L1. IntroductionL1. Introduction

MotivationsMotivations

Human world

physical world

humans

knowledge

reasoning

action/behavior

communications

collaborations

negotiations

Agent world

computers 　 (virtual space) + the Internet

agents knowledge

acquisition ?

representation ?

knowledge base ?

uncertainty ?

reasoning ?

action/behavior ?

communications?

collaborations?

negotiations?

AI

MA + DAI

ContentsContents AI techniques

- agents

- agent knowledge representation

- agent inference and reasoning

- agent learning MA ＆ DAI

- agent interactions and communications

- agent collaborations

- agent negotiations

- a multi-agents system

Syllabus and schedule

MethodMethod

lecture references 1. “Artificial Intelligence – A Modern Approach”, Stuart Russell and Peter Norvig, Prentice Hall,

ISBN 0- 13-103805-2 　 (English version).

2. “Multiagent Systems-A Modern Approach to Distributed Artificial Intelligence”,

edited by Gerhard Weiss, The MIT Press, ISBN 0-262-23203-0, 1999.

3. “Multi-Agent Systems – An Introduction to Distributed Artificial Intelligence”,

Jacques Ferber, Addison Wesley, ISBN 0-201-36048-9, 1999.

4. “Jess in Auction – Rule-based Systems in Java”, Ernest Friedman-Hill, Manning,

ISBN 1-930110-89-8.

readings and seminar references

writing reports (final report and presentation)

focus on one of the papers or systems from the references and write a report that includes your

understanding and ideas.

Agent - Definitions?Agent - Definitions?

American Heritage Dictionary:”... One that acts or has the power or authority to act ... or represent another”

Russel and Norvig: ”An agent is anything that can be viewed as perceiving its environment through sensors and

acting upon that environment through effectors.”

Maes, Pattie:”Autonomous Agents are computational systems that inhabit some complex dynamic

environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed”.

Hayes-Roth:”Intelligent Agents continuously perform three functions: perception of dynamic conditions in

the environment; action to affect conditions in the environment; and reasoning to interpret perceptions, solve problems, draw inferences, and determine actions.

...... (what is your definition?)

Agent - Properties?Agent - Properties?

Wooldridge and Jennings:An Agent is a piece of hardware or (more commonly) software-based computer

system that enjoys the following properties:• Autonomy: agents operate without the direct intervention of humans or

others, and have some kind of control over their actions and internal state;• Pro-activeness: agents do not simply act in response to their environment,

they are able to exhibit goal-directed behavior by taking the initiative.• Reactivity: agents perceive their environment and respond to it in timely

fashion to changes that occur in it.• Social Ability: agents interact with other agents (and possibly humans) via

some kind of agent-communication language.”

• Mobility: the ability of an agent to move around a network

• Rationality: an agent will act in order to achieve its goals and will not act in such a way as to prevent its goals being achieved”

Agent?Agent?

There are many definitions of agents• Often quite specific

• Or extremely general

In summary, an agent

act or behave rationally on behalf another user or entity

has some of the above characteristics

Many NamesMany Names

Many synonyms of the term ”intelligent agent”

» Robots

» Software Agents or Softbots

» Knowbots

» Taskbots

» Userbots

» ...

Related FieldsRelated Fields

Fields that inspired the Agent field?• Artificial Intelligence

- Agent Intelligence, Micro-aspects of Agents

• Software Engineering

- Agent as an abstraction

• Distributed Systems and Computer Networks

- Agent Architectures, Multi-Agent Systems, Coordination

• Game Theory and Economics

- Negotiation

How to design the agent program

• agent = architecture + agent program– The architecture, in general,

• makes the percepts from the sensors available to the program,

• runs the program,

• feeds the program action’s choices to the effectors

– architecture may be • a plain computer

• a special-purpose hardware

• some software

– The agent program is a function that implements agent mapping from percepts to actions. It is run on the architecture.

agent program

percepts　in

actions　out

Build an Agent ProgramBuild an Agent Program

Percepts

Actions

Goals

Environment Simulating　the　real　world

　toward　the　goal

Clearly　defined　

from　the　environment

Four necessary components of building an agent program:

Agent

Environment

ActionInput

SensorInput

An example: designing an automated taxi driver

自動タクシー運転手

Percepts

Actions

Goals

Environment

traffic　light,　other　traffic,　pedestrians,　in　Japan

steer,　accelerate,　brake

Safely　to　destination

cameras,　speedometer,　GPS,　sonar

Four　types　of　agent　program:

　　　　　　　　-Simple　reflex　agents

-Agents　that　keep　track　of　the　world

-Goal-based　agents

-Utility-based　agents

The vacuum world: 2 squares

State （状態） : one of the eight states above. （上の 8 状態が全て）

Operators （操作、アクション） : move left （左に移動） , move right （右に移動） , suck （吸取る） .

start-state （初期状態） :Right room has dirt, left room has dirt and vacuum is in left room.（上の図の 1 ）

goal-state （目標状態） : no dirt left in any square. （上の図の 7 または 8 ）

vacuum

dirt

2 4 6 8

1 3 5 7

An example: ｔ he vacuum problem 　自動掃除機

The wumpus world is a grid of squares surrounded by walls, where each square can contain agents and objects. The agent always starts in the lower left corner, a square that we will label [1,1]. The agent’s task is to find the gold, return to [1,1] and climb out of the cave.

1 2 3 4

4 3 2 1

s p

START

ggw b

A p

p

b

b

b

b

b

s

s

A

b

gg

p

s

w

Agent

Breeze

Gold

Pit

Stench

Wumpus

ok ok

ok ok

ok

ok

０ 1 2 3

4 5 6 7

8 9 10 11

1312 14 15

An example: find the gold in a Wumpus world　　　　　　　　　　　　　金を自動的に探索機

Robots ：　本体（からだ）＋脳みそAn example: ｔ he vacuum problem Vacuuming robot

An example: designing an automated taxi driver Vacuuming robot

An example: find the gold in a Wumpus world Gold finding robot

Brain: 脳みそ

推理ができる行動ができる

• Agent Granularity

• Heterogenity of Agents

• Methods of distributing control (among agents)

• Communication Possibilities

MAS – coarse agent granularity and high-level communication

MAS &DAIMAS &DAI

is concerned with

• To solve problems too large for a centralized agent

• To allow interconnecting and interoperation of multiple legacy systems

• To provide a solution to inherently distributed problems

• To provide solutions where expertise is distributed

• To offer conceptual clarity and simplicity of design

MASMASis

• Faster problem solving

• Decreasing communication

• Flexibility

• Increased reliability

the benefits are