T. Dean

发表

Temporal Data Base Management

Drew McDermott, Thomas L. Dean, D. McDermott, 1987, Artif. Intell..

Generating optimal policies for high-level plans with conditional branches and loops

T. Dean, Shieu-Hong Lin, 1996 .

Planning under uncertainty: structural assumptions and computational leverage

T. Dean, S. Hanks, Craig Boutilier, 1996 .

Decision-Theoretic Planning: Structural Assumptions and Computational Leverage pdf

Craig Boutilier, Thomas L. Dean, Steve Hanks, 1999, J. Artif. Intell. Res..

Decomposition Techniques for Planning in Stochastic Domains

Thomas Dean, Shieu-Hong Lin, T. Dean, 1995, IJCAI.

Hierarchical Solution of Markov Decision Processes using Macro-actions pdf

Milos Hauskrecht, Craig Boutilier, Leslie Pack Kaelbling, 1998, UAI.

Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes

Robert Givan, Thomas L. Dean, Sonia M. Leach, 1997, UAI.

A model for reasoning about persistence and causation

Keiji Kanazawa, Thomas Dean, T. Dean, 1989 .

Planning under Time Constraints in Stochastic Domains

Leslie Pack Kaelbling, Ann E. Nicholson, Thomas L. Dean, 1993, Artif. Intell..

Monte Carlo Simulation and Bottleneck-Centered Heuristics for Time-Critical Scheduling in Stochastic Domains

Lloyd G. Greenwald, T. Dean, 1994 .

Planning With Deadlines in Stochastic Domains

Leslie Pack Kaelbling, Ann E. Nicholson, Thomas L. Dean, 1993, AAAI.

An Analysis of Time-Dependent Planning

Mark S. Boddy, Thomas L. Dean, T. Dean, 1988, AAAI.

Bounded-parameter Markov decision processes

Robert Givan, Thomas L. Dean, Sonia M. Leach, 2000, Artif. Intell..

Solving Very Large Weakly Coupled Markov Decision Processes

Kee-Eung Kim, Milos Hauskrecht, Craig Boutilier, 1998, AAAI/IAAI.

Statistical Machine Learning for Large-Scale Optimization

Kee-Eung Kim, Thomas G. Dietterich, Russell Impagliazzo, 2000 .

Deliberation Scheduling for Time-Critical Sequential Decision Making

Leslie Pack Kaelbling, Ann E. Nicholson, Thomas L. Dean, 1993, UAI.

Planning and Control

Michael P. Wellman, Thomas Dean, T. Dean, 1991 .

Model Minimization in Markov Decision Processes

Robert Givan, Thomas L. Dean, T. Dean, 1997, AAAI/IAAI.

Representations and Algorithms for Large Stochastic Planning Problems

T. Dean, Kee-Eung Kim, 2001 .

Reinforcement Learning for Planning and Control

Thomas Dean, John Shewchuk, Ken Basye, 1993 .

On the Complexity of Solving Markov Decision Problems pdf

Leslie Pack Kaelbling, Thomas L. Dean, Michael L. Littman, 1995, UAI.

Bounded Parameter Markov Decision Processes

Robert Givan, Thomas L. Dean, Sonia M. Leach, 1997, ECP.

Inferring finite automata with stochastic output functions and an application to map learning

Leslie Pack Kaelbling, Shlomo Argamon, Kenneth Basye, 1992, 26th Annual Symposium on Foundations of Computer Science (sfcs 1985).

Solving Time-Dependent Planning Problems

Mark S. Boddy, Thomas L. Dean, T. Dean, 1989, IJCAI.

Solving Stochastic Planning Problems with Large State and Action Spaces

Kee-Eung Kim, Robert Givan, Thomas L. Dean, 1998, AIPS.

Probabilistic Temporal Reasoning

Thomas L. Dean, Keiji Kanazawa, T. Dean, 1988, AAAI.

A Model for Projection and Action

Thomas L. Dean, Keiji Kanazawa, T. Dean, 1989, IJCAI.

Challenge Problems for Artificial Intelligence (Panel Statements)

Eric Horvitz, Rodney A. Brooks, Tom M. Mitchell, 1996, AAAI/IAAI, Vol. 2.

Reasoning About Partially Ordered Events

Mark S. Boddy, Thomas L. Dean, T. Dean, 1988, Artificial Intelligence.

Model Minimization in Markov Decision

T. Dean, R. Givan, 1997 .

Anticipating computational demands when solving time-critical decision-making problems

Thomas Dean, Lloyd G. Greenwald, Lloyd Greenwald, 1995 .

Deliberation Scheduling for Problem Solving in Time-Constrained Environments

Mark S. Boddy, Thomas L. Dean, T. Dean, 1994, Artif. Intell..

Deadlines, Travel Time, and Robot Problem Solving

David P. Miller, R. James Firby, Thomas Dean, 1985, IJCAI.

Artificial intelligence - theory and practice

Yiannis Aloimonos, Thomas L. Dean, James F. Allen, 1995 .

INTRACTABILITY AND TIME-DEPENDENT PLANNING

Thomas Dean, T. Dean, 1987 .

Incremental Causal Reasoning

Mark S. Boddy, Thomas L. Dean, T. Dean, 1987, AAAI.

Persistence and probabilistic projection

Thomas L. Dean, Keiji Kanazawa, T. Dean, 1989, IEEE Trans. Syst. Man Cybern..

Challenge problems for artificial intelligence

Eric Horvitz, Rodney A. Brooks, Tom M. Mitchell, 1996, AAAI 1996.

Automated planning

Thomas Dean, T. Dean, 1996, CSUR.

Coping with Uncertainty in Map Learning

Jeffrey Scott Vitter, Kenneth Basye, Thomas L. Dean, 1989, Machine Learning.

Planning and Scheduling

Subbarao Kambhampati, Thomas L. Dean, T. Dean, 1997, The Computer Science and Engineering Handbook.

Solving Factored MDPs with Large Action Space Using Algebraic Decision Diagrams

Kee-Eung Kim, Thomas L. Dean, T. Dean, 2002, PRICAI.

Strategic Directions in Artificial Intelligence 1

T. Dean, J. Doyle, 1997 .

[4] Reinforcement Learning for Planning and Control

D. Angluin, T. Dean, L. Kaelbling, 2008 .

Model-based Monitoring, Diagnosis and Control

T. Dean, L. Kaelbling, J. Kurien, 2003 .

A Formal Description of the Transportation Problem a Formal Description of the Transportation Problem

Lloyd G. Greenwald, T. Dean, 1992 .

Statistical Machine Learning for Large-Scale Optimization Contributors

Thomas G. Dietterich, Wray L. Buntine, R. Impagliazzo, 2022 .

Time-Critical Planning and Scheduling inStochastic Domains ( Extended Abstract )

Lloyd G. Greenwald, T. Dean, L. Kaelbling, 2007 .

Handling shared resources in a temporal data base management system

T. Dean, 1986, COCS '86.

Designing On-Line Systems A common approach to modeling dynamical

Lloyd G. Greenwald, T. Dean, 2002 .

Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning

D. Angluin, T. Dean, L. Kaelbling, 1992, Machine Learning.

Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI 99, Stockholm, Sweden, July 31 - August 6, 1999. 2 Volumes, 1450 pages

Thomas Dean, T. Dean, 1999, IJCAI.

CS-- 1995 -01 Coping with Uncertainty in Map Learning

T. Dean, J. Vitter, K. Basye, 1989 .

Probabilistic network representations of continuous-time stochastic processes for applications in planning and control

Keiji Kanazawa, Jak Kirman, Thomas Dean, 1992 .

Approximate Solutions to Factored Markov Decision Processes via Greedy Search in the Space of Finite State Controllers

Kee-Eung Kim, Thomas L. Dean, Nicolas Meuleau, 2000, AIPS.

An Approach to Reasoning About Continuous Change for Applications in Planning

Thomas Dean, Greg Siegle, T. Dean, 1990, AAAI.

Temporal Reasoning Involving Counterfactuals and Disjunctions

Thomas Dean, T. Dean, 1985, IJCAI.

Solving factored MDPs using non-homogeneous partitions

Kee-Eung Kim, Thomas L. Dean, T. Dean, 2003, Artif. Intell..

Solving Factored MDPs via Non-Homogeneous Partitioning

Kee-Eung Kim, Thomas L. Dean, T. Dean, 2001, IJCAI.

On the Prospects for Building a Working Model of the Visual Cortex

Glenn Carroll, Thomas L. Dean, Richard Washington, 2007, AAAI.

Probabilistic Tern

T. Dean, K. Kanazawa, 1999 .

Exploiting Locality in Searching the Web

Thomas L. Dean, Joel Young, T. Dean, 2002, UAI.

Learning Dynamics: System Identification for Perceptually Challenged Agents

Leslie Pack Kaelbling, Kenneth Basye, Thomas L. Dean, 1995, Artif. Intell..

The Forbin Paper.

David P. Miller, Thomas Dean, R. J. Firby, 1987 .

Context-dependent computational components

Thomas Dean, T. Dean, 1996, SGAR.

Decision‐theoretic control of inference for time‐critical applications

Thomas Dean, T. Dean, 1991, Int. J. Intell. Syst..

Equivalence notions and model minimization in Markov decision processes

Robert Givan, Thomas L. Dean, Matthew Greig, 2003, Artif. Intell..

Model Minimization, Regression, and Propositional STRIPS Planning

Robert Givan, Thomas L. Dean, T. Dean, 1997, IJCAI.

Challenges for Theory and Practice in Planning

Thomas Dean, Jak Kirman, T. Dean, 1996 .

Learning invariant features using inertial priors

Thomas Dean, T. Dean, 2006, Annals of Mathematics and Artificial Intelligence.

A Declslon-lheodc Approach fm Planning, Perueption, and conrfrol

Kenneth Basye, Thomas Dean, Jak Kirman, 1992 .

Using temporal hierarchies to efficiently maintain large temporal databases

Thomas Dean, T. Dean, 1989, JACM.

Reasoning about the effects of actions in automated planning systems

T. L. Dean, T. Dean, 1988 .

Large-Scale Temporal Data Bases for Planning in Complex Domains

Thomas Dean, T. Dean, 1987, IJCAI.

Planning and temporal reasoning under uncertainty

Thomas Dean, T. Dean, 1984 .

Incremental Causal

T. Dean, Mark Boddyl, 1999 .

Strategic directions in artificial intelligence

Jon Doyle, Thomas Dean, T. Dean, 1996, CSUR.

A Computational Model of the Cerebral Cortex

Thomas Dean, T. Dean, 2005, AAAI.

Decision-Theoretic Planning: Structural Assumptions and Computational Leverage

T. Dean, S. Hanks, Craig Boutilier, 1999, J. Artif. Intell. Res..

Solving Time-critical Decision-making Problems with Predictable Computational Demands

Thomas Dean, Lloyd G. Greenwald, T. Dean, 1994, AIPS.

A Retrospective of the AAAI Robot Competitions

Thomas Dean, R. Peter Bonasso, T. Dean, 1997, AI Mag..

Sensor abstractions for control of navigation

Kenneth Basye, Thomas L. Dean, Jak Kirman, 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

Temporal imagery: an approach to reasoning about time for planning and problem solving

Thomas Dean, T. Dean, 1986 .

Probabilistic causal reasoning pdf

Thomas L. Dean, Keiji Kanazawa, T. Dean, 2013, UAI.

LOCALIZED TEMPORAL REASONING USING SUBGOALS AND ABSTRACT EVENTS

Thomas Dean, Shieu-Hong Lin, T. Dean, 1996, Comput. Intell..

DISTRIBUTED PLANNING AND CONTROL FOR APPLICATIONS IN TRANSPORTATION SCHEDULING

Thomas Dean, Lou Hoebel, T. Dean, 1995 .

Exploiting Locality in Temporal Reasoning

Thomas Dean, Shieu-Hong Lin, T. Dean, 1993 .

Decision-Theoretic Planning: Structural Assumptions and Computational Leverage

T. Dean, S. Hanks, Craig Boutilier, 2011, ArXiv.