- A Modern, AgentOriented Approach to Introductory Artificial Intelligence
- Convergence of reinforcement learning with general function approximators Vassilis A. Papavassiliou and Stuart Russell
- Statistical Visual Language Models for Ink Parsing Michael Shilman, Hanna Pasula, Stuart Russell, Richard Newton
- Handbook of Perception and Cognition, Vol.14 Chapter 4: Machine Learning
- Learning agents for uncertain environments (extended abstract) Stuart Russell
- Stuart Russell ANALOGY BY SIMILARITY
- Learning the Structure of Dynamic Probabilistic Networks Nir Friedman Kevin Murphy Stuart Russell
- , , 1--34 () fl Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- Local learning in probabilistic networks with hidden variables Stuart Russell, \Lambda John Binder, Daphne Koller, y Keiji Kanazawa
- The BATmobile: Towards a Bayesian Automated Taxi
- Angelic Hierarchical Planning: Optimal and Online Bhaskara Marthi
- The BATmobile: Towards a Bayesian Automated Taxi
- Efficient memorybounded search methods Stuart Russell ?
- Spaceefficient inference in dynamic probabilistic networks John Binder, Kevin Murphy, Stuart Russell \Lambda
- State Abstraction for Programmable Reinforcement Learning Agents David Andre and Stuart J. Russell
- Programmable Reinforcement Learning Agents David Andre and Stuart J. Russell
- First-Order Probabilistic Models for Information Extraction Bhaskara Marthi
- Page numbers in bold refer to definitions of terms and algorithms; page numbers in italics refer to items in the bibliography.
- 1 INTRODUCTION 2 INTELLIGENT AGENTS
- Object Identification in a Bayesian Context \Lambda Timothy Huang, Stuart Russell
- On Some Tractable Cases of Logical Filtering T. K. Satish Kumar and Stuart Russell
- A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences
- RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains Emma Brunskill
- Extending Bayesian Networks to the Open-Universe Case
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- 1 communications of the acm | july 2009 | vol. 52 | no. 7 tion not taken is a negative example.
- Anytime Lifted Belief Propagation Rodrigo de Salvo Braz braz@ai.sri.com
- Probabilistic detection of short events, with application to critical care monitoring
- Angelic Semantics for High-Level Actions Bhaskara Marthi
- Angelic Semantics for High-Level Actions Bhaskara Marthi
- 1 BLOG: Probabilistic Models with Unknown Brian Milch
- OH et al.: MARKOV CHAIN MONTE CARLO DATA ASSOCIATION FOR MULTIPLE-TARGET TRACKING 1 Markov Chain Monte Carlo Data Association
- Efficient belief-state ANDOR search, with application to Kriegspiel Stuart Russell and Jason Wolfe
- Concurrent Hierarchical Reinforcement Learning Bhaskara Marthi, Stuart Russell, David Latham
- BLOG: Relational Modeling with Unknown Objects Brian Milch milch@cs.berkeley.edu
- Efficient Gradient Estimation for Motor Control Learning Gregory Lawrence
- Decayed MCMC Filtering Bhaskara Marthi, Hanna Pasula, Stuart Russell, Yuval Peres
- State Abstraction for Programmable Reinforcement Learning David Andre and Stuart J. Russell
- Experimental Comparisons of Online and Batch Versions of Bagging and Boosting
- Approximate inference for first-order probabilistic languages Hanna Pasula and Stuart Russell
- Identity Uncertainty Stuart Russell
- Convergence of reinforcement learning with general function approximators Vassilis A. Papavassiliou and Stuart Russell
- Metareasoning Stuart J. Russell
- Handbook of Perception and Cognition, Vol.14 Chapter 4: Machine Learning
- Bibliography The following abbreviations are used for frequently cited conferences and journals
- RATIONALITY AND INTELLIGENCE STUART RUSSELL
- A QUANTITATIVE ANALYSIS OF ANALOGY BY SIMILARITY Stuart J. Russell
- Improving Gradient Estimation by Incorporating Sensor Data Gregory Lawrence
- Principles of Metareasoning \Lambda Stuart Russell and Eric Wefald
- Identity Uncertainty and Citation Matching Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart Russell, Ilya Shpitser
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- Compositional Modeling With DPNs Geoffrey Zweig Stuart Russell
- Approximate Inference for Infinite Contingent Bayesian Networks Brian Milch, Bhaskara Marthi, David Sontag, Stuart Russell, Daniel L. Ong and Andrey Kolobov
- Adaptive probabilistic networks Stuart Russell, John Binder, Daphne Koller
- Markov Chain Monte Carlo Data Association for General Multiple Target Tracking Problems
- Object Identi cation: A Bayesian Analysis with Application to Tra c Surveillance1
- Probabilistic modeling of sensor artifacts in critical care Norm Aleks and Stuart Russell NORM|RUSSELL@CS.BERKELEY.EDU
- LEARNING IN RATIONAL AGENTS STUART RUSSELL
- A Logical Approach to Reasoning by Analogy Todd R. Davies
- NPCompleteness of Searches for Smallest Possible Feature Sets Scott Davies and Stuart Russell
- Reinforcement Learning with Hierarchies of Machines Ron Parr and Stuart Russell
- Bounded Intention Planning Jason Wolfe
- Control Strategies for a Stochastic Planner \Lambda Jonathan Tash
- Approximating Optimal Policies for Partially Observable Stochastic Domains Ronald Parr, Stuart Russell
- Writing Stratagus-playing Agents in Concurrent ALisp Bhaskara Marthi, Stuart Russell, David Latham
- Expressive Probability Models in Science Stuart Russell
- Rationality and Intelligence Stuart Russell
- BLOG: Probabilistic Models with Unknown Objects Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L. Ong and Andrey Kolobov
- Extended abstract: Learning search strategies Daishi Harada and Stuart Russell
- Why are DBNs sparse? Shaunak Chatterjee Stuart Russell
- Combined Task and Motion Planning for Mobile Manipulation Jason Wolfe
- Temporal Logical Filtering Preliminary Results **** DRAFT October 16, 2002 ****
- Exploiting Belief State Structure in Graph Search Jason Wolfe and Stuart Russell
- Tracking many objects with many sensors Hanna Pasula and Stuart Russell
- Rationality and Intelligence Stuart Russell
- Angelic Hierarchical Planning: Optimal and Online Algorithms Bhaskara Marthi
- Gibbs Sampling in Open-Universe Stochastic Languages Nimar S. Arora
- Automatic Symbolic Traffic Scene Analysis Using Belief Networks \Lambda
- DECLARATIVE BIAS FOR STRUCTURAL DOMAINS Benjamin N. Grosof Stuart J. Russell
- BAYESIAN TREATY MONITORING: PRELIMINARY REPORT Stuart J. Russell,1
- MACHINE LEARNING AT THE CTBTO. TESTING, AND EVALUATION OF THE FALSE EVENTS IDENTIFICATION (FEI) AND VERTICALLY INTEGRATED SEISMIC ASSOCIATION (VISA)
- Global seismic monitoring: A Bayesian approach Nimar S. Arora and Stuart Russell
- A temporally abstracted Viterbi algorithm Shaunak Chatterjee
- Global seismic monitoring as probabilistic inference Nimar S. Arora
- A MATHEMATICAL A.1 COMPLEXITY ANALYSIS AND O() NOTATION
- B NOTES ON LANGUAGES AND ALGORITHMS
