Review Philipp Koehn 30 April 2020 Philipp Koehn Artificial - - PowerPoint PPT Presentation

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Review Philipp Koehn 30 April 2020 Philipp Koehn Artificial Intelligence: Review 30 April 2020 Exam 1 Date: Thursday, May 13, all day (but should take at most 3 hours) Posted on Piazza, to be submitted to Gradescope Format

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Review

Philipp Koehn 30 April 2020

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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1

Exam

  • Date: Thursday, May 13, all day (but should take at most 3 hours)
  • Posted on Piazza, to be submitted to Gradescope
  • Format

– open book

  • Grading: homework is 60%, exam is 40%

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Lectures Covered By Exam

  • Artificial Intelligence in Context — not covered
  • Intelligent Agents, Heuristic Search,

and Game Playing – Intelligent Agents – Basic Search – Informed Search – Game Playing – Constraint Satisfaction

  • Logic and Knowledge Representation

– Logical Agents – First Order Logic – Inference in First-Order Logic – Knowledge Representation – Planning

  • Uncertainty

– Probabilistic Reasoning – Bayesian Networks – Markov Decision Processes – Decision Theory

  • Machine Learning

– Statistical Learning – Neural Networks – Reinforcement Learning – Deep Reinforcement Learning

  • Natural Language

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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intelligent agents

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Intelligent Agents

  • Types of environments: (in)accessible, (non-)deterministic, (non)-episodic
  • Types of agents: reflex, with memory, with goals, with learning, utility-based

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Basic Search

  • Problem solving agents
  • Analysis

– completeness – time complexity – space complexity – optimality

  • Basic search algorithms

– tree search – breadth / depth-first search – iterative deepening

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Informed Search

  • Best-first search
  • A∗ search
  • Heuristic algorithms

– hill-climbing – simulated annealing

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Game Playing

  • Types of games

– deterministic / probabilistic – (im)perfect information

  • Search over game tree

– minimax algorithm – α-β pruning – evaluation functions

  • Solvable games, but typically resource limits
  • Probabilistic games: pruning with bounds

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Constraint Satisfaction

  • Variables, domains, constraints
  • Backtracking search
  • Constraint propagation

– forward checking – arc consistency

  • Problems structure
  • Iterative algorithms

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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logic

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Logical Agents

  • Knowledge-based agents

– internal representations – incorporate new percepts – deduce hidden properties of the world

  • Logic

– formal language (syntax) – truth in real world (semantics) – entailment and inference

  • Algorithms

– forward chaining – backward chaining – resolution

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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First Order Logic

  • Adding

– variables – relations – functions – quanitifiers

  • Modeling natural language
  • Dynamic world: states and fluents
  • Situation calculus

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Inference in First-Order Logic

  • Reducing first-order inference to propositional inference
  • Unification
  • Generalized modus ponens
  • Forward and backward chaining
  • Logic programming (Prolog)
  • Resolution

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Knowledge Representation

  • Representation systems
  • Categories and objects

→ ontologies

  • Frames
  • Events and scripts
  • Practical examples

– Cyc – Semantic web

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Planning

  • Search vs. planning
  • STRIPS operators
  • Partial-order planning
  • The real world

– incomplete information – incorrect information – quantification problem

  • Conditional planning
  • Monitoring and replanning

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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uncertainty

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Probabilistic Reasoning

  • Uncertainty
  • Probability

– conditional probability – independence – Bayes rule

  • Inference
  • Independence and Bayes’ Rule

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Bayesian Networks

  • Bayesian Networks
  • Parameterized distributions
  • Exact inference

– inference by enumeration – variable elimination

  • Approximate inference

– rejection sampling – likelihood weighting – Markov chain Monte Carlo

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Markov Decision Processes

  • Temporal processes
  • Hidden Markov models
  • Inference

– filtering – smoothing – best sequence

  • Kalman filters
  • Dynamic Bayesian nets
  • Example: speech recognition

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Decision Theory

  • Rational preferences
  • Utilities
  • Decision networks
  • Value of information
  • Markov decision processes
  • Inference algorithms

– value iteration – policy iteration

  • Partially observable Markov decision processes (POMDP)

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Reinforcement Learning

  • Rewards, often delayed
  • Passive reinforcement learning

(compute utility of policy) – adaptive dynamic programming – temporal difference learning

  • Active Reinforcement Learning

– greedy agent – extended adaptive dynamic programming – Q-learning

  • Generalizations over the state space
  • Policy search

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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exam questions

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Sample Exam

  • Exam will assess

– understanding of core concepts – understanding of algorithms → ability to carry them out by hand

  • Exam will be similar to last years (posted on web site)

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Sample Question

Logical knowledge representation Which of the following are semantically and syntactically correct translations of ”Everyone’s zipcode within a state has the same first digit”?

  • 1. ∀ x, s, z1 [State(s) ∧ LivesIn(x, s) ∧ Zip(x) = z1] ⇒

[∀ y, z2 LivesIn(y, s) ∧ Zip(y) = z2 ⇒ Digit(1, z1) = Digit(1, z2)].

  • 2. ∀ x, s [State(s) ∧ LivesIn(x, s) ∧ ∃ z1 Zip(x) = z1] ⇒

[∀ y, z2 LivesIn(y, s) ∧ Zip(y) = z2 ∧ Digit(1, z1) = Digit(1, z2)].

  • 3. ∀ x, y, s State(s) ∧ LivesIn(x, s) ∧ LivesIn(y, s) ⇒ Digit(1, Zip(x) =Zip(y)).
  • 4. ∀ x, y, s State(s) ∧ LivesIn(x, s) ∧ LivesIn(y, s) ⇒ Digit(1, Zip(x)) = Digit(1, Zip(y)).

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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Sample Question

Game playing Consider the game of 2 × 2 tictactoe.

  • 1. Draw the full game tree down to depth 2. You need not show nodes that are

rotations or reflections of siblings already shown.

  • 2. Circle any node that would not be evaluated by alpha–beta during a left-to-right

exploration of your tree.

Philipp Koehn Artificial Intelligence: Review 30 April 2020

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questions?

Philipp Koehn Artificial Intelligence: Review 30 April 2020