CS451CS551EE565 ARTIFICIAL INTELLIGENCE

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CS451/CS551/EE565ARTIFICIAL INTELLIGENCE

• Agent Types
• 8-29-2008
• Prof. Janice T. Searleman
• jets_at_clarkson.edu, jetsza

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Recap Rational Agents

• What is rational at a given time depends on four
  things
• Performance Measure
• Prior environment knowledge
• Actions
• Percept sequence to date (sensors).
• Defn A rational agent chooses whichever action
  maximizes the expected value of the performance
  measure given the percept sequence to date and
  prior environment knowledge.

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Recap Agent functions

• An agent is completely specified by the agent
  function mapping percept sequences to actions
• One agent function (or a small equivalence class)
  is rational
• Aim find a way to implement the rational agent
  function concisely

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Recap Structure of Intelligent Agents

• Agent architecture program
• Agent program the implementation of f P ? A,
  the agents perception-action mappingfunction
  Skeleton-Agent(Percept) returns Action memory ?
  UpdateMemory(memory, Percept) Action ?
  ChooseBestAction(memory) memory ?
  UpdateMemory(memory, Action)return Action
• Architecture a device that can execute the agent
  program (e.g., general-purpose computer,
  specialized device, bot, etc.)?

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Recap Environments

• To design a rational agent, we must specify its
  task environment.
• PEAS way to describe the environment
• Performance
• Environment
• Actuators
• Sensors

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Recap Environment types
The environment types largely determine the agent
design.
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Agent types

• Four basic kind of agent programs will be
  discussed
• Simple reflex agents
• Model-based reflex agents
• Goal-based agents
• Utility-based agents
• All these can be turned into learning agents.

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Agent types

• Function TABLE-DRIVEN_AGENT(percept) returns an
  action
• static percepts, a sequence initially empty
• table, a table of actions, indexed by percept
  sequence
• append percept to the end of percepts
• action ? LOOKUP(percepts, table)?
• return action

This approach is doomed to failure
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Agent types simple reflex

• Select action on the basis of only the current
  percept.
• e.g. the vacuum-agent
• Large reduction in possible percept/action
  situations(next page).
• Implemented through condition-action rules
• If dirty then suck

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Vacuum-cleaner world

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

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The vacuum-cleaner world

• function REFLEX-VACUUM-AGENT (location, status)
  return an action
• if status Dirty then return Suck
• else if location A then return Right
• else if location B then return Left
• Reduction from 4T to 4 entries

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Agent types simple reflex

• function SIMPLE-REFLEX-AGENT(percept) returns an
  action
• static rules, a set of condition-action rules
• state ? INTERPRET-INPUT(percept)
• rule ? RULE-MATCH(state, rule)
• action ? RULE-ACTIONrule
• return action
• Will only work if the environment is fully
  observable otherwise infinite loops may occur.

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Agent types reflex and state

• To tackle partially observable environments.
• Maintain internal state
• Over time update state using world knowledge
• How does the world change.
• How do actions affect world.
• ? Model of World

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Agent types reflex and state

• function REFLEX-AGENT-WITH-STATE(percept) returns
  an action
• static rules, a set of condition-action rules
• state, a description of the current world state
• action, the most recent action.
• state ? UPDATE-STATE(state, action, percept)
• rule ? RULE-MATCH(state, rule)
• action ? RULE-ACTIONrule
• return action

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Agent types goal-based

• The agent needs a goal to know which situations
  are desirable.
• Things become difficult when long sequences of
  actions are required to find the goal.
• Typically investigated in search and planning
  research.
• Major difference future is taken into account
• Is more flexible since knowledge is represented
  explicitly and can be manipulated.

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Agent types utility-based

• Certain goals can be reached in different ways.
• Some are better, have a higher utility.
• Utility function maps a (sequence of) state(s)
  onto a real number.
• Improves on goals
• Selecting between conflicting goals
• Select appropriately between several goals based
  on likelihood of success.

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Performance Measure vs.Utility Function

• performance measure
• - used by outside observer to evaluate success
• - function from histories to a real number
• utility function
• - used by the agent itself to evaluate how
  desirable states or histories are
• - function from state(s) to a real number

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Agent types learning

• All previous agent-programs describe methods for
  selecting actions.
• Yet it does not explain the origin of these
  programs.
• Learning mechanisms can be used to perform this
  task.
• Teach them instead of instructing them.
• Advantage is the robustness of the program toward
  initially unknown environments.

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Agent types learning

• Learning element introduce improvements in
  performance element.
• Critic provides feedback on agents performance
  based on fixed performance standard.
• Performance element selecting actions based on
  percepts.
• Corresponds to the previous agent programs
• Problem generator suggests actions that will
  lead to new and informative experiences.
• Exploration vs. exploitation

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Behavior and performance of IAs

• Perception (sequence) to Action Mapping
• f P ? A
• Ideal mapping specifies which actions an agent
  ought to take at any point in time
• Description Look-Up-Table, Closed Form,

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A driving example Beobots

• Goal build robots that can operate in
  unconstrained environments and that can solve a
  wide variety of tasks.

Beowulf Robot Beobot http//iLab.usc.edu/be
obots/
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Beowulf robot Beobot
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Look up table

• Compare to a closed form
• Output (degree of rotation) F(distance)
• example F(d) 10/d

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Using a look-up-table to encode f P ? A

• Example Collision Avoidance
• Sensors 3 proximity sensors
• Effectors Steering Wheel, Brakes
• How to generate?
• How large?
• How to select action?

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Using a look-up-table to encode f P?A

• Example Collision Avoidance
• Sensors 3 proximity sensors
• Effectors Steering Wheel, Brakes
• How to generate for each p ? Pl ? Pm ?
  Prgenerate an appropriate action, a ? S ? B
• How large size of table possible percepts
  times possible actions Pl Pm Pr S
  BE.g., P close, medium, far3 A left,
  straight, right ? on, offthen size of table
  2732 162
• How to select action? Search.

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Behavior and performance of IAs

• Perception (sequence) to Action Mapping
• f P ? A
• Ideal mapping specifies which actions an agent
  ought to take at any point in time
• Description Look-Up-Table, Closed Form,

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Interacting Agents

• Collision Avoidance Agent (CAA)?
• Goals Avoid running into obstacles
• Percepts ?
• Sensors?
• Effectors ?
• Actions ?
• Environment Freeway

• Lane Keeping Agent (LKA)?
• Goals Stay in current lane
• Percepts ?
• Sensors?
• Effectors ?
• Actions ?
• Environment Freeway

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Interacting Agents

• Collision Avoidance Agent (CAA)?
• Goals Avoid running into obstacles
• Percepts Obstacle distance, velocity,
  trajectory
• Sensors Vision, proximity sensing
• Effectors Steering Wheel, Accelerator, Brakes,
  Horn, Headlights
• Actions Steer, speed up, brake, blow horn,
  signal (headlights)?
• Environment Freeway

• Lane Keeping Agent (LKA)?
• Goals Stay in current lane
• Percepts Lane center, lane boundaries
• Sensors Vision
• Effectors Steering Wheel, Accelerator, Brakes
• Actions Steer, speed up, brake
• Environment Freeway

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Conflict Resolution by Action Selection Agents

• Override CAA overrides LKA
• Arbitrate if Obstacle is Close then CAA else
  LKA
• Compromise Choose action that satisfies
  both agents
• Any combination of the above
• Challenges Doing the right thing

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Summary

• Intelligent Agents
• Anything that can be viewed as perceiving its
  environment through sensors and acting upon that
  environment through its effectors to maximize
  progress towards its goals.
• PAGE (Percepts, Actions, Goals, Environment)
• Described as a Perception (sequence) to Action
  Mapping f P ? A
• Using look-up-table, closed form, etc.
• Agent Types reflex, state, goal-based,
  utility-based
• Rational Action The action that maximizes the
  expected value of the performance measure given
  the percept sequence to date