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CS 416 Artificial Intelligence. Lecture 2 Agents. Chess Article. Deep Blue (IBM) 418 processors, 200 million positions per second Deep Junior (Israeli Co.) 8 processors, 3 million positions per second Kasparov 100 billion neurons in brain, 2 moves per second

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chess article
Chess Article
  • Deep Blue (IBM)
    • 418 processors, 200 million positions per second
  • Deep Junior (Israeli Co.)
    • 8 processors, 3 million positions per second
  • Kasparov
    • 100 billion neurons in brain, 2 moves per second
  • But there are 85 billion ways to play the first four moves
chess article1
Chess Article
  • 1997 - Kasparov Lost to Deep Blue
  • 2002 - Kramnik tied Deep Junior (current World Champion)
  • 2003 - Kasparov (current number 1) plays Deep Junior
  • Jan 26 – Feb 7
chess article2
Chess Article
  • Cognitive psychologists report chess is a game of pattern matching for humans
    • But what patterns do we see?
    • What rules do we use to evaluate perceived patterns?
what is an agent
What is an agent?
  • Perception
    • Sensors receive input from environment
      • Keyboard clicks
      • Camera data
      • Bump sensor
  • Action
    • Actuators impact the environment
      • Move a robotic arm
      • Generate output for computer display
perception
Perception
  • Percept
    • Perceptual inputs at an instant
    • May include perception of internal state
  • Percept Sequence
    • Complete history of all prior percepts
  • Do you need a percept sequence to play Chess?
an agent as a function
An agent as a function
  • Agent maps percept sequence to action
    • Agent:
      • Set of all inputs known as state space
  • Agent Function
    • If inputs are finite, a table can store mapping
    • Scalable?
    • Reverse Engineering?
evaluating agent programs
Evaluating agent programs
  • We agree on what an agent must do
  • Can we evaluate its quality?
  • Performance Metrics
    • Very Important
    • Frequently the hardest part of the research problem
    • Design these to suit what you really want to happen
rational agent
Rational Agent
  • For each percept sequence, a rational agent should select an action that maximizes its performance measure
  • Example: autonomous vacuum cleaner
    • What is the performance measure?
  • Penalty for eating the cat? How much?
  • Penalty for missing a spot?
  • Reward for speed?
  • Reward for conserving power?
learning and autonomy
Learning and Autonomy
  • Learning
    • To update the agent function in light of observed performance of percept-sequence to action pairs
      • Explore new parts of state space
        • Learn from trial and error
      • Change internal variables that influence action selection
adding intelligence to agent function
Adding intelligence to agent function
  • At design time
    • Some agents are designed with clear procedure to improve performance over time. Really the engineer’s intelligence.
      • Camera-based user identification
  • At run-time
    • Agent executes complicated equation to map input to output
  • Between trials
    • With experience, agent changes its program (parameters)
how big is your percept
How big is your percept?
  • Dung Beetle
    • Largely feed forward
  • Sphex Wasp
    • Reacts to environment (feedback) but not learning
  • A Dog
    • Reacts to environment and can significantly alter behavior
qualities of a task environment
Qualities of a task environment
  • Fully Observable
    • Agent need not store any aspects of state
      • The Brady Bunch as intelligent agents
      • Volume of observables may be overwhelming
  • Partially Observable
    • Some data is unavailable
      • Maze
      • Noisy sensors
qualities of a task environment1
Qualities of a task environment
  • Deterministic
    • Always the same outcome for state/action pair
  • Stochastic
    • Not always predictable – random
  • Partially Observable vs. Stochastic
    • My cats think the world is stochastic
    • Physicists think the world is deterministic
qualities of a task environment2
Qualities of a task environment
  • Markovian
    • Future state only depends on current state
  • Episodic
    • Percept sequence can be segmented into independent temporal categories
      • Behavior at traffic light independent of previous traffic
  • Sequential
    • Current decision could affect all future decisions
  • Which is easiest to program?
qualities of a task environment3
Qualities of a task environment
  • Static
    • Environment doesn’t change over time
      • Crossword puzzle
  • Dynamic
    • Environment changes over time
      • Driving a car
  • Semi-dynamic
    • Environment is static, but performance metrics are dynamic
      • Drag racing
qualities of a task environment4
Qualities of a task environment
  • Discrete
    • Values of a state space feature (dimension) are constrained to distinct values from a finite set
      • Blackjack: f(your cards, exposed cards) = action
  • Continuous
    • Variable has infinite variation
      • Antilock brakes: f (vehicle speed, wheel velocity) = unlock
      • Are computers really continuous?
qualities of a task environment5
Qualities of a task environment
  • Towards a terse description of problem domains
    • State space: features, dimensionality, degrees of freedom
    • Observable?
    • Predictable?
    • Dynamic?
    • Continuous?
    • Performance metric
building agent programs
Building Agent Programs
  • The table approach
    • Build a table mapping states to actions
      • Chess has 10150 entries (1080 atoms in the universe)
      • I’ve said memory is free, but keep it within the confines of the boundable universe
    • Still, tables have their place
  • Discuss four agent program principles
simple reflex agents
Simple Reflex Agents
    • Sense environment
    • Match sensations with rules in database
    • Rule prescribes an action
  • Reflexes can be bad
    • Don’t put your hands down when falling backwards!
  • Inaccurate information
    • Misperception can trigger reflex when inappropriate
  • But rules databases can be made large and complex
simple reflex agents1
Simple Reflex Agents
  • Randomization
    • The vacuum cleaner problem

Dirty

Left

Right

model based reflex agents
Model-based Reflex Agents
  • So when you can’t see something, you model it!
    • Create an internal variable to store your expectation of variables you can’t observe
    • If I throw a ball to you and it falls short, do I know why?
      • Aerodynamics, mass, my energy levels…
      • I do have a model
        • Ball falls short, throw harder
model based reflex agents1
Model-based Reflex Agents
  • Admit it, you can’t see and understand everything
  • Models are very important!
    • We all use models to get through our lives
      • Psychologists have many names for these context-sensitive models
    • Agents need models too
goal based agents
Goal-based Agents
  • Lacking moment-to-moment performance measure
  • Overall goal is known
  • How to get from A to B?
    • Current actions have future consequences
    • Search and Planning are used to explore paths through state space from A to B
utility based agents
Utility-based Agents
  • Goal-directed agents that have a utility function
    • Function that maps internal and external states into a scalar
      • A scalar is a number
learning agents
Learning Agents
  • Learning Element
    • Making improvements
  • Performance Element
    • Selecting actions
  • Critic
    • Provides learning element with feedback about progress
  • Problem Generator
    • Provides suggestions for new tasks to explore state space
a taxi driver
A taxi driver
  • Performance Element
    • Knowledge of how to drive in traffic
  • Critic
    • Observes tips from customers and horn honking from other cars
  • Learning Element
    • Relates low tips to actions that may be the cause
  • Problem Generator
    • Proposes new routes to try and improved driving skills
review
Review
  • Outlined families of AI problems and solutions
  • Next class we study search problems