ISAIM 2008: Fort Lauderdale, Florida, USA

Papers accepted to the main technical program

• Andreas Alexander Albrecht, Peter C. R. Lane, Kathleen Steinhöfel:
  Estimating the Number of Local Maxima for k-SAT Instances.
  
• Carlos Ansótegui, Ramón Béjar, Cèsar Fernández, Carles Mateu:
  Hard SAT and CSP instances with Expander Graphs.
  
• Vincent Aravantinos, Ricardo Caferra, Nicolas Peltier:
  More Flexible Term Schematisations via Extended Primal Grammars.
  
• Manuel Atencia, W. Marco Schorlemmer:
  Formalising Interaction-Situated Semantic Alignment: The Communication Product.
  
• Michael Biggs, Ali Ghodsi, Dana F. Wilkinson, Michael H. Bowling:
  Scalable Action Respecting Embedding.
  
• Branislav Kveton, Jia Yuan Yu, Georgios Theocharous, Shie Mannor:
  A Lazy Approach to Online Learning with Constraints.
  
• Emma Brunskill, Leslie Pack Kaelbling, Tomás Lozano-Pérez, Nicholas Roy:
  Continuous-State POMDPs with Hybrid Dynamics.
  
• Douglas Cenzer, Jeffrey B. Remmel:
  A Connection between Cantor-Bendixson Derivatives and the Well-Founded Semantics of Logic Programs.
  
• Fabio Cuzzolin:
  An Interpretation of Consistent Belief Functions in Terms of Simplicial Complexes.
  
• Fabio Cuzzolin:
  Boolean and Matroidal Independence in Uncertainty Theory.
  
• Bistra N. Dilkina, Carla P. Gomes, Ashish Sabharwal:
  Tradeoffs in Backdoors: Inconsistency Detection, Dynamic Simplification, and Preprocessing.
  
• Pinar Donmez, Jaime G. Carbonell:
  Paired Sampling in Density-Sensitive Active Learning.
  
• Finale Doshi, Joelle Pineau, Nicholas Roy:
  Reinforcement Learning with Limited Reinforcement: Using Bayes Risk for Active Learning in POMDPs.
  
• P. Alex Dow, Richard E. Korf:
  Best-First Search with Maximum Edge Cost Functions.
  
• Yu Fan, Christian R. Shelton:
  Sampling for Approximate Inference in Continuous Time Bayesian Networks.
  
• Alireza Farhangfar, Russell Greiner, Martin Zinkevich:
  A Fast Way to Produce Optimal Fixed-Depth Decision Trees.
  
• Andrew Guillory, Jeff Bilmes:
  Practical Methods for Exploiting Bounds on Change in the Margin.
  
• Erik Halvorson, Ronald Parr:
  Planning Aims for a Network of Horizontal and Overhead Sensors.
  
• Jingrui He, Jaime G. Carbonell:
  Rare Class Discovery Based on Active Learning.
  
• Hengshuai Yao, Zhi-Qiang Liu:
  Minimal Residual Approaches for Policy Evaluation in Large Sparse Markov Chains.
  
• Nathalie Japkowicz, Pritika Sanghi, Peter E. Tischer:
  Classifier Utility Visualization by Distance-Preserving Projection of High Dimensional Performance D.
  
• Kin Fai Kan, Christian R. Shelton:
  Solving Structured Continuous-Time Markov Decision Processes.
  
• Hoyt A. Koepke, Bertrand S. Clarke:
  A Bayesian Approach to Cluster Validation.
  
• Lukas Kroc, Bart Selman, Ashish Sabharwal:
  Leveraging Belief Propagation, Backtrack Search, and Statistics for Model Counting.
  
• Marina Langlois, Dhruv Mubayi, Robert H. Sloan, György Turán:
  Combinatorial problems for Horn clauses.
  
• Lihong Li, Michael L. Littman:
  Efficient Value-Function Approximation via Online Linear Regression.
  
• Dennis Perez, Prashant Doshi:
  Approximate Solutions of Interactive POMDPs Using Point Based Value Iteration.
  
• Franz Pernkopf, Jeff Bilmes:
  Order-based Discriminative Structure Learning for Bayesian Network Classifiers.
  
• Marek Petrik, Shlomo Zilberstein:
  A Successive Approximation Algorithm for Coordination Problems.
  
• Gregory Provan:
  Approximation Techniques for Space-Efficient Compilation in Abductive Inference.
  
• Emmanuel Rachelson, Frédérick Garcia, Patrick Fabiani:
  Extending the Bellman equation for MDPs to continuous actions and cont. time in the discounted case.
  
• Matthew R. Rudary, Satinder P. Singh:
  Predictive Linear-Gaussian Models of Dynamical Systems with Vector-Valued Actions and Observations.
  
• Emad Saad:
  On the Relationship between Hybrid Probabilistic Logic Programs and Stochastic Satisfiability.
  
• T. K. Satish Kumar:
  Lifting Techniques for Weighted Constraint Satisfaction Problems.
  
• Sajjad Ahmed Siddiqi, Jinbo Huang:
  Probabilistic Sequential Diagnosis by Compilation.
  
• Tomás Singliar, Milos Hauskrecht:
  Approximation Strategies for Routing in Stochastic Dynamic Networks.
  
• Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein:
  Using Abstraction for Generalized Planning.
  
• Erik Talvitie, Britton Wolfe, Satinder Singh:
  Building Incomplete but Accurate Models.
  
• Allen Van Gelder:
  Verifying RUP Proofs of Propositional Unsatisfiability.
  

Papers of the Keynote Speakers

• Francesca Rossi:
  Preferences, Constraints, Uncertainty, and Multi-Agent Scenarios.
  
• Naftali Tishby:
  Extracting Relevant Information from Samples.
  

Papers of the Special Session on Logic in Artificial Intelligence

• Howard A. Blair, David W. Jakel, Robert J. Irwin, A. J. Rivera:
  Hybrid Programs: Symmetrically Combining Natively Discrete and Continuous Truth-values.
  
• Alex Bochman:
  Default Logic Generalized and Simplified.
  
• Thomas Eiter, Thomas Krennwallner, Roman Schindlauer, Giovambattista Ianni:
  Exploiting Conjunctive Queries in Description Logic Programs.
  
• Melvin Fitting:
  Explicit Logics of Knowledge and Conservativity.
  
• Michael Kaminski:
  A Non-Preferential Semantics of Non-Monotonic Modal Logic.
  
• Johann A. Makowsky:
  From Hilbert's Program to a Logic Toolbox.
  
• Veena S. Mellarkod, Michael Gelfond, Yuanlin Zhang:
  Integrating Answer Programming and Constraint Logic Programming.
  
• Ilkka Niemelä:
  Stable Models and Difference Logic.
  
• Jeffrey B. Remmel:
  A Mathematician Looks at Answer Set Programming.
  
• John S. Schlipf, Marc Denecker:
  Complexity of First Order ID-Logic.
  
• Marian Srebrny, Mateusz Srebrny, Lidia Stepien:
  SAT as a Programming Environment for Linear Algebra and Cryptanalysis.
  
• Miroslaw Truszczynski, Stefan Woltran:
  Hyperequivalence of Programs and Operators.
  

Papers of the Special Session on Computation and Social Choice

• Vincent Conitzer:
  Comparing Multiagent Systems Research in Combinatorial Auctions and Voting.
  
• John N. Hooker:
  Optimality Conditions for Distributive Justice.
  
• Toby Walsh:
  Complexity Issues in Preference Elicitation and Manipulation.
  

Papers of the Special Session on Effective Exploration in Active Learning and Reinforcement Learning

• Sanjoy Dasgupta, Daniel Hsu, Claire Monteleoni:
  A General Agnostic Active Learning Algorithm.
  
• Joelle Pineau, Stéphane Ross, Brahim Chaib-draa:
  Bayes-Adaptive POMDPs: A New Perspective on the Explore-Exploit Tradeoff in Partially Observable Domains.
  
• Pascal Poupart, Nikos A. Vlassis:
  Model-based Bayesian Reinforcement Learning in Partially Observable Domains.
  
• Irina Rish, Gerald Tesauro:
  Active Collaborative Prediction with Maximum Margin Matrix Factorization.
  
• Alexander L. Strehl:
  Probably Approximately Correct (PAC) Exploration in Reinforcement Learning.
  

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