L1. Introduction. Motivations Human world physical world humans knowledge reasoning action/behavior...
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Transcript of L1. Introduction. Motivations Human world physical world humans knowledge reasoning action/behavior...
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