processing capability spreads,
sophistication (and intelligence of a sort)
becomes ubiquitous
What could benefit from having a processor
embedded in it…?

Ambient Intelligence
Aimed at seamless delivery of services and applications.
Relies on ubiquitous computing, ubiquitous
communication and intelligent user interfaces.
The vision
An environment of potentially thousands of embedded and
mobile devices (or software components) interacting to support
user-centred goals and activity.
Suggests a component-oriented view of the world in which the
components are independent and distributed.
Autonomy, distribution, adaptation, responsiveness, and so on,
are key characteristics of these components, and in this sense
they share the same characteristics as agents.
Requires agents to be able to interact with numerous
other agents in the environment around them in order to
achieve their goals.

Interconnection
Computer systems today no longer stand
alone, but are networked into large
distributed systems
Obvious example: Internet
Since distributed and concurrent systems
have become the norm, some researchers
are putting forward theoretical models that
portray computing as primarily a process of
interaction

Connectivity -Technological context
Semantic Web
The Semantic Web is based on the idea that the data
on the Web can be defined and linked in such a way
that it can be used by machines for the automatic
processing and integration of data across different
applications (Berners-Lee et al., 2001).
Peer-to-Peer computing
Grid computing

Intelligence
The complexity of tasks that we are capable
of automating and delegating to computers
has grown steadily
If you don’t feel comfortable with this
definition of “intelligence”, it’s probably
because you are a human

Delegation
Computers are doing more for us – without
our intervention
We are giving control to computers, even in
safety critical tasks
One example: fly-by-wire aircraft, where the
machine’s judgment may be trusted more
than an experienced pilot
Next on the agenda: fly-by-wire cars,
intelligent braking systems, cruise control that
maintains distance from car in front…

Driving automation
1. Road Condition
Reporting
2. Adaptive Cruise
Control
3. Omnidirectional
Collision System
4. Lane-Departure
Prevention
5. Auto Parallel Park
6. Blind-Spot
Sensors
7. Corner Speed

Human Orientation
The movement away from machine-oriented
views of programming toward concepts and
metaphors that more closely reflect the way
we ourselves understand the world
Programmers (and users!) relate to the
machine differently
Programmers conceptualize and implement
software in terms of higher-level – more
human-oriented – abstractions

Agent-Oriented Software Engineering
Programming has progressed through
machine code
assembly language
machine-independent programming languages
sub-routines
procedures & functions
abstract data types
objects
to agents.

Where does it bring us?
Delegation and Intelligence imply the need to
build computer systems that can act
effectively on our behalf
This implies:
The ability of computer systems to act
independently
The ability of computer systems to act in a way
that represents our best interests while interacting
with other humans or systems

Interconnection and Distribution
Interconnection and Distribution have
become core motifs in Computer Science
But Interconnection and Distribution, coupled
with the need for systems to represent our
best interests, implies systems that can
cooperate and reach agreements (or even
compete) with other systems that have
different interests (much as we do with other
people)

So Computer Science expands…

These issues were not studied in Computer
Science until recently
All of these trends have led to the emergence
of a new field in Computer Science:
multiagent systems

Agents, a Definition
An agent is a computer system that is located in a
dynamic environment and is capable of
independent action on behalf of its user or owner
(figuring out what needs to be done to satisfy
design objectives, rather than constantly being
told)
Intelligent action, probably using Artificial
Intelligence tools and techniques
An agent should satisfy a certain number of
properties [lecture on week 3]

Multiagent Systems, a Definition
A multiagent system is one that
consists of a number of agents,
which interact with one-another
In the most simple case, all
agents are programmed by the
same team and they collaborate
to complete a task
In the most general case,
agents will be acting on behalf
of users with different goals and
motivations
To successfully interact, they
will require the ability to
cooperate, coordinate, and
negotiate with each other, much
as people do

Related areas
Several sub-disciplines of information
technology are related to software agent
technology:
computer networks,
software engineering,
Artificial Intelligence,
human-computer interaction,
distributed and concurrent systems,
mobile systems, computer-supported
cooperative work, control systems, decision
support, information retrieval and management,
electronic commerce...

Micro and macro aspects
Agent technologies can be grouped into three
categories
Agent-level [micro level]
technologies and techniques concerned only with individual
agents — for example, procedures for agent reasoning and
learning.
Interaction-level
technologies and techniques that concern the
communications between agents
communication languages, interaction protocols and
resource allocation mechanisms.
Organization-level [macro level]
technologies and techniques related to agent societies as a
whole.
structure, trust, norms, obligations, etc.

Multiagent Systems
In Multiagent Systems, we address questions
such as:
What kinds of languages can agents use to
communicate?
How can cooperation emerge in societies of self-
interested agents?
How can self-interested agents recognize conflict,
and how can they (nevertheless) reach
agreement?
How can autonomous agents coordinate their
activities so as to cooperatively achieve goals?

Multiagent Systems
While these questions are all addressed
in part by other disciplines (notably
Economics and Social Sciences), what
makes the multiagent systems field
unique is that it emphasizes that the
agents in question are computational,
information processing entities.

Example 1 - Spacecraft Control
When a space probe makes a flight 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 not
practicable. NASA is investigating the possibility of
making probes more autonomous — giving them
richer decision making capabilities and
responsibilities.
This is not fiction: NASA’s DS1 has done it!

Autonomous Agents for specialized tasks

The DS1 example is one of a generic class
Agents (and their physical instantiation in
robots) have a role to play in high-risk
situations, unsuitable or impossible for
humans
The degree of autonomy will differ depending
on the situation (remote human control may
be an alternative, but not always)

Example 2 - Air Traffic Control
“A key air-traffic control system suddenly fails,
leaving flights in the vicinity of the airport with
no air-traffic control support. Fortunately,
autonomous air-traffic control systems in
nearby airports recognize the failure of their
peer, and cooperate to track and deal with all
affected flights.”
Systems taking the initiative when necessary
Agents cooperating to solve problems beyond
the capabilities of any individual agent

Example 3 - 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 typically be given a query
that would require synthesizing pieces of
information from various different Internet
information sources.

Example 4 – e-personal assistants
Internet agents need not simply search
They can plan, arrange, buy, negotiate –
carry out arrangements of all sorts that would
normally be done by their human user
As more can be done electronically, software
agents theoretically have more access to
systems that affect the real-world
For example, preparing a holiday trip

Research Issues
How do you state your preferences to your
agent?
How can your agent compare different deals
from different vendors? What if there are many
different parameters?
What algorithms can your agent use to
negotiate with other agents?
These issues aren’t frivolous – automated
procurement could be used massively by (for
example) government agencies

Multiagent Systems is Interdisciplinary
The field of Multiagent Systems is influenced and
inspired by many other fields:
Economics
Philosophy
Game Theory
Logic
Ecology
Social Sciences
This can be both a strength (infusing well-founded
methodologies into the field) and a weakness (there
are many different views as to what the field is about)
This has analogies with Artificial Intelligence itself

Some Views of the Field
Agents as a paradigm for software
engineering:
Software engineers have derived a
progressively better understanding of the
characteristics of complexity in software. It
is now widely recognized that interaction is
probably the most important single
characteristic of complex software

Agents as design metaphor

Agent-oriented software engineering
Agents allow software designers and developers
to structure an application using autonomous,
communicative components.
In this sense, software agents offer a new and
often more appropriate route to the development
of complex computational systems, especially in
open and dynamic environments.
Modularity, reusability,
collaborative behaviour

Agents as source of technologies
Software agent technologies span a range of
specific techniques and algorithms
balancing reaction and deliberation in individual
agent architectures [lecture 2]
learning from and about other agents in the
environment
eliciting and acting upon user preferences
finding ways to negotiate and cooperate with
other agents
developing appropriate means of forming and
managing coalitions (and other organizations)

Social simulation
Agents as a tool for understanding human
societies:
Multiagent systems provide a novel new
tool for simulating societies, which may
help shed some light on various kinds of
social processes.
This has analogies with the interest in
“theories of the mind” explored by some
Artificial Intelligence researchers

Agent-based simulation
Multi-agent systems provide strong models
for representing complex and dynamic real-
world environments.
modelling of the impact of climate change on
biological populations
modelling the impact of public policy options on
social or economic behaviour
modelling intelligent buildings
modelling traffic systems
modelling biological populations

Distributed computing theory
Multiagent Systems is primarily a search for
appropriate theoretical foundations:
We want to build systems of interacting,
autonomous agents, but we don’t yet know
what these systems should look like
You can take a “neat” or “scruffy” approach to
the problem, seeing it as a problem of theory
or a problem of engineering
This, too, has analogies with Artificial
Intelligence research

Agent technology roadmap [AgentLink]
There are four broad phases
Phase 1: current
Phase 2: short-term future
Phase 3: medium-term future
Phase 4: long-term future

Time phases distinguished along dimensions:
The degree to which the participating agents share
common domain knowledge and common goals.
The degree to which participating agents are
designed by the same or diverse design teams.

Phase 1: present state
Systems are designed by the same teams.
Systems share common domain knowledge.
Participating agents have a global objective.
Agent communication languages are based on
standards.
Interaction protocols remain non-standard.
Low scalability, few agents.
Example systems include those to enable
automated scheduling coordination between
different departments, etc.

Phase 2: short-term future
Multi-agent systems will increasingly be designed to cross
corporate boundaries.
Participating agents have few goals in common, although
their interactions will still concern a common domain
Agents will be designed by a same team, and will share
common domain knowledge.
Standard agent communication languages, such as FIPA
ACL, will be used.
Interaction protocols will be mixed between standard and
non-standard ones.
Systems will be able to handle large numbers of agents in
pre-determined environments.
Development methodologies, languages and tools will have
reached a degree of maturity.

Phase 3: medium-term future
Multi-agent systems will permit participation by
heterogeneous agents, designed by different designers or
teams.
Any agent will be able to participate in these systems.
Their (observable) behaviour conforms to publicly-
stated requirements and standards.
Open systems will typically be specific to particular
application domains, such as B2B eCommerce or
bioinformatics.
The languages and protocols used will be agreed and
standardised, perhaps drawn from public libraries of
alternative protocols.
Commonly agreed modelling languages
Agent-UML

Phase 4: Long-term future
Truly open and fully-scalable agent systems
spanning multiple application domains and
developed by diverse design terms
Agents learn appropriate protocols and behaviour
upon entry to system
Languages, protocols, and behaviours emerge
from actual agent interactions.
Evolving organisational structure with multiple,
dynamic, interacting organisations.
Self-modifying agent communications languages.
Use of agent-specific design methodologies.

Challenges in agent technology

Implementing multi-agent systems is still a complex
task.
Lack of maturity in both methodologies and programming
tools.
Lack of specialized debugging tools.
Lack of skills needed to move from analysis and design to
code.
Problems associated with awareness of the specifics of
different agent platforms.
Problems in understanding the nature of what is a new and
distinct approach to systems development.

Technological Challenges
Increase quality of agent software to industrial
standard
Agent oriented design methodologies, tools and
development environments.
Seamless integration with existing technologies.
Web, information systems
Non-functional properties: scalability, performance,
reliability, and robustness.
Analysis point of view
Agent technology requires dedicated basic
concepts and languages:
Dynamic aspects, such as time and action.
Locality aspects, such as position in a space.

Increase quality of agent software to industrial
standard (cont.)
Design point of view
The key point is reusability
Libraries of
Generic organisation models (hierarchical, flat, etc.).
Generic agent models (reactive, deliberative, etc.)
Generic task models (diagnostic, information filtering, etc.).
Communication languages and patterns for agent societes.
Ontology patterns for agent requirements, models, and organisation.
Interaction protocol patterns between agents with special roles.
Reusable knowledge bases.

Develop reasoning capabilities for agents
Virtual organisations, e-institutions
Heterogeneity of agents, trust, and failure
handling and recovery.
Norms, legislation, authorities, and enforcement.
Coalition formation
Dynamic coalitions of small groups of agents.
Negotiation and argumentation strategies
Distributed planning techniques

Develop agent ability to understand user
requirements
User profiling
Personalisation
Knowledge acquisition tools
Develop agent ability to adapt to changes
in environment
Learning
Evolutionary programming techniques


Ensure user confidence and trust in agents
Security technologies.
Security in open agent systems.
Agents must be secure and must not reveal information
inappropriately.
Confidence that agents representing other parties will
behave within certain constrains.

Ensure user confidence and trust in agents
(cont).
Models and infrastructure for trust and reputation.
Reputation mechanisms to assess the past behaviour of
particular agents or users, to allow avoidance of
untrustworthy agents.
The adoption of norms (social rules) by all members of
an open agent system.
Enforcement of sanctions against agents not following
rules (or self-enforcing protocols).
The use of electronic contracts to represent and enforce
agreements between several parties.

Provide semantic infrastructure for open agent
communities
Ontologies
Semantic Web
Matchmaking and broker architectures
Exiting out of laboratories requires
A greater understanding of how agents, databases and
information systems interact.
Investigation of the real-world implications of information
agents.
Development of benchmarks for agent system performance
and efficiency.
New web standards that enable structural and semantic
description of information.
Creation of common ontologies, thesauri, or knowledge
bases.

Preconditions for wide-scale usage

Software agent technology is better than other ones.
At least, when compared using one measurable
parameter in the application field ....
There are enough trained, skillful persons to
develop applications with software agent
technology.
Development tools are easy to use and powerful
enough to develop applications.
Technology is generally known.
Including top-level management.

Objections to MAS (I)
Isn’t it all just Distributed/Concurrent Systems?
There is much to learn from this community,
but:
Agents are assumed to be autonomous, capable of
making independent decision – so they need
mechanisms to synchronize and coordinate their
activities at run time
Agents are (can be) self-interested, so their
interactions are “economic” encounters
(negotiation)

Objections to MAS (II)
Isn’t it all just AI?
We don’t need to solve all the problems of
Artificial Intelligence (i.e., all the components of
intelligence) in order to build really useful agents
Classical AI ignored social aspects of agency.
These are important parts of intelligent activity in
real-world settings

Objections to MAS (III)
Isn’t it all just Economics/Game Theory?
These fields also have a lot to teach us in
multiagent systems, but:
Insofar as game theory provides descriptive
concepts, it doesn’t always tell us how to compute
solutions; we’re concerned with computational,
resource-bounded agents
Some assumptions in economics/game theory
(such as a rational agent) may not be valid or
useful in building artificial agents

Objections to MAS (IV)
Isn’t it all just Social Science?
We can draw insights from the study of
human societies, but there is no particular
reason to believe that artificial societies will be
constructed in the same way
Again, we have inspiration and cross-
fertilization, but hardly subsumption

Readings for this week

M.Wooldridge: An
introduction to MultiAgent
Systems – chapter 1
A.Mas: Agentes software y
sistemas multi-agente:
conceptos, arquitecturas y
aplicaciones – initial part of
chapter 1
Agent Technology:
Computing as Interaction
AgentLink III roadmap

Mindmap, chapter 1 (Wooldridge)

Introduction to agents and multi-agent systems

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