[1] |
Thomas Dean.
Talking With Computers.
Cambridge University Press, New York, 2005.
(WWW)
|
[2] |
Thomas Dean, editor.
Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, IJCAI-99, Stockholm, Sweden, July 31 –
August 6, 1999, 2 Volumes.
Morgan Kaufmann Publishers, San Francisco, California, 1999.
|
[3] |
Thomas Dean, James Allen, and Yiannis Aloimonos.
Artificial Intelligence: Theory and Practice.
Addison-Wesley Publishing Company, Reading, Massachusetts, 1995.
(WWW)
|
[4] |
Thomas Dean and Kathy McKeown, editors.
Proceedings of the Ninth National Conference on Artificial
Intelligence, AAAI-91, Anaheim, California, July 14-19, 1991, 2
Volumes.
MIT Press, Cambridge, Massachusetts, 1991.
|
[5] |
Thomas Dean and Michael Wellman.
Planning and Control.
Morgan Kaufmann Publishers, San Francisco, California, 1991.
(WWW)
|
[6] |
Thomas Dean.
Temporal Imagery: An Approach to Reasoning about Time for
Planning and Problem Solving (Ph.D. Thesis).
Bell and Howell Information and Learning, Skokie, Illinois, 1985.
|
[1] |
Gary Marcus, Adam Marblestone and Thomas Dean.
The atoms of neural computation.
Science,
346(6289):551–552, 2014.
(PDF)
|
[2] |
David Daniel Cox and Thomas Dean.
Neural networks and neuroscience-inspired computer vision.
Current Biology,
24(18):921–929, 2014.
(PDF)
|
[3] |
Thomas Dean.
Learning invariant features using inertial priors.
Annals of Mathematics and Artificial Intelligence,
47(3-4):223–250, August 2006.
(PDF)
|
[4] |
Robert Givan, Thomas Dean, and Matthew Greig.
Equivalence notions and model minimization in Markov decision
processes.
Artificial Intelligence, 147(1-2):163–223, 2003.
(PDF)
|
[5] |
Kee-Eung Kim and Thomas Dean.
Solving factored Markov decision processes using non-homogeneous
partitions.
Artificial Intelligence, 147(1-2):225–251, 2003.
(PDF)
|
[6] |
Robert Givan, Sonia Leach, and Thomas Dean.
Bounded parameter Markov decision processes.
Artificial Intelligence, 122(1-2):71–109, 2000.
(PDF)
|
[7] |
Craig Boutilier, Thomas Dean, and Steve Hanks.
Decision theoretic planning: Structural assumptions and computational
leverage.
Journal of Artificial Intelligence Research, 11:1–94, 1999.
(PDF)
|
[8] |
Kenneth Basye, Thomas Dean, and Jeffrey Scott Vitter.
Coping with uncertainty in map learning.
Machine Learning, 29(1):65–88, 1997.
(PDF)
|
[9] |
Thomas Dean and R. Peter Bonasso.
A retrospective on the AAAI robot competitions.
AI Magazine, 18(1):11–23, 1997.
|
[10] |
Jon Doyle and Thomas Dean.
Strategic directions in artificial intelligence (Reprint).
AI Magazine, 18(1):87–101, 1997.
|
[11] |
Shieu-Hong Lin and Thomas Dean.
Localized temporal reasoning using subgoals and abstract events.
Computational Intelligence, 12(3):423–449, 1996.
(PDF)
|
[12] |
Moises Lejter and Thomas Dean.
A framework for the development of multiagent architectures.
IEEE Expert, 11(6):47–59, 1996.
(PDF)
|
[13] |
Jon Doyle and Thomas Dean.
Strategic directions in artificial intelligence (Reprinted in AI Magazine 18 (1997)).
ACM Computing Surveys, 28(4):653–670, 1996.
|
[14] |
Thomas Dean.
Integrating theory and practice in planning.
ACM Computing Surveys, 28(4):2, 1996.
|
[15] |
Lloyd Greenwald and Thomas Dean.
Package routing in transportation networks with fixed vehicle
schedules: Formulation, complexity results and approximation algorithms.
Networks, 27(1):81–93, 1996.
|
[16] |
Thomas Dean, Dana Angluin, Kenneth Basye, Sean Engelson, Leslie Kaelbling,
Evangelos Kokkevis, and Oded Maron.
Inferring finite automata with stochastic output functions and an
application to map learning.
Machine Learning, 18(1):81–108, 1995.
(PDF)
|
[17] |
Thomas Dean, Leslie Kaelbling, Jak Kirman, and Ann Nicholson.
Planning under time constraints in stochastic domains.
Artificial Intelligence, 76(1-2):35–74, 1995.
(PDF)
|
[18] |
Kenneth Basye, Thomas Dean, and Leslie Kaelbling.
Learning dynamics: System identification for perceptually
challenged agents.
Artificial Intelligence, 72(1-2):139–171, 1995.
(PDF)
|
[19] |
Mark Boddy and Thomas Dean.
Decision-theoretic deliberation scheduling for problem solving in
time-constrained environments.
Artificial Intelligence, 67(2):245–286, 1994.
(PDF)
|
[20] |
Thomas Dean and R. Peter Bonasso.
1992 AAAI robot exhibition and competition.
AI Magazine, 14(1):35–48, 1993.
|
[21] |
Kenneth Basye, Thomas Dean, Jak Kirman, and Moises Lejter.
A decision-theoretic approach to planning, perception, and control.
IEEE Expert, 7(4):58–65, 1992.
(PDF)
|
[22] |
Thomas Dean.
Decision-theoretic control of inference for time-critical
applications.
International Journal Intelligent Systems, 6(4):417–441, 1991.
|
[23] |
Thomas Dean and Keiji Kanazawa.
Persistence and probabilistic inference.
IEEE Transactions on Systems, Man, and Cybernetics,
19(3):574–585, 1989.
(PDF)
|
[24] |
Thomas Dean and Keiji Kanazawa.
A model for reasoning about persistence and causation.
Computational Intelligence Journal, 5(3):142–150, 1989.
(PDF)
|
[25] |
Thomas Dean.
Using temporal hierarchies to efficiently maintain large temporal
databases.
Journal of the ACM, 36(4):687–718, 1989.
(PDF)
|
[26] |
Thomas Dean, R. James Firby, and David P. Miller.
Hierarchical planning involving deadlines, travel time and resources
(Also appears in “Readings in Planning” (Morgan Kaufmann), edited
by James Allen, James Hendler, and Austin Tate, and in
“Autonomous Mobile Robots: Control, Planning, and Architecture”
(IEEE Computer Society Press), edited by S. S. Iyengar and
Alberto Elfes).
Computational Intelligence, 4(4):381–398, 1988.
|
[27] |
Thomas Dean and Mark Boddy.
Reasoning about partially ordered events (Also appears in
“Readings in Qualitative Reasoning About Physical Systems”
(Morgan Kaufmann), edited by Dan Weld and Johan De Kleer).
Artificial Intelligence, 36(3):375–399, 1988.
|
[28] |
Thomas Dean.
An approach to reasoning about the effects of actions for automated
planning systems.
Annals of Operations Research, 12(1-4):147–167, 1988.
|
[29] |
Thomas Dean and Drew V. McDermott.
Temporal data base management (Also appears in “Readings in
Planning” (Morgan Kaufmann), edited by James Allen, James
Hendler, and Austin Tate).
Artificial Intelligence, 32(1):1–55, 1987.
|
[30] |
Thomas Dean.
Handling shared resources in a temporal database management system.
Decision Support Systems, 2(2):135–143, 1986.
|
[1] |
Thomas Dean, Mark Ruzon, Mark Segal, Jon Shlens, Sudheendra Vijayanarasimhan,
and Jay Yagnik.
Fast, accurate detection of 100,000 object classes on a single
machine.
In IEEE Conference on Computer Vision and Pattern Recognition, Winner of the CVPR Best Paper Award, 2013.
(PDF)
|
[2] |
Thomas Dean, Greg S. Corrado, and Jonathon Shlens.
Three controversial hypotheses concerning computation in the primate
cortex.
In Proceedings of the Twenty-Sixth AAAI Conference on
Artificial Intelligence, 2012.
(PDF)
|
[3] |
Thomas Dean, Greg Corrado, and Rich Washington.
Recursive sparse spatiotemporal coding.
In Proceedings of the Fifth IEEE International Workshop on
Multimedia Information Processing and Retrieval, 2009.
|
[4] |
Thomas Dean, Glenn Carroll, and Rich Washington.
On the prospects for building a working model of the visual cortex.
In Proceedings of AAAI-07, pages 1597–1600, Cambridge,
Massachusetts, 2007. MIT Press.
(PDF)
|
[5] |
Thomas Dean.
Scalable inference in hierarchical generative models.
In Proceedings of the Ninth International Symposium on
Artificial Intelligence and Mathematics, 2006.
(PDF)
|
[6] |
Thomas Dean.
A computational model of the cerebral cortex.
In Proceedings of AAAI-05, pages 938–943, Cambridge,
Massachusetts, 2005. MIT Press.
(PDF)
|
[7] |
Joel Young and Thomas Dean.
Exploiting locality in searching the web.
In Proceedings of the 19th Annual Conference on Uncertainty in
Artificial Intelligence (UAI-03), pages 608–615, San Francisco,
California, 2003. AUAI, Morgan Kaufmann Publishers.
(PDF)
|
[8] |
Kee-Eung Kim and Thomas Dean.
Solving factored MDPs with large action spaces using algebraic
decision diagrams.
In Mitsuru Ishizuka and Abdul Satter, editors, Proceedings of
the 7th Pacific Rim International Conference on Artificial Intelligence
(PRICAI-02), pages 80–89. PRIJCAI, Springer, 2002.
|
[9] |
Kee-Eung Kim and Thomas Dean.
Solving factored MDPs using non-homogeneous partitions.
In Proceedings IJCAI-01, pages 683–689, San Francisco,
California, 2001. IJCAI, Morgan Kaufmann Publishers.
(PDF)
|
[10] |
Kee-Eung Kim, Nicolas Meuleau, and Thomas Dean.
Approximate solutions to factored Markov decision processes via
greedy search in the space of finite state controllers.
In Proceedings of the 5th International Conference on Artificial
Intelligence Planning Systems (ICAPS-2000, pages 323–330, Menlo Park,
California, 2000. AAAI Press.
(PDF)
|
[11] |
Nicolas Meuleau, Craig Boutilier, Milos Hauskrecht, Leslie Kaelbling, Kee-Eung
Kim, Leonid Peshkin, and Thomas Dean.
Solving very large weakly coupled Markov decision processes.
In Proceedings AAAI-98, pages 165–172, Cambridge,
Massachusetts, 1998. AAAI, MIT Press.
(PDF)
|
[12] |
Milos Hauskrecht, Nicolas Meuleau, Craig Boutilier, Leslie Pack Kaelbling, and
Thomas Dean.
Hierarchical solution of Markov decision processes using
macro-actions.
In Proceedings of the 14th Conference on Uncertainty in
Artificial Intelligence (UAI-98), pages 220–229, San Francisco,
California, 1998. AUAI, Morgan Kaufmann Publishers.
(PDF)
|
[13] |
Lloyd Greenwald and Thomas Dean.
A conditional scheduling approach to designing real-time systems.
In Proceedings of the 4th International Conference on Artificial
Intelligence Planning Systems (ICAPS-98), pages 224–231, 1998.
(PDF)
|
[14] |
Thomas Dean, Robert Givan, and Kee-Eung Kim.
Solving planning problems with large state and action spaces.
In Proceedings of the 4th International Conference on Artificial
Intelligence Planning Systems (ICAPS-98), pages 102–110, 1998.
(PDF)
|
[15] |
Robert Givan, Sonia Leach, and Thomas Dean.
Bounded parameter Markov decision processes.
In Proceedings of the 4th European Conference on Planning,
pages 234–246, 1997.
(PDF)
|
[16] |
Robert Givan and Thomas Dean.
Model minimization, regression, and propositional STRIPS planning.
In Proceedings IJCAI-97, pages 1163–1168, San Francisco,
California, 1997. IJCAI, Morgan Kaufmann Publishers.
(PDF)
|
[17] |
Thomas Dean, Robert Givan, and Sonia Leach.
Model reduction techniques for computing approximately optimal
solutions for Markov decision processes.
In Dan Geiger and Prakesh Pundalik Shenoy, editors, Proceedings
of the 13th Conference on Uncertainty in Artificial Intelligence, pages
124–131, San Francisco, California, 1997. AUAI, Morgan Kaufmann Publishers.
(PDF)
|
[18] |
Thomas Dean and Jean-Luc Marion.
Planning and navigation in stochastic environments.
In Yiannis Aloimonos, editor, Visual Navigation: From Biological
Systems to Unmanned Ground Vehicles. Lawrence Erlbaum Associates,
Publishers, Mahwah, New Jersey, 1997.
|
[19] |
Thomas Dean and Robert Givan.
Model minimization in Markov decision processes.
In Proceedings AAAI-97, pages 106–111, Cambridge,
Massachusetts, 1997. AAAI, MIT Press.
(PDF)
|
[20] |
Thomas Dean.
Context-dependent computational components.
SIGART Bulletin, 7(2):3–6, 1996.
|
[21] |
Bart Selman, Rodney A. Brooks, Thomas Dean, Eric Horvitz, Tom M. Mitchell, and
Nils J. Nilsson.
Challenge problems for artificial intelligence (panel statements).
In Proceedings of AAAI-96, pages 1340–1345, Cambridge,
Massachusetts, 1996. AAAI, MIT Press.
|
[22] |
Michael Littman, Thomas Dean, and Leslie Kaelbling.
On the complexity of solving Markov decision problems.
In Proceedings of the 11th Conference on Uncertainty in
Artificial Intelligence, pages 394–402, San Francisco, California, 1995.
AUAI, Morgan Kaufmann Publishers.
(PDF)
|
[23] |
Shieu-Hong Lin and Thomas Dean.
Generating optimal policies for high-level plans with conditional
branches and loops.
In Proceedings of the 3rd European Workshop on Planning, pages
205–218, 1995.
(PDF)
|
[24] |
Thomas Dean and Shieu-Hong Lin.
Decomposition techniques for planning in stochastic domains.
In Proceedings IJCAI-95, pages 1121–1127, San Francisco,
California, 1995. IJCAI, Morgan Kaufmann Publishers.
(PDF)
|
[25] |
Craig Boutilier, Thomas Dean, and Steve Hanks.
Planning under uncertainty: Structural assumptions and computational
leverage.
In Proceedings of the 3rd European Workshop on Planning, 1995.
|
[26] |
Thomas Dean.
Meditations on time and space: Expediting temporal reasoning by
exploiting time and space (Abstract).
In S.D. Goodwin and H.J. Hamilton, editors, Proceedings of the
First International Workshop on Temporal Representation and Reasoning
(TIME-94), pages 3–12, May 1994.
|
[27] |
Lloyd Greenwald and Thomas Dean.
Anticipating computational demands when solving time-critical
decision-making problems.
In K. Goldberg, D. Halperin, J.C. Latombe, and R. Wilson, editors,
The Algorithmic Foundations of Robotics. A. K. Peters, Boston,
Massachusetts, 1995.
|
[28] |
Lloyd Greenwald and Thomas Dean.
Monte Carlo simulation and bottleneck-centered heuristics for
time-critical scheduling in stochastic domains.
In ARPI Planning Initiative Workshop, 1994.
|
[29] |
Lloyd Greenwald and Thomas Dean.
Solving time-critical decision-making problems with predictable
computational demands.
In Kristian Hammond, editor, Proceedings of the 2nd
International Conference on AI Planning Systems (ICAPS-94), pages
25–30. AAAI Press, 1994.
|
[30] |
Jak Kirman, Ann Nicholson, Moises Lejter, Thomas Dean, and Eugene Santos Jr.
Using goals to find plans with high expected utility.
In E. Sandewall and C. Backstrom, editors, Current Trends in
AI Planning, Amsterdam, 1994. IOS Press.
(PDF)
|
[31] |
Shieu-Hong Lin and Thomas Dean.
Temporal reasoning: A state-based approach.
In S.D. Goodwin and H.J. Hamilton, editors, Proceedings of the
First International Workshop on Temporal Representation and Reasoning
(TIME-94), pages 3–12, May 1994.
|
[32] |
Shieu-Hong Lin and Thomas Dean.
Exploiting locality in temporal reasoning.
In E. Sandewall and C. Backstrom, editors, Current Trends in
AI Planning, Amsterdam, 1994. IOS Press.
(PDF)
|
[33] |
Thomas Dean, Leslie Kaelbling, Jak Kirman, and Ann Nicholson.
Deliberation scheduling for time-critical sequential decision making.
In Proceedings of the 9th Conference on Uncertainty in
Artificial Intelligence, pages 309–316, San Francisco, California, 1993.
AUAI, Morgan Kaufmann Publishers.
|
[34] |
Thomas Dean, Leslie Kaelbling, Jak Kirman, and Ann Nicholson.
Planning with deadlines in stochastic domains.
In Proceedings AAAI-93, pages 574–579, Cambridge,
Massachusetts, 1993. AAAI, MIT Press.
(PDF)
|
[35] |
Theodore Camus, Jonathan Monsarrat, and Thomas Dean.
Planning and selective perception for mobile robot object retrieval
tasks.
In Proceedings of the DARPA Image Understanding Workshop.
DARPA, 1993.
|
[36] |
Thomas Dean.
Anticipating tomorrow’s technology needs.
Omni Magazine, November 1992.
|
[37] |
T. Dean, J. Kirman, and K. Kanazawa.
Probabilistic network representations of continuous-time stochastic
processes for applications in planning and control.
In James Hendler, editor, Proceedings of the First International
Conference on Artificial Intelligence Planning Systems (ICAPS-92), pages
273–274. Morgan Kaufmann Publishers, San Francisco, California, 1992.
|
[38] |
Thomas Dean, Dana Angluin, Kenneth Basye, Sean Engelson, Leslie Kaelbling,
Evangelos Kokkevis, and Oded Maron.
Inferring finite automata with stochastic output functions and an
application to map learning.
In Proceedings AAAI-92, pages 208–214, Cambridge,
Massachusetts, 1992. AAAI, MIT Press.
|
[39] |
Thomas Dean, Ken Basye, and Leslie Kaelbling.
Uncertainty in graph-based map learning.
In Jonathon Connell and Sridhar Mahadevan, editors, Robot
Learning. Kluwer, 1992.
|
[40] |
Thomas Dean, Ken Basye, and John Shewchuk.
Reinforcement learning for planning and control.
In Steve Minton, editor, Machine Learning Methods for Planning
and Scheduling. Morgan Kaufmann Publishers, San Francisco, California, 1992.
(PDF)
|
[41] |
Thomas Dean and Jak Kirman.
Representation issues in Bayesian decision theory for planning and
active perception.
In Proceedings of the DARPA Image Understanding Workshop, pages
763–768. DARPA, 1992.
|
[42] |
Leslie Kaelbling, Kenneth Basye, and Thomas Dean.
Learning labelled graphs from noisy data.
In Proceedings of the 7th Yale Workshop on Adaptive and Learning
Systems, pages 149–154. Center for Systems Science, 1992.
|
[43] |
Jak Kirman, Kenneth Basye, and Thomas Dean.
Sensor abstractions for control of navigation.
In Proceedings of the IEEE International Conference on
Robotics and Automation, pages 2812–2817, 1991.
|
[44] |
Mark Boddy.
Anytime problem solving using dynamic programming.
In Proceedings AAAI-91, pages 738–743, Cambridge,
Massachusetts, 1991. AAAI, MIT Press.
|
[45] |
John Shewchuk and Thomas Dean.
Towards learning time-varying functions with high input
dimensionality.
In Proceedings of the 5th IEEE International Symposium on
Intelligent Control, pages 383–388. IEEE, 1990.
|
[46] |
Thomas Dean, Theodore Camus, and Jak Kirman.
Sequential decision making for active perception.
In Proceedings of the DARPA Image Understanding Workshop, pages
889–894. DARPA, 1990.
|
[47] |
Thomas Dean, Keiji Kanazawa, and John Shewchuk.
Prediction, observation, and estimation in planning and control.
In Proceedings of the 5th IEEE International Symposium on
Intelligent Control, pages 645–650, 1990.
|
[48] |
Thomas Dean, Kenneth Basye, and Moises Lejter.
Planning and active perception.
In Proceedings of the DARPA Workshop on Innovative Approaches to
Planning, Scheduling, and Control, pages 271–276. DARPA, 1990.
|
[49] |
Thomas Dean, Kenneth Basye, Robert Chekaluk, Seungseok Hyun, Moises Lejter, and
Margaret Randazza.
Coping with uncertainty in a control system for navigation and
exploration.
In Proceedings AAAI-90, pages 1010–1015, Cambridge,
Massachusetts, 1990. AAAI, MIT Press.
|
[50] |
Thomas Dean and Greg Siegle.
An approach to reasoning about continuous change for applications in
planning.
In Proceedings AAAI-90, pages 132–137, Cambridge,
Massachusetts, 1990. AAAI, MIT Press.
|
[51] |
D. B. Cooper, T. L. Dean, and W. A. Wolovich.
Image understanding research at brown.
In Proceedings of the DARPA Image Understanding Workshop, pages
131–133. DARPA, 1990.
|
[52] |
Keiji Kanazawa and Thomas Dean.
A model for projection and action.
In Proceedings IJCAI-89, pages 985–990, San Francisco,
California, 1989. IJCAI, Morgan Kaufmann Publishers.
|
[53] |
Thomas Dean and Michael Wellman.
On the value of goals.
In Josh Tenenberg, Jay Weber, and James Allen, editors, Proceedings from the Rochester Planning Workshop: From Formal Systems to
Practical Systems, pages 129–140, 1989.
|
[54] |
Mark Boddy and Thomas Dean.
Approximation algorithms for planning and control.
In Proceedings of the NASA Conference on Telerobotics, 1989.
|
[55] |
Mark Boddy and Thomas Dean.
Solving time-dependent planning problems.
In Proceedings IJCAI-89, pages 979–984, San Francisco,
California, 1989. IJCAI, Morgan Kaufmann Publishers.
|
[56] |
Kenneth Basye, Thomas Dean, and Jeffrey Scott Vitter.
Coping with uncertainty in map learning.
In Proceedings IJCAI-89, pages 663–668, San Francisco,
California, 1989. IJCAI, Morgan Kaufmann Publishers.
(PDF)
|
[57] |
Kenneth Basye and Thomas Dean.
Map learning with indistinguishable locations.
In Proceedings of the 5th Conference on Uncertainty in
Artificial Intelligence, pages 7–13. AUAI, 1989.
|
[58] |
Kenneth Basye and Thomas Dean.
Map learning with indistinguishable locations.
In Proceedings of the NASA Conference on Telerobotics, 1989.
|
[59] |
Akira Hayahsi and Thomas Dean.
Locating a mobile robot using local observations and a global
satellite map.
In Proceedings of the 3rd IEEE International Symposium on
Intelligent Control, pages 135–140. IEEE, 1988.
|
[60] |
Thomas Dean and Keiji Kanazawa.
Probabilistic causal reasoning.
In Proceedings of the Canadian Society for Computational Studies
of Intelligence, pages 125–132. CSCSI, 1988.
|
[61] |
Thomas Dean and Keiji Kanazawa.
Probabilistic temporal reasoning.
In Proceedings AAAI-88, pages 524–528, Cambridge,
Massachusetts, 1988. AAAI, MIT Press.
|
[62] |
Thomas Dean and Mark Boddy.
An analysis of time-dependent planning.
In Proceedings AAAI-88, pages 49–54, Cambridge,
Massachusetts, 1988. AAAI, MIT Press.
|
[63] |
Thomas Dean.
On the complexity of integrating spatial measurements.
In Proceedings of the SPIE Conference on Advances in
Intelligent Robotic Systems. SPIE, 1988.
|
[64] |
Thomas Dean and Mark Boddy.
Incremental causal reasoning.
In Proceedings AAAI-87, pages 196–201, Cambridge,
Massachusetts, 1987. AAAI, MIT Press.
|
[65] |
Thomas Dean.
High-level planning and low-level control.
In Proceedings of the SPIE Conference on Advances in
Intelligent Robotic Systems, pages 496–501. SPIE, 1987.
|
[66] |
Thomas Dean.
Benchmarks for research in planning.
In Proceedings of the 2nd Workshop on AI and Simulation,
1987.
|
[67] |
Thomas Dean.
Experiments in planning and control.
In Proceedings of the AAAI-87 Workshop on Planning for
Autonomous Mobile Robots, 1987.
|
[68] |
Thomas Dean.
Large-scale temporal databases for planning in complex domains.
In Proceedings IJCAI-87, pages 860–866, San Francisco,
California, 1987. IJCAI, Morgan Kaufmann Publishers.
|
[69] |
Thomas Dean.
Planning, execution, and control.
In Proceedings of the DARPA Knowledge-Based Planning
Workshop, pages 29:1–10. DARPA, 1987.
|
[70] |
Thomas Dean.
Planning paradigms.
In Proceedings of the DARPA Knowledge-Based Planning Workshop
(also appeared in the Proceedings of the 3rd Annual Workshop of Israel
Association for Artificial Intelligence, and in the AI Magazine, Fall
1988). DARPA, 1988.
|
[71] |
Thomas Dean.
Intractability and time-dependent planning.
In Michael P. Georgeff and Amy L. Lansky, editors, Reasoning
About Actions and Plans, pages 245–266. Morgan Kaufmann Publishers, San
Francisco, California, 1986.
|
[72] |
Thomas Dean.
Decision support for coordinated multi-agent planning.
In Proceedings of the 3rd International ACM Conference on
Office Information Systems, 1986.
|
[73] |
Yoav Shoham and Thomas Dean.
Temporal notation and causal terminology.
In Proceedings 7th Annual Conference of the Cognitive Science
Society, pages 90–99. Cognitive Science Society, 1985.
|
[74] |
David P. Miller, James R. Firby, and Thomas L. Dean.
Deadlines, travel time, and robot problem solving.
In Proceedings IJCAI-85, pages 1052–1054, San Francisco,
California, 1985. IJCAI, Morgan Kaufmann Publishers.
|
[75] |
R. James Firby, Thomas L. Dean, and David P. Miller.
Efficient robot planning with deadlines and travel time.
In Proceedings of the 6th International Symposium on Robotics
and Automation. IASTED, 1985.
|
[76] |
Thomas Dean.
Temporal reasoning involving counterfactuals and disjunctions.
In Proceedings IJCAI-85, pages 860–866, San Francisco,
California, 1985. IJCAI, Morgan Kaufmann Publishers.
|
[77] |
Thomas Dean.
Planning and temporal reasoning under uncertainty.
In Proceedings of the IEEE Workshop on Principles of
Knowledge-Based Systems, pages 131–138. IEEE, 1984.
|
[78] |
Thomas Dean.
Managing time maps.
In Proceedings of the Canadian Society for Computational Studies
of Intelligence. CSCSI, 1984.
|
Intractability and Time-Dependent Planning, in “Reasoning About Actions and Plans” edited by Amy Lansky and Mike Georgeff (Morgan Kaufmann Publishers), 1987.
Robot Problem Solving, in “Artificial Intelligence Techniques in Engineering” edited by Hojjat Adeli (McGraw-Hill), 1989.
Reinforcement Learning for Planning and Control (with Ken Basye and John Shewchuk), “Machine Learning Methods for Planning and Scheduling” edited by Steve Minton (Morgan Kaufmann Publishers), 1992.
Uncertainty in Graph-Based Map Learning (with Ken Basye and Leslie Kaelbling), “Robot Learning” edited by Jon Connell and Sridhar Mahadevan (Kluwer Academic Publishers), 1993.
AI Scheduling Methods, a review of “Intelligent Scheduling” by Monte Zweben and Mark Fox, in IEEE Expert 7 (1995).
Planning and Navigation in Stochastic Environments (with Jean-Luc Marion), “Visual Navigation” edited by Yiannis Aloimonos (Lawrence Erlbaum Publishers), 1996.
Planning and Scheduling (with Subbarao Kambhampati), “CRC Handbook of Computer Science and Engineering” edited by Allen Tucker and Hal Abelson (CRC Press) 1996.