Stuart J. Russell

Why Is Stuart J. Russell Influential?

Areas of Specialization: Artificial Intelligence
Stuart J. Russell is the founder of the Center for Human-Compatible Artificial Intelligence and professor of computer science at the University of California, Berkeley, adjunct professor of neurological surgery at the University of California, San Francisco and computer scientist. He earned a B.A. in physics from Wadham College at Oxford and a Ph.D. in computer science from Stanford University.

He is best known as the co-author of Artificial Intelligence: A Modern Approach, which is the most popular textbook on the subject. He is an active researcher in the field, exploring the history and future of artificial intelligence, machine learning, knowledge representation, probabilistic reasoning, inverse reinforcement learning and multitarget tracking.

A vocal opponent of the creation and use of autonomous weapons such as unmanned drones, he worked with the Future of Life Institute to produce a video about drones carrying out assassinations in order to get the attention of the United Nations’ governing bodies on conventional weapons.

He has been awarded the National Science Foundation’s Presidential Young Investigator Award, the World Technology Award, the AAAI/EAAI Outstanding Educator Award and the Mitchell Prize. His most recent published work is Human Compatible: Artificial Intelligence and the Problem of Control, which was published in 2019.

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Featured in Top Influential Computer Scientists Today

According to Wikipedia, Stuart Jonathan Russell is a British computer scientist known for his contributions to artificial intelligence . He is a professor of computer science at the University of California, Berkeley and was from 2008 to 2011 an adjunct professor of neurological surgery at the University of California, San Francisco. He holds the Smith-Zadeh Chair in Engineering at University of California, Berkeley. He founded and leads the Center for Human-Compatible Artificial Intelligence at UC Berkeley. Russell is the co-author with Peter Norvig of the most popular textbook in the field of AI: Artificial Intelligence: A Modern Approach used in more than 1,500 universities in 135 countries.

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Stuart J. Russell's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Papers

Artificial Intelligence: A Modern Approach (1995) (27628)
Distance Metric Learning with Application to Clustering with Side-Information (2002) (3162)
Dynamic bayesian networks: representation, inference and learning (2002) (2833)
Pharmacokinetics of a novel formulation of ivermectin after administration to goats (2000) (2617)
Artificial intelligence - a modern approach, 2nd Edition (2003) (2004)
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping (1999) (1750)
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks (2000) (1423)
Image Segmentation in Video Sequences: A Probabilistic Approach (1997) (1038)
Online bagging and boosting (2005) (907)
Reinforcement Learning with Hierarchies of Machines (1997) (816)
Learning the Structure of Dynamic Probabilistic Networks (1998) (689)
BLOG: Probabilistic Models with Unknown Objects (2005) (544)
Research Priorities for Robust and Beneficial Artificial Intelligence (2015) (504)
Bayesian Q-Learning (1998) (439)
Do the right thing (1991) (428)
Rationality and Intelligence (1995) (426)
Cooperative Inverse Reinforcement Learning (2016) (422)
Do the right thing - studies in limited rationality (1991) (417)
Combined task and motion planning through an extensible planner-independent interface layer (2014) (413)
Principles of Metareasoning (1989) (396)
Adaptive Probabilistic Networks with Hidden Variables (1997) (395)
Towards robust automatic traffic scene analysis in real-time (1994) (361)
Identity Uncertainty and Citation Matching (2002) (357)
Stochastic simulation algorithms for dynamic probabilistic networks (1995) (304)
Provably Bounded Optimal Agents (1993) (299)
Markov chain Monte Carlo data association for general multiple-target tracking problems (2004) (292)
State abstraction for programmable reinforcement learning agents (2002) (277)
Speech Recognition with Dynamic Bayesian Networks (1998) (275)
Experimental comparisons of online and batch versions of bagging and boosting (2001) (260)
Inverse Reward Design (2017) (244)
Learning agents for uncertain environments (extended abstract) (1998) (241)
A generalized mean field algorithm for variational inference in exponential families (2002) (238)
The BATmobile: Towards a Bayesian Automated Taxi (1995) (225)
Object Identification in a Bayesian Context (1997) (224)
Optimal Composition of Real-Time Systems (1996) (217)
Tracking Many Objects with Many Sensors (1999) (210)
Approximating Optimal Policies for Partially Observable Stochastic Domains (1995) (209)
Combined Task and Motion Planning for Mobile Manipulation (2010) (205)
Adversarial Policies: Attacking Deep Reinforcement Learning (2019) (195)
A Logical Approach to Reasoning by Analogy (1987) (195)
Local Learning in Probabilistic Networks with Hidden Variables (1995) (191)
Efficient Memory-Bounded Search Methods (1992) (182)
Composing Real-Time Systems (1991) (182)
Adversarial Training for Relation Extraction (2017) (180)
Automatic Symbolic Traffic Scene Analysis Using Belief Networks (1994) (175)
Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient (2019) (160)
Q-Decomposition for Reinforcement Learning Agents (2003) (153)
PAC-learnability of determinate logic programs (1992) (150)
Anytime Sensing Planning and Action: A Practical Model for Robot Control (1993) (144)
Approximate Reasoning Using Anytime Algorithms (1995) (138)
Programmable Reinforcement Learning Agents (2000) (136)
Learning Plannable Representations with Causal InfoGAN (2018) (135)
A modern, agent-oriented approach to introductory artificial intelligence (1995) (127)
NP-Completeness of Searches for Smallest Possible Feature Sets (1994) (117)
Inteligencia Artificial: un Enfoque Moderno (2013) (116)
Artificial Intelligence (1999) (108)
Variational MCMC (2001) (106)
Object Identification: A Bayesian Analysis with Application to Traffic Surveillance (1998) (100)
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism (2021) (96)
On Optimal Game-Tree Search using Rational Meta-Reasoning (1989) (87)
Probabilistic models with unknown objects (2006) (86)
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design (2020) (84)
The Off-Switch Game (2016) (84)
Angelic Semantics for High-Level Actions (2007) (78)
Robotics: Ethics of artificial intelligence (2015) (75)
Unifying logic and probability (2015) (73)
Selecting Computations: Theory and Applications (2012) (72)
General-Purpose MCMC Inference over Relational Structures (2006) (71)
Approximate Inference for Infinite Contingent Bayesian Networks (2005) (71)
Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information (2013) (70)
Artificial intelligence - a modern approach: the intelligent agent book (1995) (69)
Stephen Hawking: \'Transcendence looks at the implications of artificial intelligence - but are we taking AI seriously enough?\' (2014) (68)
Bayesian Relational Memory for Semantic Visual Navigation (2019) (68)
Rationality and Intelligence: A Brief Update (2013) (68)
Decision Theoretic Subsampling for Induction on Large Databases (1993) (67)
A Declarative Approach to Bias in Concept Learning (1987) (67)
Multitasking (2018) (64)
BLOG: Relational Modeling with Unknown Objects (2004) (64)
Approximate inference for first-order probabilistic languages (2001) (64)
Angelic Hierarchical Planning: Optimal and Online Algorithms (2008) (64)
Multilinear Dynamical Systems for Tensor Time Series (2013) (63)
Control Strategies for a Stochastic Planner (1994) (60)
Artificial intelligence. Fears of an AI pioneer. (2015) (59)
The compleat guide to MRS (1985) (59)
First-Order Probabilistic Languages: Into the Unknown (2007) (57)
Space-Efficient Inference in Dynamic Probabilistic Networks (1997) (55)
Artificial Intelligence - A Modern Approach, Third International Edition (2010) (53)
A quantitative analysis of analogy by similarity (1986) (53)
Analogical and inductive reasoning (1987) (52)
Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter (2015) (49)
Probabilistic modeling with Bayesian networks for automatic speech recognition (1998) (48)
Cognitive Model Priors for Predicting Human Decisions (2019) (47)
Markovian State and Action Abstractions for MDPs via Hierarchical MCTS (2016) (47)
Tree-Structured Bias (1988) (45)
Logical Filtering (2003) (44)
Algorithm selection by rational metareasoning as a model of human strategy selection (2014) (44)
Analogy by Similarity (1988) (42)
A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences (2002) (41)
Should We Fear Supersmart Robots? (2016) (41)
Should Robots be Obedient? (2017) (40)
Using Classical Planners for Tasks with Continuous Operators in Robotics (2013) (40)
Preliminary Steps Toward the Automation of Induction (1986) (39)
Challenge: What is the Impact of Bayesian Networks on Learning? (1997) (39)
Efficient Gradient Estimation for Motor Control Learning (2002) (37)
Efficient belief-state AND-OR search, with application to Kriegspiel (2005) (36)
EBOOK : Artificial Intelligence, A Modern Approach, 3rd edition (2010) (35)
Efficient resource-bounded reasoning in AT-RALPH (1992) (34)
Execution Architectures and Compilation (1989) (34)
How Long Will It Take? (1992) (33)
Decayed MCMC Filtering (2012) (31)
Handbook of Perception and Cognition (2011) (31)
Reinforcement learning for autonomous vehicles (2002) (30)
Rationality as an explanation of language (1987) (30)
Predicting human decisions with behavioral theories and machine learning (2019) (30)
Meta-Learning MCMC Proposals (2017) (29)
Probabilistic detection of short events, with application to critical care monitoring (2008) (29)
First-Order Probabilistic Models for Information Extraction (2003) (27)
Declarative Bias: An Overview (1990) (27)
Adaptive Probabilistic Networks (1994) (26)
Swift: Compiled Inference for Probabilistic Programming Languages (2016) (26)
Writing and sketching in the air, recognizing and controlling on the fly (2013) (26)
Artificial Intelligence (1986) (26)
Inverse reinforcement learning for video games (2018) (25)
First-Order Open-Universe POMDPs (2014) (25)
RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains (2010) (25)
Eecient Memory-bounded Search Methods (1992) (25)
Metaphysics of Planning Domain Descriptions (2016) (24)
Boundaries of Operationality (1988) (24)
Convergence of Reinforcement Learning with General Function Approximators (1999) (24)
Artificial intelligence: The future is superintelligent (2017) (23)
Decayed MCMC iltering (2002) (23)
Quantifying Differences in Reward Functions (2020) (23)
Stratagus-playing Agents in Concurrent ALisp (22)
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms (2018) (22)
Decision-Theoretic Control of Reasoning: General Theory and an (1988) (21)
Machine Learning, Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, USA, July 9-12, 1995 (1995) (20)
A Sketch of Autonomous Learning using Declarative Bias (1990) (20)
Efficient Reinforcement Learning with Hierarchies of Machines by Leveraging Internal Transitions (2017) (19)
Shift of Bias as Non-Monotonic Reasoning (1990) (19)
Analogy and Single-Instance Generalization (1987) (18)
Gaussian Process Random Fields (2015) (17)
Metareasoning for Monte Carlo Tree Search (2011) (17)
Learning from Examples and Membership Queries with Structured Determinations (1998) (17)
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning (2018) (17)
Tractability of Planning with Loops (2015) (17)
The MAGICAL Benchmark for Robust Imitation (2020) (16)
Partially Observable Sequential Decision Making for Problem Selection in an Intelligent Tutoring System (2011) (16)
A temporally abstracted Viterbi algorithm (2011) (16)
Exploiting locality in probabilistic inference (2004) (16)
Learnability of Constrained Logic Programs (1993) (15)
Clusterability in Neural Networks (2021) (15)
Adaptive Learning of Decision-Theoretic Search Control Knowledge (1989) (15)
Sequential quadratic programming for task plan optimization (2016) (14)
The MineRL BASALT Competition on Learning from Human Feedback (2021) (14)
Learning and Planning with a Semantic Model (2018) (14)
Fine-Grained Decision-Theoretic Search Control (2013) (14)
Why are DBNs sparse? (2010) (13)
MADE: Exploration via Maximizing Deviation from Explored Regions (2021) (13)
Probabilistic model-based approach for heart beat detection (2015) (13)
An architecture for bounded rationality (1991) (11)
Bounded Intention Planning (2011) (11)
The Extended Parameter Filter (2013) (11)
Prior knowledge and autonomous learning (1991) (11)
An Empirical Investigation of Representation Learning for Imitation (2022) (11)
Benefits of Assistance over Reward Learning (2020) (11)
Declarative Bias for Structural Domains (1989) (11)
Planning Using Multiple Execution Architectures (1993) (11)
Extending Bayesian Networks to the Open-Universe Case (2009) (11)
Mutual Constraints on Representation and Inference (1990) (11)
A Smart-Dumb/Dumb-Smart Algorithm for Efficient Split-Merge MCMC (2015) (11)
Cross-Domain Imitation Learning via Optimal Transport (2021) (10)
Extended abstract: Learning search strategies (1999) (10)
Understanding Learned Reward Functions (2020) (10)
IMEX: Overcoming Intactability In Explanation Based Learning (1988) (10)
Artificial Intelligence (2020) (10)
Dynamic Scaled Sampling for Deterministic Constraints (2013) (9)
Unifying Logic and Probability: A New Dawn for AI? (2014) (9)
Estimating and Penalizing Preference Shift in Recommender Systems (2021) (8)
Neural Networks are Surprisingly Modular (2020) (8)
Estimating and Penalizing Induced Preference Shifts in Recommender Systems (2022) (8)
Compositional Modeling with DPNs (1997) (8)
Angelic Hierarchical Planning: Optimal and Online Algorithms (Revised) (2009) (8)
Modelling Glycaemia in ICU Patients - A Dynamic Bayesian Network Approach (2010) (7)
Signal-based Bayesian Seismic Monitoring (2017) (7)
Detecting Modularity in Deep Neural Networks (2021) (7)
Probabilistic graphical models and algorithms for genomic analysis (2004) (7)
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning (2022) (7)
Inductive learning by machines (1991) (7)
A Compact, Hierarchical Q-function Decomposition (2012) (6)
On Some Tractable Cases of Logical Filtering (2006) (6)
A compact, hierarchically optimal Q-function decomposition (2006) (6)
Scalable Online Planning via Reinforcement Learning Fine-Tuning (2021) (5)
Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making (2018) (5)
Bayesian problem-solving applied to scheduling (1998) (5)
It's not too soon to be wary of AI: We need to act now to protect humanity from future superintelligent machines (2019) (5)
Expressive Probability Models in Science (1999) (5)
Complete Guide to MRS (1985) (5)
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory (2020) (5)
Trustworthy AI (2021) (5)
Automated Pricing Agents in the On-Demand Economy (2016) (5)
Exploiting Belief State Structure in Graph Search (2007) (4)
Artificial Intelligence and the Problem of Control (2021) (4)
Pruned Neural Networks are Surprisingly Modular (2020) (4)
Fast Gaussian Process Posteriors with Product Trees (2014) (4)
Human-Compatible Artificial Intelligence (2021) (4)
Uncertain Decisions Facilitate Better Preference Learning (2021) (4)
Object Identi cation : A Bayesian Analysiswith Application to Tra c Surveillance 1 (1998) (4)
First-Order Open-Universe POMDPs: Formulation and Algorithms (2013) (4)
Explore and Control with Adversarial Surprise (2021) (4)
PAC Learning of Causal Trees with Latent Variables (2021) (4)
Quantifying Local Specialization in Deep Neural Networks (2021) (3)
A Nearly-Black-Box Online Algorithm for Joint Parameter and State Estimation in Temporal Models (2017) (3)
Handbook of Perception and Cognition , Vol . 14 Chapter 4 : Machine Learning (2007) (3)
Machine Learning (1996) (3)
DERAIL: Diagnostic Environments for Reward And Imitation Learning (2020) (3)
Multi-Principal Assistance Games: Definition and Collegial Mechanisms (2020) (3)
PNPACK: Computing with Probabilities in Java (1997) (3)
Multi-Principal Assistance Games (2020) (3)
Concurrent Hierarchical Reinforcement Learning for RoboCup Keepaway (2017) (3)
Cooperative and uncooperative institution designs: Surprises and problems in open-source game theory (2022) (2)
BFiT: From Possible-World Semantics to Random-Evaluation Semantics in an Open Universe (2014) (2)
Neural Block Sampling (2017) (2)
Model Based Probabilistic Inference for Intensive Care Medicine (2015) (2)
Accumulating Risk Capital Through Investing in Cooperation (2021) (2)
Who speaks for AI? (2016) (2)
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian (2022) (2)
Servant of Many Masters: Shifting priorities in Pareto-optimal sequential decision-making (2017) (2)
Social impact and governance of AI and neurotechnologies (2022) (2)
Efficient Cooperative Inverse Reinforcement Learning (2017) (2)
Learning in Rational Agents (1997) (2)
Quantitative Analysis of Analogy (1986) (2)
The Physics of Text: Ontological Realism in Information Extraction (2016) (2)
Combined State and Parameter Estimation of Human Intracranial Hemodynamics (2013) (2)
Probabilistic modeling of sensor artifacts in critical care (2008) (2)
Expressive Probability Models For Speech Recognition And Understanding (1999) (2)
Reports of the AAAI 2010 Conference Workshops (2010) (1)
Control of Mobile Robots Using Anytime Computation (1992) (1)
Uncertain Observation Times (2012) (1)
Unifying Logic and Probability : Recent Developments (2014) (1)
For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria (2022) (1)
Title Feasibility Study Of Fully Autonomous Vehicles Using Decision-theoretic Control Permalink (2000) (1)
146 Modeling and Machine Learning of Cerebrovascular Dynamics. (2012) (1)
Technical perspectiveThe ultimate pilot program (2009) (1)
Towards Practical Bayesian Parameter and State Estimation (2016) (0)
imitation: Clean Imitation Learning Implementations (2022) (0)
Decision-Theoretic Planning with Multiple Execution Architectures* (1993) (0)
If We Succeed (2022) (0)
Applications of distributed acoustic sensing for security and surveillance (2017) (0)
Commentary on Baum ’ s " How a Bayesian . . ? (0)
Compositional Modeling With DPNsGeo (1997) (0)
Selecting the Partial State Abstractions of MDPs: A Metareasoning Approach with Deep Reinforcement Learning (2022) (0)
Object Identiication: a Bayesian Analysis with Application to Traac Surveillance 1 (2009) (0)
Joint State and Parameter Estimation in Temporal Models by Yusuf Bugra (2018) (0)
Statistical Relational Artificial Intelligence, Papers from the 2010 AAAI Workshop, Atlanta, Georgia, USA, July 12, 2010 (2010) (0)
vercoming Hntr ctability in Err ased Learning (1988) (0)
Uncertain Learning Agents (Abstract) (1997) (0)
Value Determination with General Function Approximators (2000) (0)
Control Strategies for a tochastic Planner (1994) (0)
Patient-adaptable intracranial pressure morphology analysis using a probabilistic model-based approach (2020) (0)
Value determination with function approximation (2003) (0)
Multitasking: Optimal Planning for Bandit Superprocesses (2015) (0)
Syllabus COSC 5365 Artificial Intelligence (2006) (0)
AI for Humanity: The Global Challenges (2021) (0)
Application to Problem-Solving Search (2003) (0)
Learning the Value of Computation (2003) (0)
Proceedings 37th International Conference on Logic Programming (Technical Communications) (2021) (0)
Resources on Existential Risk General Blockinscholarly Blockindiscussion Blockinof Blockinexistential Blockinrisk (0)
Open-Universe Theory for Bayesian Inference, Decision, and Sensing (OUTBIDS) (2014) (0)
Stuart Russell ANALOGY BY SIMILARITY (0)
Tools for Autonomous Agents (Abstract) (1996) (0)
New light on RV Tau variables. (1997) (0)
1 APPROXIMATE REASONING USING ANYTIME ALGORITHMS (1994) (0)
Temporal Logical Filtering - Preliminary Results (2002) (0)
The Future of (Artificial) Intelligence (2015) (0)
Two Techniques for Tractable Decision-Theoretic Planning* (1994) (0)
Learning a Semantic Prior for Guided Navigation (2018) (0)
Product Trees for Gaussian Process Covariance in Sublinear Time (2013) (0)
Learning the Preferences of Uncertain Humans with Inverse Decision Theory (2021) (0)
Efficient motor control learning (2009) (0)
Improving Gradient Estimation by Incorporating Sensor Data (2008) (0)
Application to Game-Playing (2003) (0)
Hierarchical Planning for Mobile Manipulation (2010) (0)
Electric light cables and the distribution of electricity (2010) (0)
Source Materials (2020) (0)
Distributed acoustic sensing for infrastructure (2017) (0)
Supplementary Material for "SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory" (2021) (0)
Towards Limited Rational Agents (2003) (0)