Transcript Real-Time Input of 3D Pose and Gestures of a User`s Hand and Its

Agents and Intelligent Agents
 An agent is anything that can be viewed as
 perceiving
its environment through sensors and
 acting upon that environment through actuators
 An intelligent agent acts further for its own interests.
Artificial Intelligence, Lecturer #8
Example of Agents
 Human agent:
 Sensors: eyes, ears, nose….
 Actuators: hands, legs, mouth, …
 Robotic agent:
 Sensors: cameras and infrared range finders
 Actuators: various motors
 Agents include humans, robots, thermostats, etc
 Perceptions: Vision, speech reorganization, etc.
Agent Function & program
 An agent is specified by an agent function f
sequences of percepts Y to actions A:
that maps
Y  { y0 , y1 ,..., yT }
A  {a0 , a1 ,..., aT }
f :Y  A
The agent program runs on the physical architecture to
produce f
 agent = architecture + program
“Easy” solution: table that maps every possible sequence Y
to an action A
Agents and Environments
The agent function maps from percept histories
(sequences of percepts) to actions:
[f: P*  A]
Example: A Vacuum-Cleaner Agent
A

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B
Percepts: location and contents, e.g., (A,dust)
• (Idealization: locations are discrete)
Actions: move, clean, do nothing:
LEFT, RIGHT, SUCK, NOP
Example: A Vacuum-Cleaner Agent
Properties of Agent
 Mobility: the ability of an agent to move around in an environment.
 Veracity: an agent will not knowingly communicate false information
 Benevolence: agents do not have conflicting goals, and that every
agent will therefore always try to do what is asked of it
 Rationality: agent will act in order to achieve its goals, and will not
act in such a way as to prevent its goals being achieved.
 Learning/adoption: agents improve performance over time
Agents Vs. Objects

Agents are autonomous
agents embody stronger notion of autonomy than objects, and in particular, t
hey decide for themselves whether or not to perform an action on request fr
om another agent

Agents are smart
capable of flexible (reactive, pro-active, social) behavior, and the standard obj
ect model has nothing to say about such types of behavior

Agents are active
a multi-agent system is inherently multi-threaded, in that each agent is assu
med to have at least one thread of active control
The Concept of Rationality
What is rational at any given time depends on four
things:
 The performance measure that defines the criterion of
success.
 The agent’s prior knowledge of the environment.
 The actions the agent can perform.
 The agent’s percept sequence to date.
Rational Agents
 Rational Agent:
For each possible percept sequence, a rational agent should
select an action that is expected to maximize its performance
measure.
 Performance measure:
An objective criterion for success of an agent's behavior, given
the evidence provided by the percept sequence.
Nature of Task Environment
 To design a rational agent we need to specify a task environment
 a problem specification for which the agent is a
 PEAS: to specify a task environment
 Performance measure
 Environment
 Actuators
 Sensors
solution
PEAS
Specifying an Automated Taxi Driver
Performance measure:
 safe, fast, legal, comfortable, maximize profits
Environment:
 roads, other traffic, pedestrians, customers
Actuators:
 steering, accelerator, brake, signal, horn
Sensors:
 cameras, sonar, speedometer, GPS
PEAS: Another Example
 Agent: Medical diagnosis system
 Performance measure:
Healthy patient, minimize costs.
 Environment:
Patient, hospital, staff
 Actuators:
Screen display (questions, tests, diagnoses, treatments, referrals)
 Sensors:
Keyboard (entry of symptoms, findings, patient's answers)
Recommended Textbooks
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[Negnevitsky, 2001] M. Negnevitsky “ Artificial Intelligence: A guide to
Intelligent Systems”, Pearson Education Limited, England, 2002.
[Russel, 2003] S. Russell and P. Norvig Artificial Intelligence: A Modern
Approach Prentice Hall, 2003, Second Edition
[Patterson, 1990] D. W. Patterson, “Introduction to Artificial Intelligence
and Expert Systems”, Prentice-Hall Inc., Englewood Cliffs, N.J, USA, 1990.
[Minsky, 1974] M. Minsky “A Framework for Representing Knowledge”,
MIT-AI Laboratory Memo 306, 1974.
[Hubel, 1995] David H. Hubel, “Eye, Brain, and Vision”
[Ballard, 1982] D. H. Ballard and C. M. Brown, “Computer Vision”,
Prentice Hall, 1982.