CS343H: Honors Artificial Intelligence</a> -- Spring 2015: Resources Page

Resources for Honors Artificial Intelligence (cs343H)

Weeks 0 and 1: Introduction

  • The slides presented in class: PDF
  • Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents ( Alternative link).
    Stan Franklin and Art Graesser
  • Talk on the AI 100 report in Finland
  • Forum for Artificial Intelligence: AI and Life in 2030
  • Information on pair programming; A video
  • Related lecture slides (UC Berkeley CS188): Introduction
  • Related lecture videos (UC Berkeley CS188): Introduction to AI

    • Week 2: Search:

  • The slides presented in class: Tuesday ; Thursday
  • Sven Koenig's site on LPA* and D* lite
  • Andrew Ng's A* search notes
  • A student found this A* tutorial useful.
  • Online Learning of Search Heuristics
  • Bidirectional Search That Is Guaranteed to Meet in the Middle
  • Some research papers related to this week's material
  • AlphaGo's Nature paper
  • AlphaGo Zero
  • Related lecture slides (UC Berkeley CS188): Uninformed Search, Informed Search
  • Related lecture videos (UC Berkeley CS188): Agents and Search, A* Search and Heuristics
  • SBS videos: Uninformed Search, A* Search and Heuristics
  • Handout-I, Solution-I, Handout-II, Solution-II

    • Week 3: Beyond Classical Search:

  • The slides presented in class: Tuesday; Thursday
  • Continuous state space learning on Aibos: walking; ball control
  • GA applications
  • From a former student: Genetic Mona Lisa
  • From a former student: robot evolution
  • Path search in continuous environments using RRT's.
  • Some research papers related to this week's material
  • A constraint-based method for solving sequential manipulation planning problems
  • Randomized algorithm for informative path planning with budget constraints
  • Related lecture slides (UC Berkeley CS188): CSPs I, II
  • Related lecture videos (UC Berkeley CS188): CSPs I, II
  • Handout, Solution

    • Week 4: Adversarial Search

  • The slides presented in class: Tuesday; Thursday; Optimizer's curse
  • Some adversarial reasoning in the 2010 Super Bowl.
  • And some more from the 2012 Super Bowl.
  • The University of Alberta GAMES group.
  • The Berkeley GamesCrafters group.
  • The Stanford General Game Playing group.
  • A paper showing PacMan is NP-hard. A slashdot discussion on it.
  • A video about loss aversion and risk assessment by people
  • Some research papers related to this week's material
  • Evolutionary Many-Objective Optimization Based on Adversarial Decomposition
  • Playing Multi-Action Adversarial Games: Online Evolutionary Planning versus Tree Search
  • Related lecture slides (UC Berkeley CS188): Adversarial Search, Expectimax Search and Utilities
  • Related lecture videos (UC Berkeley CS188): Adversarial Search, Uncertainty and Utilities
  • Starting at minute 10 of this video is a keynote by Mike Bowling on game playing AI, featuring their recent computer poker victory against people.
  • SBS videos: Alpha-Beta
  • Handout, Solution

    • Week 5: Markov Decision Processes

  • The slides presented in class: Context; Tuesday; Thursday
  • Learn a reward function: Inverse Reinforcement Learning
  • Some research papers related to this week's material
  • Graph-based Cross Entropy method for solving multi-robot decentralized POMDPs
  • Dynamically Constructed (PO) MDPs for Adaptive Robot Planning
  • Related lecture slides (UC Berkeley CS188): MDP I, II
  • Related lecture videos (UC Berkeley CS188): MDP I, II
  • Handout, Solution

    • Week 6: Reinforcement Learning

  • The slides presented in class: Tuesday; Thursday
  • Side-Stepping of the Triple Pendulum on a Cart
  • RL for POMDP
  • Theory of Generalization: How an infinite model can learn from a finite sample
  • Deep RL Bootcamp lectures.
  • Some research papers related to this week's material
  • Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning
  • Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates
  • Related lecture slides (UC Berkeley CS188): RL I, II
  • Related lecture videos (UC Berkeley CS188): RL I, II
  • Handout, Solution

    • Week 7: Bayes' Nets Representation and Inference

  • The slides presented in class: Tuesday-Probability; Tuesday-Representation; Thursday-Independence; Thursday-Inference
  • A Bayes' Net tool that you can play with.
  • Review of probability theory
  • Common probability distribution
  • Some research papers related to this week's material
  • Bayesian learning for safe high-speed navigation in unknown environments
  • Dynamic Bayesian Network for semantic place classification in mobile robotics
  • Related lecture slides (UC Berkeley CS188): Representation, Probability, Independence, Inference, Sampling
  • Related lecture videos (UC Berkeley CS188): Representation, Probability, Independence, Inference, Sampling
  • SBS videos: Independence, Sampling, Sampling II, Elimination of One Variable, Variable Elimination
  • Handout-I, Solution-I, Handout-II, Solution-II

    • Week 8: Midterm

  • The slides presented in class: Tuesday-Sampling
  • The Berkeley course's past exams (with solutions).

    • Week 9: (Hidden) Markov Models, Particle Filters, and VOI

  • The slides presented in class: Tuesday-Context; Tuesday-HMM; Tuesday-Particle Filter
    Thursday-DecisionsVPI; Thursday-Localization
  • The readings on particle filters from my Fall 2015 graduate class on autonomous robots. Some are from the book "Probabilistic Robotics" by Thrun, Burgard, and Fox.
  • The full list of resources from that class, including the following:
  • Slides from the book: (ppt).
  • Some videos from the textbook authors.
  • Stochastic Simulation Algorithms for Dynamic Probabilistic Networks
  • Markov Chain vs. HMM
  • Some research papers related to this week's material
  • Vehicle localization using particle filter
  • 3D Audio-Visual Speaker Tracking with an Adaptive Particle Filter
  • A good explanation of smoothing/filering
  • Related lecture slides (UC Berkeley CS188): Markov Models, HMM, Particle Filters and Applications of HMMs
  • Related lecture videos (UC Berkeley CS188): Markov Models, Applications of HMMs, HMMs Filtering
  • Handouts: HMM; HMM-solution; VPI; VPI-solution; Decision Networks; Decision Networks-solution

    • Week 10: Naive Bayes and Perceptrons

  • The slides presented in class: Tuesday-Context; Tuesday; Thursday
  • Naive Bayes in Python and R
  • From Perceptrons to Deep Networks
  • An explanation of the connection between the number of bits required to encode a hypothesis and minimum description length (MDL)
  • Some research papers related to this week's material
  • Multi-face Detection System Design based on Naive Bayes Classifier
  • Extreme Learning Machine for Multilayer Perceptron
  • Being Bayesian about Network Structure: A Bayesian Approach to Structure Discovery in Bayesian Networks
  • Related lecture slides (UC Berkeley CS188): Naive Bayes, Perceptron
  • Related lecture videos (UC Berkeley CS188): Naive Bayes, Perceptron
  • SBS videos: Maximum Likelihood Examples, Laplace Smoothing, Perceptrons
  • In-class exercises; solutions

    • Week 11: Deep Learning

  • The slides presented in class: policy gradient; optimizing NN structure for RL.
  • Helpful Neural Net Notes: Backpropagation; Function approximation; CNN for Visual Recognition
  • Goodfellow, Bengio, and Courville's Deep Learning book.
  • Why deep learning is suddenly changing your life
  • Explanation on why ReLu is better
  • Geoffrey Hinton talk: What is wrong with CNN
  • Geoffrey Hinton's recent theory to replace deep learning
  • The UC Irvine Machine Learning Repository
  • Open source classification/regression software: WEKA
  • A new effort for comparing ML algorithms: ML comp
  • Some research papers related to this week's material
  • Deep Learning
  • A Neural Algorithm of Artistic Style
  • Generating Sequences With Recurrent Neural Networks
  • Designing Neural Network Architectures using Reinforcement Learning
  • A slide deck on TensorFlow
  • Related lecture slides (UC Berkeley CS188): Deep Learning I, Deep Learning II
  • In-class exercises: computing gradients; solutions; NN representation; solutions;

    • Week 12: SVMs, Kernels, and Clustering

  • The slides presented in class: Tuesday-Context; Tuesday;
  • SVM-Kernels python implementation
  • Clustering implementation
  • Comparing Python Clustering Algorithms
  • Deep learning vs. kernel acoustic models for speech recognition
  • Clustering Vs. Classification on Keyword Research
  • How Much Training Data is Required for Machine Learning?
  • SVM incremental learning, Adaptation and Optimization
  • Some research papers related to this week's material
  • Deep Learning using Linear Support Vector Machines
  • General Tensor Spectral Co-clustering for Higher-Order Data
  • Related lecture slides (UC Berkeley CS188): Kernels and Clustering
  • Related lecture videos (UC Berkeley CS188): Kernels and Clustering
  • Some practice problems and solutions (see especially the first problem).

    • Week 13: Classical Planning

  • The slides presented in class: Tuesday-Context; Tuesday; Tuesday-Prodigy; Thursday
  • A USC planning class, including some graphplan slides
  • Planning today: 27th International Conference on Automated Planning and Scheduling.
  • Planning competitions
  • Answer Set Programming
  • Two papers I published on domain-independent planning heuristics during my first year of grad school. A shorter conference paper and a longer journal article.
  • Course: Planning, Execution, and Learning
  • Practice, Solution

    • Week 14: Philosophical Foundations

  • The slides presented in class: Thursday
  • The killer drones video from Stuart Russell.
  • Why the Future Doesn't Need Us by Bill Joy - Wired, 2000. (pdf version)
  • The Essence of Soccer: Can Robots Play Too?
    Peter Stone, Michael Quinlan, and Todd Hester.
    Appeared in a book on philosophy and soccer.
  • Some writings on the singularity: a resesarch institute, and Ray Kurzweil's page.
  • Jordan Pollack's GOLEM project: evolving physical robots.
  • The Lifeboat Foundation is dedicated to assessing and protecting against threats to humanity.
  • The Asilomar conference on recombinant DNA.
  • Ben Kuipers' personal stance on accepting military research funding.
  • 3 principles for creating safer AI
  • Can we build AI without losing control over it?
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