Intelligent Agents and Search Problems

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Intelligent Agents and Search Problems

• Chapters 2 3

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Outline

• Intelligent Agents
• Agents and environments
• Rationality
• PEAS (Performance measure, Environment,
  Actuators, Sensors)
• Environment types
• Agent types
• Search Problems

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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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A vacuum-cleaner agent
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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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Rational agents

• 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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PEAS

• PEAS Performance measure, Environment,
  Actuators, Sensors
• Must first specify the setting for intelligent
  agent design
  
• Consider, e.g., the task of designing an
  automated taxi driver
  
• Performance measure
• Environment
• Actuators
• Sensors

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PEAS

• Must first specify the setting for intelligent
  agent design
  
• Consider, e.g., the task of designing an
  automated taxi driver
  
• Performance measure Safe, fast, legal,
  comfortable trip, maximize profits
  
• Environment Roads, other traffic, pedestrians,
  customers
  
• Actuators Steering wheel, accelerator, brake,
  signal, horn
  
• Sensors Cameras, sonar, speedometer, GPS,
  odometer, engine sensors, keyboard
  

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PEAS

• Agent Medical diagnosis system
• Performance measure Healthy patient, minimize
  costs, lawsuits
• Environment Patient, hospital, staff
• Actuators Screen display (questions, tests,
  diagnoses, treatments, referrals)
  
• Sensors Keyboard (entry of symptoms, findings,
  patient's answers)

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PEAS

• 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

• Agent Interactive English tutor
• Performance measure Maximize student's score on
  test
• Environment Set of students
• Actuators Screen display (exercises,
  suggestions, corrections)
• Sensors Keyboard

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Environment types

• Fully observable (vs. partially observable) An
  agent's sensors give it access to the complete
  state of the environment at each point in time.
  
• Deterministic (vs. stochastic) The next state of
  the environment is completely determined by the
  current state and the action executed by the
  agent. (If the environment is deterministic
  except for the actions of other agents, then the
  environment is strategic)
• Episodic (vs. sequential) The agent's experience
  is divided into atomic "episodes" (each episode
  consists of the agent perceiving and then
  performing a single action), and the choice of
  action in each episode depends only on the
  episode itself.

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Environment types

• Static (vs. dynamic) The environment is
  unchanged while an agent is deliberating. (The
  environment is semidynamic if the environment
  itself does not change with the passage of time
  but the agent's performance score does)
• Discrete (vs. continuous) A limited number of
  distinct, clearly defined percepts and actions.
  
• Single agent (vs. multiagent) An agent operating
  by itself in an environment.
  

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Environment types

• Chess with Chess without Taxi driving
• a clock a clock
• Fully observable Yes Yes No
• Deterministic Strategic Strategic No
• Episodic No No No
• Static Semi Yes No
• Discrete Yes Yes No
• Single agent No No No
• The environment type largely determines the agent
  design
  
• The real world is (of course) partially
  observable, stochastic, sequential, dynamic,
  continuous, multi-agent
  

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Agent types

• Four basic types in order of increasing
  generality
  
• Simple reflex agents
• Agents that keep track of the world
• Goal-based agents
• Utility-based agents

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Simple reflex agents
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Simple reflex agents
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Agents that keep track of the world (Agents with
internal states)
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Agents with internal states
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Goal-based 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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Outline

• Intelligent Agents
• Agents and environments
• Rationality
• PEAS (Performance measure, Environment,
  Actuators, Sensors)
• Environment types
• Agent types
• Search Problems

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Search and AI

• Search methods are ubiquitous in AI systems. They
  often are the backbones of both core and
  peripheral modules
• An autonomous robot uses search methods
• to decide which actions to take and which sensing
  operations to perform,
• to quickly anticipate collision,
• to plan trajectories,
• to interpret large numerical datasets provided by
  sensors into compact symbolic representations,
• to diagnose why something did not happen as
  expected,
• etc...
• Many searches may occur concurrently and
  sequentially

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Applications

• Search plays a key role in many applications,
  e.g.
• Route finding airline travel, networks
• Package/mail distribution
• Pipe routing, VLSI routing
• Comparison and classification of protein folds
• Pharmaceutical drug design
• Design of protein-like molecules
• Video games

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Example 8-Puzzle
State Any arrangement of 8 numbered tiles and an
empty tile on a 3x3 board
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8-Puzzle Successor Function
The successor function is knowledgeabout the
8-puzzle game, but it does not tell us which
outcome to use, nor to which state of the board
to apply it.
Search is about the exploration of alternatives
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• Across history, puzzles and games requiring the
  exploration of alternatives have been considered
  a challenge for human intelligence
• Chess originated in Persia and India about 4000
  years ago
• Checkers appear in 3600-year-old Egyptian
  paintings
• Go originated in China over 3000 years ago

So, its not surprising that AI uses games to
design and test algorithms
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15-Puzzle

• Introduced in 1878 by Sam Loyd, who dubbed
  himself Americas greatest puzzle-expert

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15-Puzzle

• Sam Loyd offered 1,000 of his own money to the
  first person who would solve the following
  problem

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• But no one ever won the prize !!

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Stating a Problem as a Search Problem
S

• State space S
• Successor function x ? S ? SUCCESSORS(x) ?
  2S
• Initial state s0
• Goal test
• x?S ? GOAL?(x) T or F
• Arc cost

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State Graph

• Each state is represented by a distinct node
• An arc (or edge) connects a node s to a node s
  if s ? SUCCESSORS(s)
• The state graph may contain more than one
  connected component

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Solution to the Search Problem

• A solution is a path connecting the initial node
  to a goal node (any one)

G
I
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Solution to the Search Problem

• A solution is a path connecting the initial node
  to a goal node (any one)
• The cost of a path is the sum of the arc costs
  along this path
• An optimal solution is a solution path of minimum
  cost
• There might be no solution !

I
G
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How big is the state space of the (n2-1)-puzzle?

• 8-puzzle ? ?? states

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How big is the state space of the (n2-1)-puzzle?

• 8-puzzle ? 9! 362,880 states
• 15-puzzle ? 16! 2.09 x 1013 states
• 24-puzzle ? 25! 1025 states
• But only half of these states are reachable from
  any given state(but you may not know that in
  advance)

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Permutation Inversions

• Let the goal be
• A tile j appears after a tile i if, if either j
  appears on the same row as i to the right of i,
  or on another row below the row of i.
• For all i 1, 2, ..., 15, let ni be the number
  of tiles j lt i that appear after tile i
  (permutation inversions)
• N n2 n3 ? n15 row number of empty tile

n2 0 n3 0 n4 0 n5 0 n6 0 n7 1 n8
1 n9 1 n10 4 n11 0 n12 0 n13 0 n14
0 n15 0
? N 7 4
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• Proposition (N mod 2) is invariant under any
  legal move of the empty tile
• Proof
• Any horizontal move of the empty tile leaves N
  unchanged
• A vertical move of the empty tile changes N by an
  even increment (? 1 ? 1 ? 1 ? 1)

N(s) N(s) 3 1
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• Proposition (N mod 2) is invariant under any
  legal move of the empty tile
• ? For a goal state g to be reachable from a state
  s, a necessary condition is that N(g) and N(s)
  have the same parity
• It can be shown that this is also a sufficient
  condition
• ? The state graph consists of two connected
  components of equal size

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N 4
N 5

• So, the second state is not reachable from the
  first, and Sam Loyd took no risk with his money
  ...

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What is the Actual State Space?

• The set of all states? e.g., a set of 16!
  states for the 15-puzzle
• The set of all states reachable from a given
  initial state? e.g., a set of 16!/2 states for
  the 15-puzzle
• In general, the answer is a) because one does
  not know in advance which states are reachable

But a fast test determining whether a state is
reachable from another is very useful, as search
techniques are often inefficient when a problem
has no solution
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Searching the State Space

• It is often not feasible (or too expensive) to
  build a complete representation of the state
  graph

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8-, 15-, 24-Puzzles
8-puzzle ? 362,880 states
15-puzzle ? 2.09 x 1013 states 24-puzzle ?
1025 states
100 millions states/sec
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Searching the State Space

• Often it is not feasible (or too expensive) to
  build a complete representation of the state
  graph
• A problem solver must construct a solution by
  exploring a small portion of the graph

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Searching the State Space
Search tree
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Searching the State Space
Search tree
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Searching the State Space
Search tree
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Searching the State Space
Search tree
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Searching the State Space
Search tree
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Searching the State Space
Search tree
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Simple Problem-Solving-Agent Algorithm

• I ? sense/read initial state
• GOAL? ? select/read goal test
• Succ ? select/read successor function
• solution ? search(I, GOAL?, Succ)
• perform(solution)