Articles, notes and slides of paper presentations will be available here. A partial list is available. I will try to have all papers available on-line.

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A background task

• (r) Hirschman, Light, Breck and Burger: Deep Read: A Reading Comprehension System

Foundations

• Introduction: logical representations, probabilistic representations; computational issues.
• (r) Kirsh: Foundations of AI: the big issues
• (r) Nilsson: Logic and artificial intelligence
• (r) Birnbaum: Rigor mortis: a response to Nilsson's "Logic and artificial intelligence"
• (r) Roth: Learning in Order to Reason: The Approach
• Guest Presentation (9/13) :
           (r) Punyakanok, Roth: The Use of Classifiers in Sequential Inference . Presented by Vasin Punyakanok
• Presentation #1 (9/18):
           (p) Brooks: Intelligence without representation.    Presentation by Rodrigo de salvo Braz.

Reasoning paradigms (propositional)

• Presentation #2/3 (9/20,9/25):
           (p) Khardon, Roth: Reasoning with Models.  
  Presentation by: Dan Roth
           (r) Levesque: Is Reasoning too Hard?.  

Learning to Reason

• Presentation #4 (10/2, 10/4):
           (p) Khardon, Roth: Learning to Reason.  
  Presentation by: Kiran Lakkaraju
• Presentation #5 (10/4,10/9):
           (p) Valiant: Robust Logic.  
  Presentation by: Jason O'Kane
• Presentation #6 (10/11):
           (p) Reiter: Nonmonotonic Reasoning.  
  Presentation by: Top Changwatchai
           (r)Neufeld: Defaults and probabilities; extension and coherence.  
           (r) Roth: Learning to Reason: The Non-Monotonic Case.  
           (r) Khardon, Roth: Defaults and Relevance in Model Based Reasoning.  

Expressive Representations

• Presentation #7 (10/16):
           (p) Levesque, Brachman: A fundamental tradeoff in knowledge representation and reasoning.  
  Presentation by: Chris Neihengen
           (r) Woods: What's in a link.  
• Discussion (10/23)
           (r) Roth: Learning to Reason: The Non-Monotonic Case.  
• Presentation #8 (10/23):
           (p) McAllester, Givan: Natural Language Syntax and First Order Inference.  
  Presentation by: Ramya Nagarajan

  Expressive Representations; Natural Language

• Presentation #9 (10/25):
           (p) Charniak: Jack and Janet in search of a theory of knowledge.  
  Presentation by: Arun Bhalla
• Presentation #10 (10/30):
           (p) Schubert, Pelletier: From English to Logic: Context Free Computation of Conventional Logical Translations.  
  Presentation by: Melissa Poole
• Presentation #11 (11/1):
           (p) Moore: Problems in Logical Forms.  
  Presentation by: Scott Yih
           (r) Moore: In defense of Logic.  
• Presentation #12 (11/6):
           (p) Hobbs, Stickel: Interpretation as Abduction.  
  Presentation by: Chad Cumby

  Probabilistic Representations

• Presentation #13 (11/6), Tuesday afternoon, 5:15,, 2261 DCL.
           (p) Pearl: Fusion, Propagation and Structuring in Belief Networks.  
  Presentation by: Adam Laud
  (r) Roth: On the hardness of approximate reasoning.  
• Presentation #14 (11/8):
           (p) Darwiche: A Differential Approach to Inference in Bayesian Networks.  
  Presentation by: Xin Li
• Presentation #15 (11/27):
           (p) Ngo, Haddawy Answering queries from context sensitive probabilistic knowledge bases.  
  Presentation by: Stephen Kloder
           (r) Ngo, Haddawy Probabilistic Logic Programming and Bayesian Networks.  
• Presentation #16 (11/27), Tuesday afternoon, 5:15, 2261 DCL:
           (p) Kersting, De Raedt Bayesian Logic Programs.  
  Presentation by: Howard Sun
• Presentation #17: (11/29)
           (p) Koller, Pfeffer: Probabilistic frame-based systems..  
  Presentation by: Ira Cohen
           (r) Jaeger: Relational Bayesian networks..  
• Presentation #18 (11/29): Thursday afternoon, 5pm, 2261 DCL.
           (p) Getoor, Friedman, Koller, Pfeffer: Learning Probabilistic Relational Models .  
  Presentation by: Dav Zimak