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ORCIDDavid H. Wolpert
David Hilton Wolpert
Person information
- affiliation: Santa Fe Institute, NM, USA
- affiliation: Massachusetts Institute of Technology (MIT), Department of Aeronautics and Astronautics, Cambridge, MA, USA
- affiliation: Arizona State University, Center for Bio-social Complex Systems, Tempe, AZ, USA
- affiliation: Los Alamos National Laboratory, CCS-3, NM, USA
- affiliation: NASA Ames Research Center, Moffett Field, CA, USA
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Journal Articles
- 2023
[j39]Farita Tasnim, David H. Wolpert:
Stochastic Thermodynamics of Multiple Co-Evolving Systems - Beyond Multipartite Processes. Entropy 25(7): 1078 (2023)- 2022
- [j38]Timothy A. Kohler, Darcy Bird
, David H. Wolpert:
Social Scale and Collective Computation: Does Information Processing Limit Rate of Growth in Scale? J. Soc. Comput. 3(1): 1-17 (2022) - 2019
- [j37]David H. Wolpert, Justin Grana:
How Much Would You Pay to Change a Game before Playing It? Entropy 21(7): 686 (2019) - [j36]Artemy Kolchinsky, Brendan D. Tracey, David H. Wolpert:
Nonlinear Information Bottleneck. Entropy 21(12): 1181 (2019) - [j35]Kunal Menda, Yi-Chun Chen, Justin Grana, James W. Bono, Brendan D. Tracey, Mykel J. Kochenderfer, David H. Wolpert:
Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes. IEEE Trans. Intell. Transp. Syst. 20(4): 1259-1268 (2019) - 2018
- [j34]Joshua A. Grochow, David H. Wolpert:
Beyond Number of Bit Erasures: New Complexity Questions Raised by Recently Discovered Thermodynamic Costs of Computation. SIGACT News 49(2): 33-56 (2018) - 2017
- [j33]Johannes Rauh, Pradeep Kr. Banerjee, Eckehard Olbrich, Jürgen Jost, Nils Bertschinger, David H. Wolpert:
Coarse-Graining and the Blackwell Order. Entropy 19(10): 527 (2017) - 2016
- [j32]David H. Wolpert:
The Free Energy Requirements of Biological Organisms; Implications for Evolution. Entropy 18(4): 138 (2016) - [j31]David H. Wolpert:
Correction: Wolpert, D.H. The Free Energy Requirements of Biological Organisms; Implications for Evolution. Entropy 2016, 18, 138. Entropy 18(6): 219 (2016) - [j30]Justin Grana, David H. Wolpert, Joshua Neil, Dongping Xie, Tanmoy Bhattacharya, Russell Bent:
A likelihood ratio anomaly detector for identifying within-perimeter computer network attacks. J. Netw. Comput. Appl. 66: 166-179 (2016) - 2013
- [j29]David H. Wolpert, Simon DeDeo:
Estimating Functions of Distributions Defined over Spaces of Unknown Size. Entropy 15(11): 4668-4699 (2013) - [j28]David H. Wolpert, James W. Bono:
Predicting Behavior in Unstructured Bargaining with a Probability Distribution. J. Artif. Intell. Res. 46: 579-605 (2013) - [j27]David H. Wolpert, Gregory Benford:
The lesson of Newcomb's paradox. Synth. 190(9): 1637-1646 (2013) - [j26]Scott Backhaus, Russell Bent, James W. Bono, Ritchie Lee, Brendan D. Tracey, David H. Wolpert, Dongping Xie, Yildiray Yildiz:
Cyber-Physical Security: A Game Theory Model of Humans Interacting Over Control Systems. IEEE Trans. Smart Grid 4(4): 2320-2327 (2013) - [j25]David H. Wolpert:
Ubiquity symposium: Evolutionary computation and the processes of life: what the no free lunch theorems really mean: how to improve search algorithms. Ubiquity 2013(December): 2:1-2:15 (2013) - 2010
- [j24]David H. Wolpert:
Why income comparison is rational. Games Econ. Behav. 69(2): 458-474 (2010) - 2009
- [j23]David H. Wolpert:
Trembling hand perfection for mixed quantal/best response equilibria. Int. J. Game Theory 38(4): 539-551 (2009) - 2007
- [j22]David H. Wolpert, William G. Macready:
Using self-dissimilarity to quantify complexity. Complex. 12(3): 77-85 (2007) - 2006
- [j21]David H. Wolpert, Charlie E. M. Strauss, Dev G. Rajnarayan:
Advances in Distributed Optimization Using Probability Collectives. Adv. Complex Syst. 9(4): 383-436 (2006) - 2005
- [j20]David H. Wolpert, William G. Macready:
Coevolutionary free lunches. IEEE Trans. Evol. Comput. 9(6): 721-735 (2005) - 2002
- [j19]David H. Wolpert, Kagan Tumer:
Collective Intelligence, Data Routing and Braess' Paradox. J. Artif. Intell. Res. 16: 359-387 (2002) - 2001
- [j18]David H. Wolpert, Kagan Tumer:
Optimal Payoff Functions for Members of Collectives. Adv. Complex Syst. 4(2-3): 265-280 (2001) - [j17]Mario Köppen, David H. Wolpert, William G. Macready:
Remarks on a recent paper on the "no free lunch" theorems. IEEE Trans. Evol. Comput. 5(3): 295-296 (2001) - 1999
- [j16]David H. Wolpert, William G. Macready:
An Efficient Method To Estimate Bagging's Generalization Error. Mach. Learn. 35(1): 41-55 (1999) - [j15]Philip K. Chan, Salvatore J. Stolfo, David H. Wolpert:
Guest Editors' Introduction. Mach. Learn. 36(1-2): 5-7 (1999) - [j14]Padhraic Smyth, David H. Wolpert:
Linearly Combining Density Estimators via Stacking. Mach. Learn. 36(1-2): 59-83 (1999) - 1998
- [j13]David H. Wolpert, Emanuel Knill, Tal Grossman:
Some results concerning off-training-set and IID error for the Gibbs and the Bayes optimal generalizers. Stat. Comput. 8(1): 35-54 (1998) - [j12]William G. Macready, David H. Wolpert:
Bandit problems and the exploration/exploitation tradeoff. IEEE Trans. Evol. Comput. 2(1): 2-22 (1998) - 1997
- [j11]David H. Wolpert:
On Bias Plus Variance. Neural Comput. 9(6): 1211-1243 (1997) - [j10]David H. Wolpert, William G. Macready:
No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1): 67-82 (1997) - 1996
- [j9]William G. Macready, David H. Wolpert:
What makes an optimization problem hard? Complex. 1(5): 40-46 (1996) - [j8]David H. Wolpert:
The Lack of A Priori Distinctions Between Learning Algorithms. Neural Comput. 8(7): 1341-1390 (1996) - [j7]David H. Wolpert:
The Existence of A Priori Distinctions Between Learning Algorithms. Neural Comput. 8(7): 1391-1420 (1996) - 1992
- [j6]David H. Wolpert:
On the Connection between In-sample Testing and Generalization Error. Complex Syst. 6(1) (1992) - [j5]David H. Wolpert:
Stacked generalization. Neural Networks 5(2): 241-259 (1992) - 1990
- [j4]David H. Wolpert:
A Mathematical Theory of Generalization: Part I. Complex Syst. 4(2) (1990) - [j3]David H. Wolpert:
A Mathematical Theory of Generalization: Part II. Complex Syst. 4(2) (1990) - [j2]David H. Wolpert:
The Relationship Between Occam's Razor and Convergent Guessing. Complex Syst. 4(3) (1990) - [j1]David H. Wolpert:
Constructing a generalizer superior to NETtalk via a mathematical theory of generalization. Neural Networks 3(4): 445-452 (1990)
Conference and Workshop Papers
- 2016
- [c26]Rémi Lam, Karen Willcox, David H. Wolpert:
Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach. NIPS 2016: 883-891 - 2013
- [c25]David H. Wolpert, Dev G. Rajnarayan:
Using Machine Learning to Improve Stochastic Optimization. AAAI (Late-Breaking Developments) 2013 - 2012
- [c24]Guanhua Yan, Ritchie Lee, Alex Kent, David H. Wolpert:
Towards a bayesian network game framework for evaluating DDoS attacks and defense. CCS 2012: 553-566 - [c23]Erik J. Schlicht, Ritchie Lee, David H. Wolpert, Mykel J. Kochenderfer, Brendan D. Tracey:
Predicting the behavior of interacting humans by fusing data from multiple sources. UAI 2012: 756-765 - 2010
- [c22]David H. Wolpert:
Inference Concerning Physical Systems. CiE 2010: 438-447 - [c21]David H. Wolpert, James W. Bono:
PGT: A Statistical Approach to Prediction and Mechanism Design. SBP 2010: 314-322 - 2008
- [c20]David H. Wolpert, Nilesh V. Kulkarni:
Managing Multiple Interacting Adaptive Systems Via Game Theory. AHS 2008: 459-466 - 2005
- [c19]Chien-Feng Huang, Stefan Bieniawski, David H. Wolpert, Charlie E. M. Strauss:
A comparative study of probability collectives based multi-agent systems and genetic algorithms. GECCO 2005: 751-752 - 2004
- [c18]Chiu Fan Lee, David H. Wolpert:
Product Distribution Theory for Control of Multi-Agent Systems. AAMAS 2004: 522-529 - [c17]Stefan Bieniawski, David H. Wolpert:
Adaptive, Distributed Control of Constrained Multi-Agent Systems. AAMAS 2004: 1230-1231 - [c16]David H. Wolpert, Stefan Bieniawski:
Distributed control by Lagrangian steepest descent. CDC 2004: 1562-1567 - [c15]David H. Wolpert, Stefan Bieniawski:
Distributed Adaptive Control: Beyond Single-Instant, Discrete Control Variables. MSRAS 2004: 31-52 - 2003
- [c14]Stéphane Airiau, Sandip Sen, David H. Wolpert, Kagan Tumer:
Providing Effective Access to Shared Resources: A COIN Approach. Engineering Self-Organising Systems 2003: 249-264 - 2002
- [c13]John W. Lawson, David H. Wolpert:
The Design of Collectives of Agents to Control Non-Markovian Systems. AAAI/IAAI 2002: 332-337 - [c12]Kagan Tumer, Adrian K. Agogino, David H. Wolpert:
Learning sequences of actions in collectives of autonomous agents. AAMAS 2002: 378-385 - [c11]David H. Wolpert, John W. Lawson:
Designing agent collectives for systems with Markovian dynamics. AAMAS 2002: 1066-1073 - 2001
- [c10]David H. Wolpert, Joseph Sill, Kagan Tumer:
Reinforcement Learning in Distributed Domains: Beyond Team Games. IJCAI 2001: 819-824 - 2000
- [c9]Kagan Tumer, David H. Wolpert:
Collective Intelligence and Braess' Paradox. AAAI/IAAI 2000: 104-109 - [c8]David H. Wolpert, Sergey Kirshner, Christopher J. Merz, Kagan Tumer:
Adaptivity in agent-based routing for data networks. Agents 2000: 396-403 - 1999
- [c7]David H. Wolpert, Kevin R. Wheeler, Kagan Tumer:
General Principles of Learning-Based Multi-Agent Systems. Agents 1999: 77-83 - 1998
- [c6]David H. Wolpert, Kagan Tumer, Jeremy Frank:
Using Collective Intelligence to Route Internet Traffic. NIPS 1998: 952-960 - 1997
- [c5]Padhraic Smyth, David H. Wolpert:
Anytime Exploratory Data Analysis for Massive Data Sets. KDD 1997: 54-60 - [c4]Padhraic Smyth, David H. Wolpert:
Stacked Density Estimation. NIPS 1997: 668-674 - 1996
- [c3]Ron Kohavi, David H. Wolpert:
Bias Plus Variance Decomposition for Zero-One Loss Functions. ICML 1996: 275-283 - 1993
- [c2]David H. Wolpert:
Bayesian Backpropagation Over I-O Functions Rather Than Weights. NIPS 1993: 200-207 - 1992
- [c1]David H. Wolpert:
On the Use of Evidence in Neural Networks. NIPS 1992: 539-546
Parts in Books or Collections
- 2013
- [p1]Ritchie Lee, David H. Wolpert, James W. Bono, Scott Backhaus, Russell Bent, Brendan D. Tracey:
Counter-Factual Reinforcement Learning: How to Model Decision-Makers That Anticipate the Future. Decision Making and Imperfection 2013: 101-128
Editorship
- 2017
- [e3]Tatiana V. Guy, Miroslav Kárný, David Ríos Insua, David H. Wolpert:
Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers: Admitting Real-World Rationality, Barcelona, Spain, December 9, 2016. Proceedings of Machine Learning Research 58, PMLR 2017 [contents] - 2015
- [e2]Tatiana V. Guy, Miroslav Kárný, David H. Wolpert:
Decision Making: Uncertainty, Imperfection, Deliberation and Scalability. Studies in Computational Intelligence 538, Springer 2015, ISBN 978-3-319-15143-4 [contents] - 2013
- [e1]Tatiana V. Guy, Miroslav Kárný, David H. Wolpert:
Decision Making and Imperfection. Studies in Computational Intelligence 474, Springer 2013, ISBN 978-3-642-36405-1 [contents]
Reference Works
- 2017
- [r2]Dev G. Rajnarayan, David H. Wolpert:
Bias-Variance Trade-Offs: Novel Applications. Encyclopedia of Machine Learning and Data Mining 2017: 129-139 - 2010
- [r1]Dev G. Rajnarayan, David H. Wolpert:
Bias-Variance Trade-offs: Novel Applications. Encyclopedia of Machine Learning 2010: 101-110
Informal and Other Publications
- 2023
- [i55]Farita Tasnim, Nahuel Freitas, David H. Wolpert:
The fundamental thermodynamic costs of communication. CoRR abs/2302.04320 (2023) - [i54]Maria Alejandra Ramirez, Yoav Kolumbus, Rosemarie Nagel, David H. Wolpert, Jürgen Jost:
Game Manipulators - the Strategic Implications of Binding Contracts. CoRR abs/2311.10586 (2023) - [i53]David H. Wolpert, Jan Korbel, Christopher Lynn, Farita Tasnim, Joshua A. Grochow, Gülce Kardes, James B. Aimone, Vijay Balasubramanian, Eric De Giuli, David Doty, Nahuel Freitas, Matteo Marsili, Thomas E. Ouldridge, Andréa W. Richa, Paul M. Riechers, Édgar Roldán, Brenda M. Rubenstein, Zoltán Toroczkai, Joseph A. Paradiso:
Is stochastic thermodynamics the key to understanding the energy costs of computation? CoRR abs/2311.17166 (2023) - 2022
- [i52]David H. Wolpert:
What can we know about that which we cannot even imagine? CoRR abs/2208.03886 (2022) - 2021
- [i51]Artemy Kolchinsky, David H. Wolpert:
The state dependence of integrated, instantaneous, and fluctuating entropy production in quantum and classical processes. CoRR abs/2103.05734 (2021) - [i50]David H. Wolpert:
The Implications of the No-Free-Lunch Theorems for Meta-induction. CoRR abs/2103.11956 (2021) - [i49]David H. Wolpert, Michael H. Price, Stefani A. Crabtree, Timothy A. Kohler, Jürgen Jost, James Evans, Peter F. Stadler, Hajime Shimao, Manfred D. Laubichler:
The Past as a Stochastic Process. CoRR abs/2112.05876 (2021) - 2020
- [i48]David H. Wolpert:
Minimum entropy production in multipartite processes due to neighborhood constraints. CoRR abs/2001.02205 (2020) - [i47]David H. Wolpert:
What is important about the No Free Lunch theorems? CoRR abs/2007.10928 (2020) - [i46]Artemy Kolchinsky, David H. Wolpert:
Entropy production and thermodynamics of information under protocol constraints. CoRR abs/2008.10764 (2020) - [i45]David H. Wolpert, David Kinney:
Noisy Deductive Reasoning: How Humans Construct Math, and How Math Constructs Universes. CoRR abs/2012.08298 (2020) - 2019
- [i44]David H. Wolpert:
Stochastic thermodynamics of computation. CoRR abs/1905.05669 (2019) - [i43]Tom Conte, Erik DeBenedictis, Natesh Ganesh, Todd Hylton, John Paul Strachan, R. Stanley Williams, Alexander A. Alemi, Lee Altenberg, Gavin E. Crooks, James P. Crutchfield, Lídia del Rio, Josh Deutsch, Michael Robert DeWeese, Khari Douglas, Massimiliano Esposito, Michael P. Frank, Robert Fry, Peter Harsha, Mark D. Hill, Christopher T. Kello, Jeff Krichmar, Suhas Kumar, Shih-Chii Liu, Seth Lloyd, Matteo Marsili, Ilya Nemenman, Alex Nugent, Norman H. Packard, Dana Randall, Peter Sadowski, Narayana Santhanam, Robert Shaw, Adam Z. Stieg, Elan Stopnitzky, Christof Teuscher, Chris Watkins, David H. Wolpert, J. Joshua Yang, Yan Yufik:
Thermodynamic Computing. CoRR abs/1911.01968 (2019) - [i42]David H. Wolpert:
Uncertainty relations and fluctuation theorems for Bayes nets. CoRR abs/1911.02700 (2019) - 2018
- [i41]David Hilton Wolpert, Artemy Kolchinsky:
Exact, complete expressions for the thermodynamic costs of circuits. CoRR abs/1806.04103 (2018) - 2017
- [i40]Johannes Rauh, Pradeep Kr. Banerjee, Eckehard Olbrich, Jürgen Jost, Nils Bertschinger, David H. Wolpert:
Coarse-graining and the Blackwell order. CoRR abs/1701.07602 (2017) - [i39]David H. Wolpert, Justin Grana, Brendan D. Tracey, Tim Kohler, Artemy Kolchinsky:
Modeling Social Organizations as Communication Networks. CoRR abs/1702.04449 (2017) - [i38]Artemy Kolchinsky, Brendan D. Tracey, David H. Wolpert:
Nonlinear Information Bottleneck. CoRR abs/1705.02436 (2017) - [i37]David H. Wolpert, Artemy Kolchinsky, Jeremy A. Owen:
The minimal hidden computer needed to implement a visible computation. CoRR abs/1708.08494 (2017) - [i36]Jeremy A. Owen, Artemy Kolchinsky, David H. Wolpert:
Number of hidden states needed to physically implement a given conditional distribution. CoRR abs/1709.00765 (2017) - [i35]Kunal Menda, Yi-Chun Chen, Justin Grana, James W. Bono, Brendan D. Tracey, Mykel J. Kochenderfer, David H. Wolpert:
Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes. CoRR abs/1709.06656 (2017) - [i34]David H. Wolpert:
Constraints on physical reality arising from a formalization of knowledge. CoRR abs/1711.03499 (2017) - 2016
- [i33]Artemy Kolchinsky, David H. Wolpert:
Thermodynamic cost due to changing the initial distribution over states. CoRR abs/1607.00956 (2016) - [i32]Justin Grana, David H. Wolpert, Joshua Neil, Dongping Xie, Tanmoy Bhattacharya, Russell Bent:
A Likelihood Ratio Detector for Identifying Within-Perimeter Computer Network Attacks. CoRR abs/1609.00104 (2016) - 2014
- [i31]Nils Bertschinger, David H. Wolpert, Eckehard Olbrich, Jürgen Jost:
Information geometry of influence diagrams and noncooperative games. CoRR abs/1401.0001 (2014) - [i30]Erik J. Schlicht, Ritchie Lee, David H. Wolpert, Mykel J. Kochenderfer, Brendan D. Tracey:
Predicting the behavior of interacting humans by fusing data from multiple sources. CoRR abs/1408.2053 (2014) - [i29]David H. Wolpert, Joshua A. Grochow, Eric Libby, Simon DeDeo:
A framework for optimal high-level descriptions in science and engineering - preliminary report. CoRR abs/1409.7403 (2014) - 2013
- [i28]Scott Backhaus, Russell Bent, James W. Bono, Ritchie Lee, Brendan D. Tracey, David H. Wolpert, Dongping Xie, Yildiray Yildiz:
Cyber-Physical Security: A Game Theory Model of Humans Interacting over Control Systems. CoRR abs/1304.3996 (2013) - 2012
- [i27]Erik J. Schlicht, Ritchie Lee, David H. Wolpert, Mykel J. Kochenderfer, Brendan D. Tracey:
Predicting the behavior of interacting humans by fusing data from multiple sources. CoRR abs/1206.6080 (2012) - [i26]Ritchie Lee, David H. Wolpert, James W. Bono, Scott Backhaus, Russell Bent, Brendan D. Tracey:
Counter-Factual Reinforcement Learning: How to Model Decision-Makers That Anticipate The Future. CoRR abs/1207.0852 (2012) - 2011
- [i25]Ritchie Lee, David H. Wolpert:
Game theoretic modeling of pilot behavior during mid-air encounters. CoRR abs/1103.5169 (2011) - [i24]Kagan Tumer, David H. Wolpert:
Collective Intelligence, Data Routing and Braess' Paradox. CoRR abs/1106.1821 (2011) - [i23]Brendan D. Tracey, David H. Wolpert, Juan J. Alonso:
Using Supervised Learning to Improve Monte Carlo Integral Estimation. CoRR abs/1108.4879 (2011) - 2010
- [i22]David H. Wolpert, Gregory Benford:
What does Newcomb's paradox teach us? CoRR abs/1003.1343 (2010) - [i21]David H. Wolpert, Michael Harré, Eckehard Olbrich, Nils Bertschinger, Jürgen Jost:
Hysteresis effects of changing parameters of noncooperative games. CoRR abs/1010.5749 (2010) - 2009
- [i20]David H. Wolpert, Gregory Benford:
What does Newcomb's paradox teach us? CoRR abs/0904.2540 (2009) - 2008
- [i19]Dev G. Rajnarayan, David H. Wolpert:
Bias-Variance Techniques for Monte Carlo Optimization: Cross-validation for the CE Method. CoRR abs/0810.0877 (2008) - [i18]William G. Macready, David H. Wolpert:
Distributed Constrained Optimization with Semicoordinate Transformations. CoRR abs/0811.0823 (2008) - 2007
- [i17]David H. Wolpert, Dev G. Rajnarayan:
Parametric Learning and Monte Carlo Optimization. CoRR abs/0704.1274 (2007) - [i16]David H. Wolpert:
Physical limits of inference. CoRR abs/0708.1362 (2007) - 2005
- [i15]David H. Wolpert:
A Predictive Theory of Games. CoRR abs/nlin/0512015 (2005) - 2004
- [i14]David H. Wolpert:
Information Theory - The Bridge Connecting Bounded Rational Game Theory and Statistical Physics. CoRR cond-mat/0402508 (2004) - [i13]David H. Wolpert, Stefan Bieniawski:
Distributed Control by Lagrangian Steepest Descent. CoRR cs.MA/0403012 (2004) - [i12]David H. Wolpert:
Metrics for more than two points at once. CoRR nlin.AO/0404032 (2004) - 2003
- [i11]Kagan Tumer, David H. Wolpert:
Collectives for the Optimal Combination of Imperfect Objects. CoRR cond-mat/0301459 (2003) - [i10]David H. Wolpert:
Product Distribution Field Theory. CoRR cond-mat/0307630 (2003) - [i9]David H. Wolpert, Kagan Tumer, Esfandiar Bandari:
Improving Search Algorithms by Using Intelligent Coordinates. CoRR math.OC/0301268 (2003) - 2000
- [i8]David H. Wolpert:
On the computational capabilities of physical systems part I: the impossibility of infallible computation. CoRR physics/0005058 (2000) - [i7]David H. Wolpert:
On the computational capabilities of physical systems part II: relationship with conventional computer science. CoRR physics/0005059 (2000) - 1999
- [i6]Kagan Tumer, David H. Wolpert:
Avoiding Braess' Paradox through Collective Intelligence. CoRR cs.DC/9912012 (1999) - [i5]David H. Wolpert, Kagan Tumer, Jeremy Frank:
Using Collective Intelligence to Route Internet Traffic. CoRR cs.LG/9905004 (1999) - [i4]David H. Wolpert, Kevin R. Wheeler, Kagan Tumer:
Collective Intelligence for Control of Distributed Dynamical Systems. CoRR cs.LG/9908013 (1999) - [i3]David H. Wolpert, Kagan Tumer:
An Introduction to Collective Intelligence. CoRR cs.LG/9908014 (1999) - [i2]David H. Wolpert, Kevin R. Wheeler, Kagan Tumer:
General Principles of Learning-Based Multi-Agent Systems. CoRR cs.MA/9905005 (1999) - [i1]David H. Wolpert, Sergey Kirshner, Christopher J. Merz, Kagan Tumer:
Adaptivity in Agent-Based Routing for Data Networks. CoRR cs.MA/9912011 (1999)
Coauthor Index
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