COMP 4640 Intelligent

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COMP 4640Intelligent Interactive Systems
Lecture 2 Intelligent Agents

• Cheryl Seals, Ph.D.
• Computer Science
• Software Engineering
• Auburn University

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Intelligent Interactive Systems Lecture2

• Intelligent Agents
• An Agent is anything that perceives its
  environment via sensors and acts upon that
  environment via effectors.
• In this course, we will be interested in the
  design of rational agents, i.e. agents that do
  the right thing giving a sequence of percepts.
• In order to design rational agents (and also to
  be able to judge whether they are actually
  rational!) we must know
• how to evaluate an agents success
• when to evaluate an agents success
• The how part is accomplished through the
  development of a performance measure.
• For the when part, it may be best to measure
  performance over an extended period of time
  however, at times it may be advantageous to have
  intermediate performance measurements along the
  way.

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Agents

• An agent is anything that can be viewed as
  perceiving its environment through sensors and
  acting upon that environment through actuators
  
• Human agent eyes, ears, and other organs for
  sensors hands,
• legs, mouth, and other body parts for actuators
• Robotic agent cameras and infrared range finders
  for sensors
• various motors for actuators

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Agents and environments

• 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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Vacuum-cleaner world

• Percepts location and contents, e.g., A,Dirty
• Actions Left, Right, Suck, NoOp

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Intelligent Interactive Systems Lecture2

• For an agent, one can determine rational behavior
  based on
• the performance measure of the agent,
• the percept sequence (this is the collection of
  all of the percepts received by the agent),
• what the agent currently knows about the
  environment, and
• the set of actions that the agent can presently
  perform.

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Rational agents

• An agent should strive to "do the right thing",
  based on what it can perceive and the actions it
  can perform. The right action is the one that
  will cause the agent to be most successful
• Performance measure An objective criterion for
  success of an agent's behavior
  
• E.g., performance measure of a vacuum-cleaner
  agent could be amount of dirt cleaned up, amount
  of time taken, amount of electricity consumed,
  amount of noise generated, etc.

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Intelligent Interactive Systems Lecture2

• Ideal Rational Agents
• An ideal rational agent is one that performs any
  action that is expected to maximize its
  performance measure given its percept sequence
  and its built-in knowledge.
• Ideal rational agents are formed by ideal
  mappings from percept sequences to actions
• An ideal rational agent may have some sense of
  autonomy. That is, it may be able to learn,
  adapt, and operate successfully in a variety of
  environments.

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Intelligent Interactive Systems Lecture2

• Structure of Intelligent Agents
• The objective of AI is the design and application
  of agent programs that implement mappings between
  percepts and actions
• An agent can be viewed as a program that is
  developed to run on a particular architecture
  (some computing device).
• The architecture
• makes percepts available,
• runs the agent program,
• sends actions to the effectors
• Types of Agent Programs
• Intelligent systems are typically composed of a
  number of intelligent agents. Each agent
  interacts (directly or indirectly) with one or
  more aspects of an environment. This type of
  agent interaction is similar to what we see in
  sports, business, and other organizations that
  are composed of a number of different agents with
  different responsibilities working together for
  the common good.

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Intelligent Interactive Systems Lecture2

• There are a number of different agent programs
  however, many can be classified as one of the
  following
• Simple Reflex (Reactive) Agents
• Goal-based Agents
• Model-based Agents
• Utility-based Agents
• Communication-based Agents

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Simple reflex agents
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Vacuum-cleaner world

• Percepts location and contents, e.g., A,Dirty
• Actions Left, Right, Suck, NoOp

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Model-based reflex agents
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Goal-based agents

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Utility-based agents
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Learning agents
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Next Class

• Chapter 2 (continued)
• Agent Examples
• Agent Environments