default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDKagan Tumer
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c133]Ayhan Alp Aydeniz, Enrico Marchesini, Christopher Amato, Kagan Tumer:
Entropy Seeking Constrained Multiagent Reinforcement Learning. AAMAS 2024: 2141-2143- [c132]Andrew Festa, Gaurav Dixit, Kagan Tumer:
Influence-Focused Asymmetric Island Model. AAMAS 2024: 2261-2263 - [c131]Everardo Gonzalez, Siddarth Viswanathan, Kagan Tumer:
Indirect Credit Assignment in a Multiagent System. AAMAS 2024: 2288-2290 - [c130]Joshua Cook
, Kagan Tumer:
Learning Aligned Local Evaluations For Better Credit Assignment In Cooperative Coevolution. GECCO 2024 - [c129]Joshua Cook, Kagan Tumer:
Multidimensional Archive Of The State Space. GECCO Companion 2024: 267-270 - [c128]Gaurav Dixit, Kagan Tumer:
Informed Diversity Search for Learning in Asymmetric Multiagent Systems. GECCO 2024 - [c127]Andrew Festa, Gaurav Dixit, Kagan Tumer:
Reinforcing Inter-Class Dependencies in the Asymmetric Island Model. GECCO 2024 - [c126]Everardo Gonzalez, Siddarth Viswanathan, Kagan Tumer:
Influence Based Fitness Shaping for Coevolutionary Agents. GECCO 2024 - [c125]Anna Nickelson, Kagan Tumer:
Redefining the Behavior Space for Multi-Objective MAP-Elites. GECCO Companion 2024: 355-358 - 2023
- [c124]Gaurav Dixit, Kagan Tumer:
Learning Inter-Agent Synergies in Asymmetric Multiagent Systems. AAMAS 2023: 1569-1577 - [c123]Joshua Cook, Tristan Scheiner, Kagan Tumer:
Multi-Team Fitness Critics For Robust Teaming. AAMAS 2023: 2406-2408 - [c122]Nicholas Zerbel, Kagan Tumer:
Knowledge Injection for Multiagent Systems via Counterfactual Perception Shaping. ECAI 2023: 2978-2985 - [c121]Connor Yates, Kagan Tumer:
Knowledge Shaped Behavior Generation for Online Adaptation. GECCO Companion 2023: 343-346 - [c120]Ayhan Alp Aydeniz, Robert Tyler Loftin, Kagan Tumer:
Novelty Seeking Multiagent Evolutionary Reinforcement Learning. GECCO 2023: 402-410 - [c119]Joshua Cook, Kagan Tumer, Tristan Scheiner:
Leveraging Fitness Critics To Learn Robust Teamwork. GECCO 2023: 429-437 - [c118]Gaurav Dixit, Kagan Tumer:
Learning Synergies for Multi-Objective Optimization in Asymmetric Multiagent Systems. GECCO 2023: 447-455 - [c117]Anna Nickelson, Kagan Tumer, William D. Smart:
Contextual Multi-Objective Path Planning. ICRA 2023: 10240-10246 - [c116]Anna Nickelson, Nicholas Zerbel, Gaurav Dixit, Kagan Tumer:
Shaping the Behavior Space with Counterfactual Agents in Multi-Objective Map Elites. IJCCI 2023: 41-52 - [c115]Ayhan Alp Aydeniz, Enrico Marchesini, Robert Tyler Loftin, Kagan Tumer:
Entropy Maximization in High Dimensional Multiagent State Spaces. MRS 2023: 92-99 - 2022
- [c114]Gaurav Dixit, Kagan Tumer:
Behavior Exploration and Team Balancing for Heterogeneous Multiagent Coordination. AAMAS 2022: 1578-1579 - [c113]Ayhan Alp Aydeniz, Anna Nickelson, Kagan Tumer:
Entropy-based local fitnesses for evolutionary multiagent systems. GECCO Companion 2022: 212-215 - [c112]Gaurav Dixit, Kagan Tumer:
Balancing teams with quality-diversity for heterogeneous multiagent coordination. GECCO Companion 2022: 236-239 - [c111]Golden Rockefeller, Kagan Tumer:
Bootstrapped fitness critics with bidirectional temporal difference. GECCO Companion 2022: 280-283 - [c110]Joshua Cook, Kagan Tumer:
Fitness shaping for multiple teams. GECCO 2022: 332-340 - [c109]Gaurav Dixit, Everardo Gonzalez, Kagan Tumer:
Diversifying behaviors for learning in asymmetric multiagent systems. GECCO 2022: 350-358 - 2021
- [c108]Enna Sachdeva, Shauharda Khadka, Somdeb Majumdar, Kagan Tumer:
Dynamic Skill Selection for Learning Joint Actions. AAMAS 2021: 1637-1639 - [c107]Joshua Cook, Kagan Tumer:
Ad hoc teaming through evolution. GECCO Companion 2021: 89-90 - [c106]Gaurav Dixit, Charles Koll, Kagan Tumer:
Heterogeneous agent coordination via adaptive quality diversity and specialization. GECCO Companion 2021: 95-96 - [c105]Enna Sachdeva, Shauharda Khadka, Somdeb Majumdar, Kagan Tumer:
MAEDyS: multiagent evolution via dynamic skill selection. GECCO 2021: 163-171 - [c104]Connor Yates, Ayhan Alp Aydeniz, Kagan Tumer:
Adaptive multi-fitness learning for robust coordination. GECCO Companion 2021: 175-176 - [c103]Connor Yates, Ayhan Alp Aydeniz, Kagan Tumer:
Reactive Multi-Fitness Learning for Robust Multiagent Teaming. MRS 2021: 92-100 - 2020
- [j43]Jen Jen Chung, Damjan Miklic, Lorenzo Sabattini, Kagan Tumer, Roland Siegwart:
The impact of agent definitions and interactions on multiagent learning for coordination in traffic management domains. Auton. Agents Multi Agent Syst. 34(1): 21 (2020) - [c102]Gaurav Dixit, Stéphane Airiau, Kagan Tumer:
Gaussian Processes as Multiagent Reward Models. AAMAS 2020: 330-338 - [c101]Golden Rockefeller, Shauharda Khadka, Kagan Tumer:
Multi-level Fitness Critics for Cooperative Coevolution. AAMAS 2020: 1143-1151 - [c100]Nicholas Zerbel, Kagan Tumer:
The Power of Suggestion. AAMAS 2020: 1602-1610 - [c99]Connor Yates, Reid Christopher, Kagan Tumer:
Multi-fitness learning for behavior-driven cooperation. GECCO 2020: 453-461 - [c98]Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen McAleer, Kagan Tumer:
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination. ICML 2020: 6651-6660
2010 – 2019
- 2019
- [j42]Daniel E. Hulse, Kagan Tumer, Christopher Hoyle, Irem Y. Tumer:
Modeling multidisciplinary design with multiagent learning. Artif. Intell. Eng. Des. Anal. Manuf. 33(1): 85-99 (2019) - [j41]Jen Jen Chung, Carrie Rebhuhn, Connor Yates, Geoffrey A. Hollinger, Kagan Tumer:
A multiagent framework for learning dynamic traffic management strategies. Auton. Robots 43(6): 1375-1391 (2019) - [j40]Shauharda Khadka, Jen Jen Chung, Kagan Tumer:
Neuroevolution of a Modular Memory-Augmented Neural Network for Deep Memory Problems. Evol. Comput. 27(4): 639-664 (2019) - [c97]Jen Jen Chung, Damjan Miklic, Lorenzo Sabattini, Kagan Tumer, Roland Siegwart:
The Impact of Agent Definitions and Interactions on Multiagent Learning for Coordination. AAMAS 2019: 1752-1760 - [c96]Shauharda Khadka, Connor Yates, Kagan Tumer:
Memory based Multiagent One Shot Learning. AAMAS 2019: 2054-2056 - [c95]Golden Rockefeller, Patrick Mannion, Kagan Tumer:
Curriculum Learning for Tightly Coupled Multiagent Systems. AAMAS 2019: 2174-2176 - [c94]Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer:
Collaborative Evolutionary Reinforcement Learning. ICML 2019: 3341-3350 - [c93]Shauharda Khadka, Connor Yates, Kagan Tumer:
Memory-Based Multiagent One-Shot Learning: Extended Abstract. MRS 2019: 145-147 - [c92]Gaurav Dixit, Nicholas Zerbel, Kagan Tumer:
Dirichlet-Multinomial Counterfactual Rewards for Heterogeneous Multiagent Systems. MRS 2019: 209-215 - [c91]Golden Rockefeller, Patrick Mannion, Kagan Tumer:
Fitness Critics for Multiagent Learning: Extended Abstract. MRS 2019: 222-224 - [i15]Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer:
Collaborative Evolutionary Reinforcement Learning. CoRR abs/1905.00976 (2019) - [i14]Shauharda Khadka, Somdeb Majumdar, Kagan Tumer:
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination. CoRR abs/1906.07315 (2019) - 2018
- [j39]Nicolás F. Soria Zurita, Mitchell K. Colby, Irem Y. Tumer, Christopher Hoyle, Kagan Tumer:
Design of Complex Engineered Systems Using Multi-Agent Coordination. J. Comput. Inf. Sci. Eng. 18(1) (2018) - [c90]Jen Jen Chung, Scott Chow, Kagan Tumer:
When Less is More: Reducing Agent Noise with Probabilistically Learning Agents. AAMAS 2018: 1900-1902 - [c89]Shauharda Khadka, Connor Yates, Kagan Tumer:
A Memory-based Multiagent Framework for Adaptive Decision Making. AAMAS 2018: 1977-1979 - [c88]Shauharda Khadka, Kagan Tumer:
Evolution-Guided Policy Gradient in Reinforcement Learning. NeurIPS 2018: 1196-1208 - [i13]Shauharda Khadka, Kagan Tumer:
Evolutionary Reinforcement Learning. CoRR abs/1805.07917 (2018) - 2017
- [j38]Mitchell K. Colby, Kagan Tumer:
Fitness function shaping in multiagent cooperative coevolutionary algorithms. Auton. Agents Multi Agent Syst. 31(2): 179-206 (2017) - [c87]Shauharda Khadka, Jen Jen Chung, Kagan Tumer:
Evolving memory-augmented neural architecture for deep memory problems. GECCO 2017: 441-448 - [c86]Shauharda Khadka, Jen Jen Chung, Kagan Tumer:
Memory-augmented multi-robot teams that learn to adapt. MRS 2017: 128-134 - [c85]Callie Branyan, Chloe Fleming, Jacquelin Remaley, Ammar Kothari, Kagan Tumer, Ross L. Hatton, Yigit Mengüç:
Soft snake robots: Mechanical design and geometric gait implementation. ROBIO 2017: 282-289 - 2016
- [j37]Mitchell K. Colby, Logan Michael Yliniemi, Kagan Tumer:
Autonomous Multiagent Space Exploration with High-Level Human Feedback. J. Aerosp. Inf. Syst. 13(8): 301-315 (2016) - [j36]Chris HolmesParker, Adrian K. Agogino, Kagan Tumer:
Combining reward shaping and hierarchies for scaling to large multiagent systems. Knowl. Eng. Rev. 31(1): 3-18 (2016) - [j35]Logan Michael Yliniemi, Kagan Tumer:
Multi-objective multiagent credit assignment in reinforcement learning and NSGA-II. Soft Comput. 20(10): 3869-3887 (2016) - [c84]Logan Michael Yliniemi, Kagan Tumer:
Using Awareness to Promote Richer, More Human-Like Behaviors in Artificial Agents. AAMAS Workshops (Visionary Papers) 2016: 122-133 - [c83]Mitchell K. Colby, Theodore Duchow-Pressley, Jen Jen Chung, Kagan Tumer:
Local Approximation of Difference Evaluation Functions. AAMAS 2016: 521-529 - [c82]Mitchell K. Colby, Logan Michael Yliniemi, Paolo Pezzini, David Tucker, Kenneth Mark Bryden, Kagan Tumer:
Multiobjective Neuroevolutionary Control for a Fuel Cell Turbine Hybrid Energy System. GECCO 2016: 877-884 - [c81]Shauharda Khadka, Kagan Tumer, Mitchell K. Colby, Dave Tucker, Paolo Pezzini, Kenneth Mark Bryden:
Neuroevolution of a Hybrid Power Plant Simulator. GECCO 2016: 917-924 - [c80]Aida Rahmattalabi, Jen Jen Chung, Mitchell K. Colby, Kagan Tumer:
D++: Structural credit assignment in tightly coupled multiagent domains. IROS 2016: 4424-4429 - 2015
- [j34]Atil Iscen, Ken Caluwaerts, Jonathan Bruce, Adrian K. Agogino, Vytas SunSpiral, Kagan Tumer:
Learning Tensegrity Locomotion Using Open-Loop Control Signals and Coevolutionary Algorithms. Artif. Life 21(2): 119-140 (2015) - [j33]Logan Michael Yliniemi, Adrian K. Agogino, Kagan Tumer:
Simulation of the introduction of new technologies in air traffic management. Connect. Sci. 27(3): 269-287 (2015) - [c79]Mitchell K. Colby, Sepideh Kharaghani, Chris HolmesParker, Kagan Tumer:
Counterfactual Exploration for Improving Multiagent Learning. AAMAS 2015: 171-179 - [c78]Logan Michael Yliniemi, Drew Wilson, Kagan Tumer:
Multi-Objective Multiagent Credit Assignment in NSGA-II Using Difference Evaluations. AAMAS 2015: 1635-1636 - [c77]Mitchell K. Colby, William J. Curran, Kagan Tumer:
Approximating Difference Evaluations with Local Information. AAMAS 2015: 1659-1660 - [c76]Mitchell K. Colby, Kagan Tumer:
A Replicator Dynamics Analysis of Difference Evaluation Functions. AAMAS 2015: 1661-1662 - [c75]Mitchell K. Colby, Kagan Tumer:
An Evolutionary Game Theoretic Analysis of Difference Evaluation Functions. GECCO 2015: 1391-1398 - [c74]Andrew Gabler, Mitchell K. Colby, Kagan Tumer:
Learning Based Control of a Fuel Cell Turbine Hybrid Power System. GECCO (Companion) 2015: 1393-1394 - [c73]Logan Michael Yliniemi, Kagan Tumer:
Complete Multi-Objective Coverage with PaCcET. GECCO (Companion) 2015: 1525-1526 - [c72]Carrie Rebhuhn, Ryan Skeele, Jen Jen Chung, Geoffrey A. Hollinger, Kagan Tumer:
Learning to trick cost-based planners into cooperative behavior. IROS 2015: 4627-4633 - [c71]Mitchell K. Colby, Jen Jen Chung, Kagan Tumer:
Implicit adaptive multi-robot coordination in dynamic environments. IROS 2015: 5168-5173 - 2014
- [j32]Logan Michael Yliniemi, Adrian K. Agogino, Kagan Tumer:
Multirobot Coordination for Space Exploration. AI Mag. 35(4): 61-74 (2014) - [c70]Mitchell K. Colby, Matt Knudson, Kagan Tumer:
Multiagent Flight Control in Dynamic Environments with Cooperative Coevolutionary Algorithms. AAAI Spring Symposia 2014 - [c69]Carrie Rebhuhn, Matt Knudson, Kagan Tumer:
Announced Strategy Types in Multiagent RL for Conflict-Avoidance in the National Airspace. AAAI Spring Symposia 2014 - [c68]Sam Devlin, Logan Michael Yliniemi, Daniel Kudenko, Kagan Tumer:
Potential-based difference rewards for multiagent reinforcement learning. AAMAS 2014: 165-172 - [c67]Chris HolmesParker, Matthew E. Taylor, Adrian K. Agogino, Kagan Tumer:
CLEANing the reward: counterfactual actions to remove exploratory action noise in multiagent learning (extended abstract). AAMAS 2014: 1353-1354 - [c66]William J. Curran, Adrian K. Agogino, Kagan Tumer:
Using reward/utility based impact scores in partitioning. AAMAS 2014: 1563-1564 - [c65]Mitchell K. Colby, William J. Curran, Carrie Rebhuhn, Kagan Tumer:
Approximating difference evaluations with local knowledge. AAMAS 2014: 1577-1578 - [c64]William J. Curran, Adrian K. Agogino, Kagan Tumer:
Hierarchical simulation for complex domains: air traffic flow management. GECCO 2014: 1087-1094 - [c63]Logan Michael Yliniemi, Adrian K. Agogino, Kagan Tumer:
Evolutionary agent-based simulation of the introduction of new technologies in air traffic management. GECCO 2014: 1215-1222 - [c62]Atil Iscen, Adrian K. Agogino, Vytas SunSpiral, Kagan Tumer:
Flop and roll: Learning robust goal-directed locomotion for a Tensegrity Robot. IROS 2014: 2236-2243 - [c61]Logan Michael Yliniemi, Kagan Tumer:
PaCcET: An Objective Space Transformation to Iteratively Convexify the Pareto Front. SEAL 2014: 204-215 - [c60]Logan Michael Yliniemi, Kagan Tumer:
Multi-objective Multiagent Credit Assignment Through Difference Rewards in Reinforcement Learning. SEAL 2014: 407-418 - [c59]Chris HolmesParker, Matthew E. Taylor, Adrian K. Agogino, Kagan Tumer:
CLEAN Rewards to Improve Coordination by Removing Exploratory Action Noise. WI-IAT (3) 2014: 127-134 - 2013
- [j31]Kagan Tumer, Scott Proper:
Coordinating actions in congestion games: impact of top-down and bottom-up utilities. Auton. Agents Multi Agent Syst. 27(3): 419-443 (2013) - [j30]Matt Knudson, Kagan Tumer:
Dynamic Partnership Formation for Multi-Rover Coordination. Adv. Complex Syst. 16(1) (2013) - [j29]Jaime Junell, Kagan Tumer:
Robust predictive cruise control for commercial vehicles. Int. J. Gen. Syst. 42(7): 776-792 (2013) - [j28]Max Salichon, Kagan Tumer:
A neuro-evolutionary approach to control surface segmentation for micro aerial vehicles. Int. J. Gen. Syst. 42(7): 793-805 (2013) - [j27]MohammadJavad NoroozOliaee, Bechir Hamdaoui, Kagan Tumer:
Efficient Objective Functions for Coordinated Learning in Large-Scale Distributed OSA Systems. IEEE Trans. Mob. Comput. 12(5): 931-944 (2013) - [c58]Scott Proper, Kagan Tumer:
Multiagent Learning with a Noisy Global Reward Signal. AAAI 2013: 826-832 - [c57]Logan Michael Yliniemi, Kagan Tumer:
Elo Ratings for Structural Credit Assignment in Multiagent Systems. AAAI (Late-Breaking Developments) 2013 - [c56]Mitchell K. Colby, Kagan Tumer:
Multiagent reinforcement learning in a distributed sensor network with indirect feedback. AAMAS 2013: 941-948 - [c55]Chris HolmesParker, Adrian K. Agogino, Kagan Tumer:
CLEAN rewards for improving multiagent coordination in the presence of exploration. AAMAS 2013: 1113-1114 - [c54]Chris HolmesParker, Adrian K. Agogino, Kagan Tumer:
Exploiting structure and utilizing agent-centric rewards to promote coordination in large multiagent systems. AAMAS 2013: 1181-1182 - [c53]Atil Iscen, Adrian K. Agogino, Vytas SunSpiral, Kagan Tumer:
Learning to control complex tensegrity robots. AAMAS 2013: 1193-1194 - [c52]Atil Iscen, Kagan Tumer:
Decentralized coordination via task decomposition and reward shaping. AAMAS 2013: 1269-1270 - [c51]Scott Proper, Kagan Tumer:
Graphical models in continuous domains for multiagent reinforcement learning. AAMAS 2013: 1277-1278 - [c50]William J. Curran, Adrian K. Agogino, Kagan Tumer:
Addressing hard constraints in the air traffic problem through partitioning and difference rewards. AAMAS 2013: 1281-1282 - [c49]William J. Curran, Adrian K. Agogino, Kagan Tumer:
Partitioning agents and shaping their evaluation functions in air traffic problems with hard constraints. GECCO (Companion) 2013: 183-184 - [c48]Atil Iscen, Adrian K. Agogino, Vytas SunSpiral, Kagan Tumer:
Controlling tensegrity robots through evolution. GECCO 2013: 1293-1300 - 2012
- [j26]Adrian K. Agogino, Kagan Tumer:
A multiagent approach to managing air traffic flow. Auton. Agents Multi Agent Syst. 24(1): 1-25 (2012) - [j25]Liz Sonenberg, Peter Stone, Kagan Tumer, Pinar Yolum:
Ten Years of AAMAS: Introduction to the Special Issue. AI Mag. 33(3): 11-13 (2012) - [j24]Ehsan M. Nasroullahi, Kagan Tumer:
Combining coordination mechanisms to improve performance in multi-robot teams. Artif. Intell. Res. 1(2): 1-10 (2012) - [j23]Bechir Hamdaoui, MohammadJavad NoroozOliaee, Kagan Tumer, Ammar Rayes:
Coordinating Secondary-User Behaviors for Inelastic Traffic Reward Maximization in Large-Scale \osa Networks. IEEE Trans. Netw. Serv. Manag. 9(4): 501-513 (2012) - [j22]Max Salichon, Kagan Tumer:
Evolving a Multiagent Controller for Micro Aerial Vehicles. IEEE Trans. Syst. Man Cybern. Part C 42(6): 1772-1783 (2012) - [c47]Mitchell K. Colby, Kagan Tumer:
Shaping fitness functions for coevolving cooperative multiagent systems. AAMAS 2012: 425-432 - [c46]Scott Proper, Kagan Tumer:
Modeling difference rewards for multiagent learning. AAMAS 2012: 1397-1398 - [c45]Adrian K. Agogino, Chris HolmesParker, Kagan Tumer:
Evolving distributed resource sharing for cubesat constellations. GECCO 2012: 1015-1022 - [c44]Adrian K. Agogino, Chris HolmesParker, Kagan Tumer:
Evolving large scale UAV communication system. GECCO 2012: 1023-1030 - [c43]Matt Knudson, Kagan Tumer:
Policy transfer in mobile robots using neuro-evolutionary navigation. GECCO (Companion) 2012: 1411-1412 - 2011
- [j21]Matt Knudson, Kagan Tumer:
Adaptive navigation for autonomous robots. Robotics Auton. Syst. 59(6): 410-420 (2011) - [c42]Christian Roth, Matt Knudson, Kagan Tumer:
Agent fitness functions for evolving coordinated sensor networks. GECCO 2011: 275-282 - [c41]Mitchell K. Colby, Ehsan M. Nasroullahi, Kagan Tumer:
Optimizing ballast design of wave energy converters using evolutionary algorithms. GECCO 2011: 1739-1746 - [c40]Bechir Hamdaoui, MohammadJavad NoroozOliaee, Kagan Tumer, Ammar Rayes:
Aligning Spectrum-User Objectives for Maximum Inelastic-Traffic Reward. ICCCN 2011: 1-6 - [c39]MohammadJavad NoroozOliaee, Bechir Hamdaoui, Kagan Tumer:
Achieving optimal elastic traffic rewards in dynamic multichannel access. HPCS 2011: 155-161 - [e1]Liz Sonenberg, Peter Stone, Kagan Tumer, Pinar Yolum:
10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), Taipei, Taiwan, May 2-6, 2011, Volume 1-3. IFAAMAS 2011, ISBN 978-0-9826571-5-7 [contents] - [i12]Kagan Tumer, David H. Wolpert:
Collective Intelligence, Data Routing and Braess' Paradox. CoRR abs/1106.1821 (2011) - 2010
- [j20]Adrian K. Agogino, Kagan Tumer:
A Multiagent Coordination Approach to Robust Consensus Clustering. Adv. Complex Syst. 13(2): 165-197 (2010) - [c38]Matt Knudson, Kagan Tumer:
Robot coordination with ad-hoc team formation. AAMAS 2010: 1441-1442 - [c37]Matt Knudson, Kagan Tumer:
Coevolution of heterogeneous multi-robot teams. GECCO 2010: 127-134 - [c36]Max Salichon, Kagan Tumer:
A neuro-evolutionary approach to micro aerial vehicle control. GECCO 2010: 1123-1130 - [c35]Jack F. Shepherd III, Kagan Tumer:
Robust neuro-control for a micro quadrotor. GECCO 2010: 1131-1138
2000 – 2009
- 2009
- [j19]Kagan Tumer, Newsha Khani:
Learning from Actions not Taken in Multiagent Systems. Adv. Complex Syst. 12(4-5): 455-473 (2009) - [j18]Kagan Tumer, Adrian K. Agogino:
Multiagent Learning for Black Box System Reward Functions. Adv. Complex Syst. 12(4-5): 475-492 (2009) - [j17]Adrian K. Agogino, Kagan Tumer:
Learning Indirect Actions in Complex Domains: Action Suggestions for Air Traffic Control. Adv. Complex Syst. 12(4-5): 493-512 (2009) - [j16]Kagan Tumer, Adrian K. Agogino:
Improving Air Traffic Management with a Learning Multiagent System. IEEE Intell. Syst. 24(1): 18-21 (2009) - [c34]Kagan Tumer, John W. Lawson:
Coordinating Learning Agents for Multiple Resource Job Scheduling. ALA 2009: 123-140 - [c33]Adrian K. Agogino, Kagan Tumer:
Improving air traffic management through agent suggestions. AAMAS (2) 2009: 1271-1272 - [c32]Newsha Khani, Kagan Tumer:
Learning from actions not taken: a multiagent learning algorithm. AAMAS (2) 2009: 1277-1278 - [p2]Kagan Tumer, Zachary T. Welch, Adrian K. Agogino:
Traffic Congestion Management as a Learning Agent Coordination Problem. Multi-Agent Systems for Traffic and Transportation Engineering 2009: 261-279 - 2008
- [j15]Adrian K. Agogino, Kagan Tumer:
Analyzing and visualizing multiagent rewards in dynamic and stochastic domains. Auton. Agents Multi Agent Syst. 17(2): 320-338 (2008) - [j14]Adrian K. Agogino, Kagan Tumer:
Efficient Evaluation Functions for Evolving Coordination. Evol. Comput. 16(2): 257-288 (2008) - [j13]Nikunj C. Oza, Kagan Tumer:
Applications of ensemble methods. Inf. Fusion 9(1): 2-3 (2008) - [j12]Nikunj C. Oza, Kagan Tumer:
Classifier ensembles: Select real-world applications. Inf. Fusion 9(1): 4-20 (2008) - [j11]Kagan Tumer, Adrian K. Agogino:
Ensemble clustering with voting active clusters. Pattern Recognit. Lett. 29(14): 1947-1953 (2008) - [c31]Kagan Tumer, Adrian K. Agogino:
Adaptive Management of Air Traffic Flow: A Multiagent Coordination Approach. AAAI 2008: 1581-1584 - [c30]Adrian K. Agogino, Kagan Tumer:
Regulating air traffic flow with coupled agents. AAMAS (2) 2008: 535-542 - [c29]Kagan Tumer, Zachary T. Welch, Adrian K. Agogino:
Aligning social welfare and agent preferences to alleviate traffic congestion. AAMAS (2) 2008: 655-662 - 2007
- [c28]Kagan Tumer, Adrian K. Agogino:
Distributed agent-based air traffic flow management. AAMAS 2007: 255 - [c27]Adrian K. Agogino, Kagan Tumer:
Evolving distributed agents for managing air traffic. GECCO 2007: 1888-1895 - [p1]Kagan Tumer, Adrian K. Agogino:
Evolving Multi Rover Systems in Dynamic and Noisy Environments. Evolutionary Computation in Dynamic and Uncertain Environments 2007: 371-387 - 2006
- [j10]Adrian K. Agogino, Kagan Tumer:
Handling Communication Restrictions and Team Formation in Congestion Games. Auton. Agents Multi Agent Syst. 13(1): 97-115 (2006) - [c26]Adrian K. Agogino, Kagan Tumer:
QUICR-Learning for Multi-Agent Coordination. AAAI 2006: 1438-1443 - [c25]Nachi Gupta, Adrian K. Agogino, Kagan Tumer:
Efficient agent-based models for non-genomic evolution. AAMAS 2006: 58-64 - [c24]Adrian K. Agogino, Kagan Tumer:
Efficient agent-based cluster ensembles. AAMAS 2006: 1079-1086 - [c23]Kagan Tumer:
Coordinating simple and unreliable agents. AAMAS 2006: 1119-1121 - [c22]Adrian K. Agogino, Kagan Tumer:
Distributed evaluation functions for fault tolerant multi-rover systems. GECCO 2006: 1079-1086 - 2005
- [c21]Adrian K. Agogino, Kagan Tumer:
Multi-agent reward analysis for learning in noisy domains. AAMAS 2005: 81-88 - [c20]Kagan Tumer, Adrian K. Agogino:
Coordinating multi-rover systems: evaluation functions for dynamic and noisy environments. GECCO 2005: 591-598 - [c19]Adrian K. Agogino, Kagan Tumer, Risto Miikkulainen:
Efficient credit assignment through evaluation function decomposition. GECCO 2005: 1309-1316 - [c18]Kagan Tumer, Adrian K. Agogino:
Efficient Reward Functions for Adaptive Multi-rover Systems. LAMAS 2005: 177-191 - 2004
- [c17]Adrian K. Agogino, Kagan Tumer:
Unifying Temporal and Structural Credit Assignment Problems. AAMAS 2004: 980-987 - [c16]Kagan Tumer, Adrian K. Agogino:
Time-Extended Policies in Multi-Agent Reinforcement Learning. AAMAS 2004: 1338-1339 - [c15]Adrian K. Agogino, Kagan Tumer:
Efficient Evaluation Functions for Multi-rover Systems. GECCO (1) 2004: 1-11 - 2003
- [j9]Kagan Tumer, Nikunj C. Oza:
Input decimated ensembles. Pattern Anal. Appl. 6(1): 65-77 (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 - [c13]Adrian K. Agogino, Kagan Tumer:
Team formation and communication restrictions in collectives. AAMAS 2003: 916-917 - [c12]Kagan Tumer, John W. Lawson:
Collectives for multiple resource job scheduling across heterogeneous servers. AAMAS 2003: 1142-1143 - [c11]Nikunj C. Oza, Kagan Tumer, Irem Y. Tumer, Edward M. Huff:
Classification of Aircraft Maneuvers for Fault Detection. Multiple Classifier Systems 2003: 375-384 - [i11]Kagan Tumer, David H. Wolpert:
Collectives for the Optimal Combination of Imperfect Objects. CoRR cond-mat/0301459 (2003) - [i10]David H. Wolpert, Kagan Tumer, Esfandiar Bandari:
Improving Search Algorithms by Using Intelligent Coordinates. CoRR math.OC/0301268 (2003) - 2002
- [j8]Jussi Karlgren, Pentti Kanerva, Björn Gambäck, Kenneth D. Forbus, Kagan Tumer, Peter Stone, Kai Goebel, Gaurav S. Sukhatme, Tucker R. Balch, Bernd Fischer, Doug Smith, Sanda M. Harabagiu, Vinay K. Chaudri, Mike Barley, Hans W. Guesgen, Thomas F. Stahovich, Randall Davis, James A. Landay:
The 2002 AAAI Spring Symposium Series. AI Mag. 23(4): 101-106 (2002) - [j7]David H. Wolpert, Kagan Tumer:
Collective Intelligence, Data Routing and Braess' Paradox. J. Artif. Intell. Res. 16: 359-387 (2002) - [j6]Kagan Tumer, Joydeep Ghosh:
Robust Combining of Disparate Classifiers through Order Statistics. Pattern Anal. Appl. 5(2): 189-200 (2002) - [c10]Kagan Tumer, Adrian K. Agogino, David H. Wolpert:
Learning sequences of actions in collectives of autonomous agents. AAMAS 2002: 378-385 - 2001
- [j5]David H. Wolpert, Kagan Tumer:
Optimal Payoff Functions for Members of Collectives. Adv. Complex Syst. 4(2-3): 265-280 (2001) - [c9]David H. Wolpert, Joseph Sill, Kagan Tumer:
Reinforcement Learning in Distributed Domains: Beyond Team Games. IJCAI 2001: 819-824 - [c8]Nikunj C. Oza, Kagan Tumer:
Input Decimation Ensembles: Decorrelation through Dimensionality Reduction. Multiple Classifier Systems 2001: 238-247 - 2000
- [c7]Kagan Tumer, David H. Wolpert:
Collective Intelligence and Braess' Paradox. AAAI/IAAI 2000: 104-109 - [c6]David H. Wolpert, Sergey Kirshner, Christopher J. Merz, Kagan Tumer:
Adaptivity in agent-based routing for data networks. Agents 2000: 396-403
1990 – 1999
- 1999
- [c5]David H. Wolpert, Kevin R. Wheeler, Kagan Tumer:
General Principles of Learning-Based Multi-Agent Systems. Agents 1999: 77-83 - [i9]Kagan Tumer, David H. Wolpert:
Avoiding Braess' Paradox through Collective Intelligence. CoRR cs.DC/9912012 (1999) - [i8]David H. Wolpert, Kagan Tumer, Jeremy Frank:
Using Collective Intelligence to Route Internet Traffic. CoRR cs.LG/9905004 (1999) - [i7]Kagan Tumer, Joydeep Ghosh:
Robust Combining of Disparate Classifiers through Order Statistics. CoRR cs.LG/9905013 (1999) - [i6]David H. Wolpert, Kevin R. Wheeler, Kagan Tumer:
Collective Intelligence for Control of Distributed Dynamical Systems. CoRR cs.LG/9908013 (1999) - [i5]David H. Wolpert, Kagan Tumer:
An Introduction to Collective Intelligence. CoRR cs.LG/9908014 (1999) - [i4]David H. Wolpert, Kevin R. Wheeler, Kagan Tumer:
General Principles of Learning-Based Multi-Agent Systems. CoRR cs.MA/9905005 (1999) - [i3]David H. Wolpert, Sergey Kirshner, Christopher J. Merz, Kagan Tumer:
Adaptivity in Agent-Based Routing for Data Networks. CoRR cs.MA/9912011 (1999) - [i2]Kagan Tumer, Nirmala Ramanujam, Joydeep Ghosh, Rebecca R. Richards-Kortum:
Ensembles of Radial Basis Function Networks for Spectroscopic Detection of Cervical Pre-Cancer. CoRR cs.NE/9905011 (1999) - [i1]Kagan Tumer, Joydeep Ghosh:
Linear and Order Statistics Combiners for Pattern Classification. CoRR cs.NE/9905012 (1999) - 1998
- [j4]Kagan Tumer, Nirmala Ramanujam, Joydeep Ghosh, Rebecca R. Richards-Kortum:
Ensembles of radial basis function networks for spectroscopic detection of cervical precancer. IEEE Trans. Biomed. Eng. 45(8): 953-961 (1998) - [c4]David H. Wolpert, Kagan Tumer, Jeremy Frank:
Using Collective Intelligence to Route Internet Traffic. NIPS 1998: 952-960 - 1996
- [j3]Kagan Tumer, Joydeep Ghosh:
Error Correlation and Error Reduction in Ensemble Classifiers. Connect. Sci. 8(3): 385-404 (1996) - [j2]Kagan Tumer, Joydeep Ghosh:
Analysis of decision boundaries in linearly combined neural classifiers. Pattern Recognit. 29(2): 341-348 (1996) - [c3]Kagan Tumer, Joydeep Ghosh:
Estimating the Bayes error rate through classifier combining. ICPR 1996: 695-699 - [c2]Kagan Tumer, Nirmala Ramanujam, Rebecca R. Richards-Kortum, Joydeep Ghosh:
Spectroscopic Detection of Cervical Pre-Cancer through Radial Basis Function Networks. NIPS 1996: 981-987 - 1995
- [j1]José Nelson Amaral, Kagan Tumer, Joydeep Ghosh:
Designing genetic algorithms for the state assignment problem. IEEE Trans. Syst. Man Cybern. 25(4): 687-694 (1995) - 1994
- [c1]Kagan Tumer, Joydeep Ghosh:
Sequence Recognition by Input Anticipation. IEA/AIE 1994: 77-86
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-20 21:55 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
