Transcript Document

L1. Introduction
Motivations
 Human world
 physical
world
 humans
 knowledge
 reasoning
 action/behavior
 communications
Agent world
 computers
(virtual space) + the Internet
 agents
 knowledge
 acquisition ?
 representation ?
 knowledge base ?
AI
 uncertainty ?
 reasoning ?
 action/behavior ?
 collaborations
 negotiations
 communications?
 collaborations?
UI: ubiquitous intelligence
 negotiations?
MA + DAI
Contents
 AI
techniques
Syllabus and schedule
- agents
- agent knowledge representation
- agent inference and reasoning
- agent learning
 MA ＆DAI
- agent interactions and communications
- agent collaborations
- agent negotiations
- a multi-agents system
Ubiquitous Intelligence
- context from the real world
- intelligence processing and responding to the context
Method
 lecture
references
1. “Artificial Intelligence – A Modern Approach”, Stuart Russell and Peter Norvig, Prentice Hall,
ISBN 0- 13-103805-2
(English version).
2. “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?
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?
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?
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 Names
Many synonyms of the term ”intelligent agent”
» Robots
» Software Agents or Softbots
» Knowbots
» Taskbots
» Userbots
» ...
Related 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
•
Ubiquitous Intelligence
- Agent linked with the real world
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.
percepts in
actions out
agent program
Build an Agent Program
Four necessary components of building an agent program:
Percepts
from the environment
Actions
toward the goal
Goals
Clearly defined
Agent
Action
Input
Sensor
Input
Environment Simulating the real world
Environment
An example: designing an automated taxi driver
自動タクシー運転手
Percepts
cameras, speedometer, GPS, sonar
Actions
steer, accelerate, brake
Goals
Safely to destination
Environment
traffic light, other traffic, pedestrians, in Japan
Four types of agent program:
-Simple reflex agents
-Agents that keep track of the world
-Goal-based agents
-Utility-based agents
An example: ｔhe vacuum problem 自動掃除機
The vacuum world: 2 squares
vacuum
1
3
5
7
2
4
6
8
dirt
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）
An example: find the gold in a Wumpus world
金を自動的に探索機
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.
12
13
14
s
4
9
w
s
g
4
2
b
10
6
7
ok
1
ok
2
b
START
ok
1
b
b
A
b
Breeze
g
Gold
p
Pit
s
Stench
3
p
b
3
4
ok
2
Agent
11
p
s
０
A
ok
5
ok
1
p
b
8
3
15
w
Wumpus
Robots： 本体（からだ）＋脳みそ
An example: ｔhe vacuum problem
Vacuuming robot
An example: designing an automated taxi driver
Taxi 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
MAS &DAI
is concerned with
•
•
•
•
Agent Granularity
Heterogenity of Agents
Methods of distributing control (among agents)
Communication Possibilities

MAS – coarse agent granularity and high-level
communication
MAS
is
•
•
•
•
•
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
the benefits are
•
•
•
•
Faster problem solving
Decreasing communication
Flexibility
Increased reliability