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CS 416 Artificial Intelligence

CS 416 Artificial Intelligence. Lecture 2 Agents. Review. We’ll study systems that act rationally They need not necessarily “think” or act like humans They need not “think” in rational ways The domain of AI research changes over time AI research draws from many fields

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CS 416 Artificial Intelligence

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  1. CS 416Artificial Intelligence Lecture 2 Agents

  2. Review • We’ll study systems that act rationally • They need not necessarily “think” or act like humans • They need not “think” in rational ways • The domain of AI research changes over time • AI research draws from many fields • Philosophy, psychology, neuroscience, mathematics, economics, mechanical, linguistics • AI has had ups and downs since 1950

  3. 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

  4. 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?

  5. An agent as a function • Agent maps percept sequence to action • Agent: • Set of all inputs known as state space • Repeating loop: • We must construct f( ), our agent • It must act rationally

  6. The agent’s environment • What is known about percepts? • Quantity, range, certainty… • If percepts are finite, could a table store mapping? • What is known about environment? • Is f (a, e) a known function and predictable? • More on this later

  7. 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

  8. Performance vis-à-vis rationality • 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?

  9. Learning and Autonomy • Learning • To update the agent function, ,in light of observed performance of percept-sequence to action pairs • Does the agent control observations? • What parts of state space to explore? • Learn from trial and error • How do observations affect agent function? • Change internal variables that influence action selection

  10. 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)

  11. How big is your percept? • Dung Beetle • Almost no perception (percept) • Rational agents fine-tune actions based on feedback • Sphex Wasp • Has percepts, but lacks percept sequence • Rational agents change plans entirely when fine tuning fails • A Dog • Equipped with percepts and percept sequences • Reacts to environment and can significantly alter behavior

  12. Qualities of a task environment • Fully Observable • Agent need not store any aspects of state • The Brady Bunch as intelligent agents (lost in Hawaii) • Volume of observables may be overwhelming • Partially Observable • Some data is unavailable • Maze • Noisy sensors

  13. Qualities of a task environment • Deterministic • Always the same outcome for environment/action pair • Stochastic • Not always predictable – random • Partially Observable vs. Stochastic • My cats think the world is stochastic (lack of perception) • Physicists think the world is deterministic

  14. Qualities of a task environment • Markovian • Future environment depends only on current environment and action • 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?

  15. 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 (reward for reaching finish line after 12 seconds is different from reward for reaching it after 14 seconds)

  16. Qualities of a task environment • Discrete • Values of a state space feature (dimension) are constrained to distinct values from a finite set • Blackjack: • Continuous • Variable has infinite variation • Antilock brakes: • Are computers really continuous?

  17. Qualities of a task environment • Towards a terse description of problem domains • Environment: features, dimensionality, degrees of freedom • Observable? • Predictable? • Dynamic? • Continuous? • Performance metric

  18. 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

  19. 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

  20. Simple Reflex Agents w/ Incomplete Sensing • How can you react to things you cannot see? • Vacuum cleaning the room w/o any sensors • Vacuum cleaning room w/ bump sensor • Vacuum cleaning room w/ GPS and perfect map of static environment

  21. 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? • I don’t really know why… • Aerodynamics, mass, my energy levels… • I do have a model • Ball falls short, throw harder

  22. 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

  23. Goal-based Agents • Overall goal is known, but lacking moment-to-moment performance measure • Don’t exactly know what performance maximizing action is at each step • Example: • 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

  24. 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 used to make moment-to-moment evaluations of candidate actions

  25. Learning Agents • Desirable to build a system that “figures it out” • Generalizable • Compensates for absence of designer knowledge • Reusable • Learning by example isn’t easy to accomplish • What exercises do you do to learn? • What outcomes do you observe? • What inputs to your alter?

  26. Learning Agents • Performance Element • Selecting actions (this is the “agent” we’ve been discussing) • Problem Generator • Provides suggestions for new tasks to explore state space • Critic • Provides learning element with feedback about progress (are we doing good things or should we try something else?) • Learning Element • Making improvements (how is agent changed based on experience)

  27. A taxi driver • Performance Element • Knowledge of how to drive in traffic • Problem Generator • Proposes new routes to try to hopefully improve driving skills • Critic • Observes tips from customers and horn honking from other cars • Learning Element • Relates low tips to actions that may be the cause

  28. Review • Outlined families of AI problems and solutions • I consider AI to be a problem of searching • Countless things differentiate search problems • Number of percepts, number of actions, amount of a priori knowledge, predictability of world… • Textbook is divided into sections based on these differences

  29. Sections of book • Problem solving: Searching through predictable, discrete environments • Knowledge and Reasoning: Searching when a model of the world is known • a leads to b and b leads to c… so go to a to reach c • Planning: Refining search techniques to take advantage of domain knowledge • Uncertainty: Using statistics and observations to collect knowledge • Learning: Using observations to understand the way the world works and to act rationally within it

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