MULTI AGENT SYSTEMS

MULTI-AGENT SYSTEMS
Reference

[1] Michael Wooldridge, “An Introduction to MultiAgent Systems”,
Second Edition, 2009

[2] R.H. Bordini, J.F.Hubner, M. Wooldridge, “Programming multi-
agent systems in AgentSpeak using Jason”, 2007.
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Outline

Background

Agent

Environment

Architecture for Agents
Reading: Chapter 1&2, [1]
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Background

Distributed Artificial Intelligence (DAI)

Subfield of AI

Development of distributed solutions for complex problems

problem that is beyond the capability of an individual problem
solver


Two mainstreams

Distributed prolem solving (DPS)

MultiAgent systems (MAS)
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Background

Distributed Artificial Intelligence (DAI)

Two mainstreams

Distributed prolem solving (DPS)

Centralized Control, Distributed Data

MultiAgent systems (MAS)

Distributed Control, Distributed Data

a system comprising several agents that “live” and interact in the same
environment.
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Outline

Background

Agent

Environment


Architecture for Agents
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Example

Cleaning robot

Gold miners
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What is an Agent?

There is no universally accepted definition of the term “Agent”

There is a general consensus that autonomy is central to the
notion of agency.

Difficulty is that various attributes associated with agency are
of diffening importance for different domains.
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What is an Agent?

Autonomy:

capable of acting independently,

exhibiting control over their internal state

Thus: an agent is a computer system capable of autonomous action
in some environment in order to meet its design objectives
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SYSTEM
ENVIRONMENT
input
output
What is an Agent?

An agent in its environment
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Environment
?
Sensors
Feedback
Actions
What is an Agent?

In most domain of reasonable complexity, an agent will not have
complete control over its environment.

 It will have at best partial control, in that it can influence it.
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What is an Agent?

Trivial (non-interesting) agents:

Thermostat

Have a sensor for detecting room temperature

Two signals: too low, and OK


Available actions: heating on , and heating off

Rules:

Too cold  heating on

Temperature Ok  heating off

When the door of the room is close?  guaranteed effects

When the door of the room is open?
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What is an Agent?

An intelligent agent is a computer system capable of flexible
autonomous action in some environment

By flexible, we mean:

reactive

pro-active

social
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Reactivity

If a program’s environment is guaranteed to be fixed, the program
need never worry about its own success or failure – program just
executes blindly


Example of fixed environment: compiler

The real world is not like that: things change, information is
incomplete. Many (most?) interesting environments are dynamic
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Reactivity

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

we generally want agents to do things for us

goal directed behavior

Pro-activeness = generating and attempting to achieve goals;
not driven solely by events; taking the initiative

Recognizing 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


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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Outline

Background

Agent

Environment

Abstract Architecture for Agents
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Environments
Accessible vs. inaccessible

An accessible environment is one in which the agent can obtain complete,
accurate, up-to-date information about the environment’s state

Most moderately complex environments (including, for example, the
everyday physical world and the Internet) are inaccessible
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Environments
Accessible vs. inaccessible

The more accessible an environment is, the simpler it is to build
agents to operate in it

Example:


a vacuum agent with only a local dirt sensor cannot tell whether there is
dirt in other squares,

an automated taxi cannot see what other drivers are thinking
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Environments
Deterministic vs. non-deterministic

A deterministic environment is one in which any action has a single
guaranteed effect — there is no uncertainty about the state that will
result from performing an action
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Environments
Deterministic vs. non-deterministic

Example:

The vacuum world as we described it is deterministic, but variations
can include stochastic elements such as randomly appearing dirt
and an unreliable suction mechanism

Taxi driving is clearly non-deterministic in this sense, because one
can never predict the behavior of traffic exactly; moreover, one's
tires blow out and one's engine seizes up without warning
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Environments
Static vs. dynamic

A static environment is one that can be assumed to remain

unchanged except by the performance of actions by the agent

A dynamic environment is one that has other processes
operating on it, and which hence changes in ways beyond the
agent’s control
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Environments
Static vs. dynamic

Other processes can interfere with the agent’s actions (as in
concurrent systems theory)

The physical world is a highly dynamic environment

Example:

Taxi driving is clearly dynamic

Crossword puzzles are static
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Environments
Discrete vs. continuous

An environment is discrete if there are a fixed, finite number
of actions and percepts in it

Russell and Norvig give a chess game as an example of a
discrete environment, and taxi driving as an example of a
continuous one
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