Lecture 2: Intelligent Agents

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Lecture 2 Intelligent Agents

• Reading AIMA, Ch. 2

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What is an agent?

• An entity in an environment that perceives it
  through sensors and acts upon it through
  actuators.

Percepts
Environment
Agent
sensor
Modified from "A Kalman Filter Model of the
Visual Cortex", by P. Rao, Neural Computation
9(4)721--763, 1997
actuator
Actions
Agents human, robot, softbot, thermostat,
etc. Agents act on environment to achieve a goal.
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Agent function program

• Agents choice of action is based on a sequence
  of percepts
• Agent is specified by an agent function f that
  maps sequences of percepts Y to actions a
• Agent program implements agent function on a
  physical architecture
• Easy solution table that maps every possible
  sequence Y to an action a
• Problem not feasible

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Example Vacuum-cleaner world
A
B

• Percepts location and contents, e.g., (A,dirty)
• Actions move, clean, do nothing LEFT, RIGHT,
  SUCK, NOP

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Vacuum-cleaner world agent function

• What is the right function?
• Can the function be implemented in a short
  program?

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The right agent function rational behavior

• Rational agent is the one that does the right
  thing functional table is filled out correctly
• What is the right thing?
• Define success through a performance measure, r
• Vacuum-cleaner world
• 1 point for each clean square in time T
• 1 point for clean square, -1 for each move
• -1000 for more than k dirty squares
• Rational agentAn agent who selects an action
  that is expected to maximize the performance
  measure for a given percept sequence and its
  built-in knowledge
• Ideal agent maximizes actual performance, but
  needs to be omniscient. Impossible!
• Builds a model of environment.

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Properties of a rational agent

• Maximize expected performance
• Gathers information does actions to modify
  future percepts
• Explores in unknown environments
• Learns from what it has perceived so far(dung
  beetle, sphex wasp)
• Autonomous increase its knowledge by learning

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Task environment

• To design a rational agent we need to specify a
  task environment problem to which the agent is
  a solution
• P.E.A.S. Performance measure Environment Act
  uators Sensors
• Example 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

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More PEAS examples

• College test-taker
• Internet shopping agent
• Mars lander
• The president

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Properties of task environments
Solitaire Backgammon Internet shopping Taxi
Observable (hidden)
Deterministic (stochastic)
Episodic (sequential)
Static (Dynamic)
Discrete (Continuous)
Single-agent (multi-agent)
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Properties of task environments (contd)
Solitaire Backgammon Internet shopping Taxi
Observable (hidden) Yes Yes No No
Deterministic (stochastic)
Episodic (sequential)
Static (Dynamic)
Discrete (Continuous)
Single-agent (multi-agent)
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Properties of task environments (contd)
Solitaire Backgammon Internet shopping Taxi
Observable (hidden) Yes Yes No No
Deterministic (stochastic) Yes No Partly No
Episodic (sequential)
Static (Dynamic)
Discrete (Continuous)
Single-agent (multi-agent)
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Properties of task environments (contd)
Solitaire Backgammon Internet shopping Taxi
Observable (hidden) Yes Yes No No
Deterministic (stochastic) Yes No Partly No
Episodic (sequential) No No No No
Static (Dynamic)
Discrete (Continuous)
Single-agent (multi-agent)
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Properties of task environments (contd)
Solitaire Backgammon Internet shopping Taxi
Observable (hidden) Yes Yes No No
Deterministic (stochastic) Yes No Partly No
Episodic (sequential) No No No No
Static (Dynamic) Yes Semi Semi No
Discrete (Continuous)
Single-agent (multi-agent)
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Properties of task environments (contd)
Solitaire Backgammon Internet shopping Taxi
Observable (hidden) Yes Yes No No
Deterministic (stochastic) Yes No Partly No
Episodic (sequential) No No No No
Static (Dynamic) Yes Semi Semi No
Discrete (Continuous) Yes Yes Yes No
Single-agent (multi-agent)
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Properties of task environments (contd)
Solitaire Backgammon Internet shopping Taxi
Observable (hidden) Yes Yes No No
Deterministic (stochastic) Yes No Partly No
Episodic (sequential) No No No No
Static (Dynamic) Yes Semi Semi No
Discrete (Continuous) Yes Yes Yes No
Single-agent (multi-agent) Yes No No No
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Structure of agents

• Goal of AI give task environment, construct
  agent function, and design an agent program that
  implements agent function on a particular
  architecture
• Skeleton agent

function SKELETON-AGENT(perceptt) returns
actiont static state, the agents memory of the
world statet Update-State(statet-1,,perceptt,
actiont-1) actiont Choose-Best-Action(statet)
statet Update-Memory(statet,actiont) return
action
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Skeleton agent

• Graphical depiction (we will see more of it later
  in the semester)

at1
st1


yt1
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Agent types

• Simplest agentTable-driven agent for each
  percept sequence Y, has a table entry with
  associated action

function TABLE-DRIVEN-AGENT( percept ) returns
action static sequence or percepts percepts
Update-Percepts( percept ) action Table(
percepts ) return action

• Four basic types, in order of increasing
  complexity
• Simple reflex agent
• Model-based reflex agent (reflex agent with
  state)
• Goal-based agent
• Utility-driven agent

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Simple reflex agent
function REFLEX_VACUUM_AGENT( percept ) returns
action (location,status) UPDATE_STATE(
percept ) if status DIRTY then action
SUCK else if location A then return
RIGHT else if location B then return LEFT
return action
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Model-based reflex agent
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Goal-driven agent
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Utility-based agent
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Learning agent
Any other agent