• Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents. Stan Franklin and Art Graesser
  
• Forum for Artificial Intelligence: AI and Life in 2030 (to view, log in using your CS account)
• Information on pair programming; A video
• Rodney Brooks' dated predctions regarding the future of AI
• The Good Systems project at UT Austin

• Sven Koenig's site on LPA* and D* lite
• Andrew Ng's A* search notes
• A student found this A* tutorial useful
• Related research papers
  • Online Learning of Search Heuristics
  • Bidirectional Search That Is Guaranteed to Meet in the Middle
  • AlphaGo's Nature paper
  • AlphaGo Zero

• 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.
• Related research papers
  • A constraint-based method for solving sequential manipulation planning problems
  • Randomized algorithm for informative path planning with budget constraints

• Guide on pruning
• 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
• A paper showing PacMan is NP-hard
• A slashdot discussion on it.
• A video about loss aversion and risk assessment by people
• Related research papers
  • Evolutionary Many-Objective Optimization Based on Adversarial Decomposition
  • Playing Multi-Action Adversarial Games: Online Evolutionary Planning versus Tree Search
• 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.

• Side-Stepping of the Triple Pendulum on a Cart
• Theory of Generalization: How an infinite model can learn from a finite sample
• Related research papers
  • Learning a reward function: Inverse Reinforcement Learning
  • Graph-based Cross Entropy method for solving multi-robot decentralized POMDPs
  • Dynamically Constructed (PO) MDPs for Adaptive Robot Planning

• The Berkeley course's past exams (with solutions)

• Deep RL Bootcamp lectures
• Review of probability theory
• Common probability distribution
• Related research papers
  • RL for POMDP
  • Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning
  • Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates

• A Bayes Net tool that you can play with
• Review of probability theory
• Common probability distribution
• Related research papers
  • Bayesian learning for safe high-speed navigation in unknown environments
  • Dynamic Bayesian Network for semantic place classification in mobile robotics

• Markov Chain vs. HMM

(Hidden) Markov Models, Particle Filters

• [Hidden Markov Models]
• [Particle Filters]

• The readings on particle filters from Peter Stone's 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.
• Related research papers
  • Vehicle localization using particle filter
  • 3D Audio-Visual Speaker Tracking with an Adaptive Particle Filter
  • Stochastic Simulation Algorithms for Dynamic Probabilistic Networks

Midterm II, CoRL week (no class)

• The Berkeley course's past exams (with solutions)

Decision Networks, Naive Bayes

• [Decision Networks]
• [Naive Bayes]

• Naive Bayes in Python and R
• An explanation of the connection between the number of bits required to encode a hypothesis and minimum description length (MDL)
• Clustering implementation
• Comparing Python Clustering Algorithms
• Clustering Vs. Classification on Keyword Research
• How Much Training Data is Required for Machine Learning?
• Related research papers
  • Multi-face Detection System Design based on Naive Bayes Classifier
  • Being Bayesian about Network Structure: A Bayesian Approach to Structure Discovery in Bayesian Networks

Perceptrons, Clustering, Deep Learning

• [Perceptrons, Clustering]
• [Deep Learning]

• From Perceptrons to Deep Networks
• Helpful Neural Net Notes: Backpropagation; Function approximation; CNN for Visual Recognition
• Goodfellow, Bengio, and Courville's Deep Learning book
• Open source deep learning software: PyTorch, TensorFlow
• Explanation on why ReLu is better
• Geoffrey Hinton talk: What is wrong with CNN
• Geoffrey Hinton's recent theory to replace deep learning
• UC Irvine Machine Learning (dataset) Repository
• Deep learning vs. kernel acoustic models for speech recognition
• Related research papers
  • Deep Learning
  • Generative Adversarial Networks
  • Generating Sequences With Recurrent Neural Networks
  • Designing Neural Network Architectures using Reinforcement Learning
  • Attention Is All You Need

Thanksgiving (no class)

Guest Lecture, Conclusion

• [Conclusions]

• 2022 AI Index Report and a summary on the report
• The killer drones video from Stuart Russell
• Why the Future Doesn't Need Us by Bill Joy - Wired, 2000
• Why AI is Harder Than We Think
• The Essence of Soccer: Can Robots Play Too? Peter Stone, Michael Quinlan, and Todd Hester.
• A writing on the singularity: 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?