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Today’s Outline
• Defining agents
• Examples of agents
• What is perception
• What are rational agents
• Rationality vs omniscience
• PEAS (Performance measure, Environment, Actuators,
Sensors)
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What is Agent
An agent is an entity which is:
1. Situated in some environment.
2. Autonomous, in the sense that it can act without direct
intervention from humans or other software processes, and
controls over its own actions and internal state.
3. Flexible which means
• Responsive (reactive): agents should perceive their environment and
respond to changes that occur in it.
• Proactive: agents should not simply act in response to their environment,
they should be able to exhibit opportunistic, goal-directed behavior and
take the initiative when appropriate.
• Social: agents should be able to interact with humans or other artificial
agents.
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Agent
• An agent uses perception of the
environment to make decisions about
actions to take.
• The perception capability is usually
called a sensor.
• The actions can depend on the most
recent perception or on the entire
history (percept sequence).
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Agent Definition
• “An agent is anything that can be viewed as perceiving its
environment through sensors and acting upon that
environment through effectors/actuators.” Russell & Norvig, Artificial
Intelligence, A modern Approach, 3rd edition (2010)
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Agent Example
• Human agent:
• Sensors: Eyes, ears and other organs
• Actuators: hands, leg, mouth and other parts of the body.
• Robotic agent:
• Sensors: Cameras and infrared range finders
• Actuators: Various motors
• Software agent:
• Sensors: Keystrokes, file contents, receiving network packets
• Actuators: Displaying on screen, Writing files and sending network
packets
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Agent Function and Agent Program
• The agent function maps from percept histories to actions:
[f: P* A]
• The agent program runs on the physical architecture to
produce f
agent = architecture + program
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Example: Vacuum-cleaner world
• Percepts: location and contents, e.g., [A,Dirty]
• Actions: Left, Right, Suck, NoOp
• Agent’s function → look-up table
• For many agents this is a very large table
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Rational agents
Rationality: What is rational at any given time depends on
four things i.e.
Performance measure that defines success
Agents prior knowledge of environment
Actions that agent can perform
Agent’s percept sequence to date
This leads to the definition of Rational Agent
For each possible percept sequence, a rational agent should
select an action that is expected to maximize its performance
measure, given the evidence provided by the percept
sequence and whatever built-in knowledge the agent has.
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Rationality vs Omniscience
• Rationality is different from omniscience
• Percepts may not supply all relevant information
• E.g., in card game, don’t know cards of others.
• Rational is different from being perfect
• Rationality maximizes expected outcome.
• while perfection maximizes actual outcome.
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Specifying the Task Environments
• Task Environment are essentially the problems to which
rational agents are the solutions.
• In designing an agent the first step must always be to specify
the task environment as fully as possible a.k.a PEAS
(Performance measure, Environment, Actuators, Sensors)
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PEAS - Example # 02
• Agent: Part-Picking Robot
• Performance measure: Percentage of parts in correct bins
• Environment: Conveyor belt with parts, bins
• Actuators: Jointed arm and hand
• Sensors: Camera, joint angle sensors
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PEAS Example #03
• Agent: Specifying an interactive English Tutor
• Performance measure: Maximize student's score on test
• Environment: Set of students
• Actuators: Screen display (exercises, suggestions, corrections)
• Sensors: Keyboard
