CS 323: Automated Reasoning
Stanford / Computer Science / Spring 2014-2015
Announcements will be posted on Piazza.
We've moved to Building 380-380X.
- Lectures: Tue/Thu 2:15-3:30pm, Building 380-380X , 3-4 units
- Office Hours: Mondays 11 am, Gates 158 or by appointment.
Instructor: Stefano Ermon
- Course Description:
Intelligent computer agents must reason about complex, uncertain, and dynamic environments. This course is a graduate level introduction to automated reasoning techniques and their applications, covering logical and probabilistic approaches. Topics include: logical and probabilistic foundations, backtracking strategies and algorithms behind modern SAT solvers, stochastic local search and Markov Chain Monte Carlo algorithms, classes of reasoning tasks and reductions, and applications.
The goal of the course is to expose students to general modeling languages such as SAT and factor graphs, which have numerous applications in AI and beyond. Students will also learn about different types of reasoning tasks and the main families of inference and search algorithms, understanding their strengths, weaknesses and exploring relationships between them.
The course will cover (tenatively):
Logical Reasoning: logic, satisfiability, transformations; SAT solvers Practical Applications of SAT/SMT, SAT competitions; Tractable fragments, 2-SAT, random walks; Random 3SAT, Phase transitions, connections with statistical physics (Survey Propagation); (Weighted) MAX-SATProbabilistic Reasoning: Constraint networks and factor graphs; Probabilistic inference, counting problems and complexity classes; Reductions: MPE to weighted maxSAT, probabilistic inference to weighted model counting Sampling and counting self-reducibility; MCMC sampling: Markov Chain analysis, connections with stochastic local search (Simulated Annealing, etc); Sampling and Counting with an NP-Oracle (SAT solver); Planning and bounded model checking with SAT
Prerequisites: Students are expected to have background in basic probability theory, statistics, programming, algorithm design and analysis.
- Evaluation: The main component of this course will be a research project. This project can be a new application of one of the techniques presented or theoretically-oriented. This will involve an initial project proposal and a final report. The final report should resemble a conference paper, and will be evaluated on the basis of soundness, significance, novelty, and clarity. Students might also be asked to present a paper (depending on class size).
Required Textbook: There is no required textbook. Reading materials will be provided.
Modeling and Reasoning with Bayesian networks by Adnan Darwiche.
Machine Learning: a Probabilistic Perspective by Kevin P. Murphy.
- Survey Propagation: An Algorithm for Satisfiability. A. Braunstein1, M. Mezard and R. Zecchina
- Random Walks That Find Perfect Objects and the Lovasz Local Lemma. Dimitris Achlioptas, Fotis Iliopoulos
- Algorithm Runtime Prediction: Methods and Evaluation. F. Hutter, L. Xu, H. Hoos, K. Leyton-Brown
- Ten Challenges Redux: Recent Progress in Propositional Reasoning and Search. Henry Kautz, Bart Selman
- Markov Logic: A Unifying Framework for Statistical Relational Learning. Pedro Domingos, M. Richardson
- Church: a language for generative models. Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Keith Bonawitz, and Joshua B. Tenenbaum
- Sum-Product Networks: A New Deep Architecture. Hoifung Poon and Pedro Domingos
- Probability Distributions over Structured Spaces. Arthur Choi and Guy Van den Broeck and Adnan Darwiche
- Reducing the Sampling Complexity of Topic Models. Aaron Q Li; Amr Ahmed; Sujith Ravi.; Alexander J Smola
- Herded Gibbs Sampling. Luke Bornn, Yutian Chen, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling
- Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. A. Korattikara, Y. Chen and M. Welling
- Variational MCMC. Nando de Freitas, Pedro Hojen-Sorensen, Michael I. Jordan, Stuart Russell
- Adaptive MCMC with Bayesian Optimization. Nimalan Mahendran, Ziyu Wang, Firas Hamze, Nando de Freitas
- Projecting Markov Random Fields for Fast Mixing. Xianghang Liu, Justin Domke
- Probabilistic Theorem Proving. Vibhav Gogate and Pedro Domingos
- Approximate Inference by Compilation to Arithmetic Circuits. Daniel Lowd Pedro Domingos
- On Probabilistic Inference by Weighted Model Counting. Mark Chavira and Adnan Darwiche
- First-order probabilistic inference. David Poole
- A* Sampling. Chris J. Maddison,Daniel Tarlow, Tom Minka
- Belief propagation for structured decision making. Liu, Ihler