artificial intelligencejeffclune.com/courses/media/courses/2016-fall-ai/lectures/l03-ai-20… ·...
TRANSCRIPT
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Jeff Clune
Assistant ProfessorEvolving Artificial Intelligence Laboratory
Artificial Intelligence
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Make Friends
• 3 minutes with a new person
• Funny story involving fire, climbing trees, or poor-ordering
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Great diversity!
Thanks Paul
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Guest Lecturer
• Thurs
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Logical Deduction AI Approach
• Based on formal logic
• Example • Toasters are smaller than car trunks • Things that are smaller than something can fit inside that
something. • ergo: Toasters can fit inside car trunks
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Logical Deduction AI Approach
• Based on formal logic
• Example • Toasters are smaller than cows • Things that are smaller than something can fit inside that
something. • ergo: Toasters can fit inside cows • Maybe true...but not quite right
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Logical Deduction AI Approach
• Called Expert Systems
• Problems • Too hard to hand enter all facts
- oranges are smaller than watermelons - toasters don’t usually go inside of cows, etc. etc. etc. ... - Cyc
- Started 1984 - Attempts to gather all commonsense facts - Currently has over one-million hand-entered facts! (e.g. “plants die eventually”) - Can do some things: with “trees are plants”, and “plants die eventually”,“do trees die?”
- It answers “yes”
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Logical Deduction AI Approach
• Called Expert Systems
• Problems • Too hard to hand enter all facts • informal knowledge is hard to codify, especially when fuzzy
- e.g. the fact that toasters don’t go inside cows
• exponential computational power required as fact database increases - even a few hundred rules becomes untenable, unless
- you give the system tips about which reasoning steps to try first - (same problem in all AI fields....to be fair)
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Logical Deduction AI Approach
• Called Expert Systems
• We’re not going to cover these systems • If interested, read Section III of the textbook
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History of AI
• There is a great history of AI in RN1 • Including the AI winter and our current AI spring (summer?!) • We won’t cover it in lecture, but you should read it
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Modern Era
• Learning from big data sets • often, more/better data is more important than specific choice of
algorithm • information is learned from data sets, not hand-entered
• Dealing with uncertainty • Statistics/probability (often Bayesian)
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Agents
from: http://www.cs.cornell.edu/courses/cs4700/2011fa/lectures/02_ProblemSolvingAsSearch.pdf
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Agents
• Translate inputs (percepts) to outputs (actions)
• We want them to perform well • Need a performance measure
• What is a good performance measure for a vacuum cleaning robot?
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Agents
• Performance measures: notoriously difficult to design • e.g. vacuum-robot:
- measure: amount of dirt vacuumed up - result: vacuums, dumps dirt on ground, repeat! - better measure: percent of floor clean summed over all time steps
- possibly with penalties for energy used, noise, dust, etc.
• General rule: reward what you want the agent to do, not how you think it should accomplish that goal • problem: no stepping-stones....
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Agents
• Maximize expected payoff • from built-in knowledge (instincts, hard-wired knowledge...) • from learned knowledge (percept sequence) • including gathering info (active learning)
• “outcomes don’t justify decisions” • not dying doesn’t mean it was smart to play Russian Roulette
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The Gambler
Every gambler knows that the secret to survivin'
Is knowin' what to throw away and knowing what to keep
'Cause every hand's a winner and every hand's a loser
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The Gambler
Every gambler knows that the secret to survivin'
Is knowin' what to throw away and knowing what to keep
'Cause every hand's a winner and every hand's a loser
And the best that you can hope for is to die in your sleep
to maximize expected reap!
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Environments/Problems/Domains
• Fully observable vs. partially observable • Fully observable: agents sensors can see all relevant info
- no need to keep internal state - examples of fully observable problems?
• Partially observable: - what are different reasons why world may not be observable? - examples of partially observable problems?
- Is chess fully observable? - What about the castle rule? “Can only occur if neither piece is moved…”
- What if I mark each piece as moved? - What if I list each move that has occurred?
- Would that make every environment fully observable?
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Environments/Problems/Domains
• Single agent vs. multi-agent • examples of single? • multi?
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Environments/Problems/Domains
• Single agent vs. multi-agent • examples of single? • multi?
- competitive - cooperative
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Environments/Problems/Domains
• Deterministic vs. stochastic
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Environments/Problems/Domains
• Episodic vs. sequential • Episodic: sense & act, repeat, but episode(t) is independent of
episode (t-1) - examples?
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Environments/Problems/Domains
• Episodic vs. sequential • Episodic: sense & act, repeat, but episode(t) is independent of
episode (t-1) - examples?
- classification tasks
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Environments/Problems/Domains
• Episodic vs. sequential • Episodic: sense & act, repeat, but episode(t) is independent of
episode (t-1) - examples?
- classification tasks
• Sequential - current action can influence all future actions - which is harder?
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Environments/Problems/Domains
• Static vs. Dynamic • Dynamic: environment can change while agent is deliberating
- If you choose not to decide (or think too long), you still have made a choice!
• Examples of both?
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Environments/Problems/Domains
• Discrete vs. Continuous • states, time, percepts, actions
- separate time steps - integers vs. floats
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Environments/Problems/Domains
• Known vs. Unknown • Known: agent knows the laws of physics ahead of time
- or the rules of the game...how the system works
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Environments/Problems/Domains
• Hardest • Partially observable • multiagent • stochastic • sequential • dynamic • continuous • unknown
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Environments/Problems/Domains
• Hardest • Partially observable or fully? • single or multi agent? • deterministic or stochastic? • sequential or episodic? • dynamic or static? • discrete or continuous? • known or unknown?
What is homework one?
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Possible Agents: Lookup Table
• If percepts are:
• Then
• aka “State Machine”
o
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Possible Agents: Lookup Table
• State space: all of the possible agent situations
• Chess ~ 10150
• Any robot with camera: ~10250,000,000,000
• video at 27 mb/second - 30 frames per second, 640x480 pixels, 24 bits of color info
• Number of atoms in observable universe: ~1050
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Possible agents: Lookup Table
• Such large lookup tables are not going to work • can’t store them, learn them, etc.
• Key challenge for AI: • make small programs perform as well as optimal/good, vast,
lookup tables
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Possible Agents: Simple Reflex Agent
• Other end of the spectrum: • open-loop controller
- steadfastly refuses to alter its plan despite what’s happening in reality - (i.e. ignores/has no percepts)
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• Simple reflex agent • responds to current precepts • very limited
- why? can you think of examples where you need more than the current percepts?
Possible Agents: Simple Reflex Agent
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• Simple reflex agent • responds to current precepts • very limited
- only works optimally if environment is fully observable
Possible Agents: Simple Reflex Agent
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• Best way to handle partial observability? • Remember important things that you can’t perceive now • I.e. build a model of the world
- internal states that represent external states
Possible Agents: Model-based Reflex Agent
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• Best way to handle partial observability? • Remember important things that you can’t perceive now • I.e. build a model of the world
- internal states that represent external states
• To build a model • must guess how the world changes when you can’t see it • must guess how the agent’s actions change the world
Possible Agents: Model-based Reflex Agent
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Goal-based Agents
• Have goal; can figure out autonomously how to accomplish it
• The more abstract the goal can be specified, the better • Imagine hand coding every decision a robot makes to deliver a
pizza vs. saying “deliver this pizza to 300 Water St.”
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Utility-based Agents
• Have many goals • deliver pizza • fast • don’t crash • etc.
• Best way to combine these different factors into one score?
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Utility-based Agents
• Have many goals • deliver pizza • fast • don’t crash • etc.
• Given uncertainty (stochasticity): • Maximize expected utility (aka expected value)
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Pen and Paper
• Start to bring them please • Lots of in-class problems
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Utility-based Agents
• Have many goals • deliver pizza • fast • don’t crash • etc.
• Given uncertainty (stochasticity): • Maximize expected utility (aka expected value)
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Learning Agents
• Instead of programming each decision, they learn • there is usually an implicit or explicit utility function
• To key parts of an agent • Select actions to perform • Learn from what happened
- requires knowing what’s good/bad - either a reward signal - or a internal “critic”
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Exploration vs. Exploitation
• Two-armed bandit problem • Let’s play
- Arm 1: payoff = ??? - Arm 2: payoff = ??? - Goal: a policy that maximized expected
value over N pulls - Problem version 1: payoffs don’t change
- your policy? - Problem version 2: payoffs change
- your policy?
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Exploration vs. Exploitation
• Exploitation: Doing the best you can given your current knowledge
• Exploration: Trying things that are less-optimal (according to your current model) in order to improve the model • examples from real life?
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Abstraction
• Including only what’s relevant • e.g. in chess, the color of the pieces is not important
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Probability
• When to add vs. multiply? • Probability of rolling:
- P(1 ^ 2 ^ 3 ^4 ^ 5) in order for 5 rolls of one die - P(1 v 2 v 3 v 4 v 5) in 1 roll of one die
On your own
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Probability
• When to add vs. multiply?
• Intuition: • X AND Y = less likely
- stranger is tall AND fat AND bald AND funny
• X OR Y = more likely - stranger is tall OR fat OR bald OR funny
• Probability of rolling: - 1 ^ 2 ^ 3 ^4 ^ 5? - 1 v 2 v 3 v 4 v 5?
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Probability
• When to add vs. multiply?
• Intuition: • X AND Y = less likely
- stranger is tall AND fat AND bald AND funny
• X OR Y = more likely - stranger is tall OR fat OR bald OR funny
• Probability of rolling: - 1 ^ 2 ^ 3 ^4 ^ 5? - 1 v 2 v 3 v 4 v 5?