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2020 – today
- 2023
[c121]Gaurav Dixit, Kagan Tumer:
Learning Inter-Agent Synergies in Asymmetric Multiagent Systems. AAMAS 2023: 1569-1577- [c120]Joshua Cook, Tristan Scheiner, Kagan Tumer:
Multi-Team Fitness Critics For Robust Teaming. AAMAS 2023: 2406-2408 - [c119]Connor Yates
, Kagan Tumer:
Knowledge Shaped Behavior Generation for Online Adaptation. GECCO Companion 2023: 343-346 - [c118]Ayhan Alp Aydeniz, Robert Tyler Loftin, Kagan Tumer:
Novelty Seeking Multiagent Evolutionary Reinforcement Learning. GECCO 2023: 402-410 - [c117]Joshua Cook, Kagan Tumer, Tristan Scheiner:
Leveraging Fitness Critics To Learn Robust Teamwork. GECCO 2023: 429-437 - [c116]Gaurav Dixit, Kagan Tumer:
Learning Synergies for Multi-Objective Optimization in Asymmetric Multiagent Systems. GECCO 2023: 447-455 - [c115]Anna Nickelson, Kagan Tumer, William D. Smart:
Contextual Multi-Objective Path Planning. ICRA 2023: 10240-10246 - 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
- [j42]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
- [j41]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) - [j40]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) - [j39]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
- [j38]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
- [j37]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
- [j36]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) - [j35]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) - [j34]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
- [j33]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) - [j32]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
- [j31]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
- [j30]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) - [j29]Matt Knudson, Kagan Tumer:
Dynamic Partnership Formation for Multi-Rover Coordination. Adv. Complex Syst. 16(1) (2013) - [j28]Jaime Junell, Kagan Tumer:
Robust predictive cruise control for commercial vehicles. Int. J. Gen. Syst. 42(7): 776-792 (2013) - [j27]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) - [j26]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
- [j25]Adrian K. Agogino, Kagan Tumer:
A multiagent approach to managing air traffic flow. Auton. Agents Multi Agent Syst. 24(1): 1-25 (2012) - [j24]Liz Sonenberg, Peter Stone, Kagan Tumer, Pinar Yolum:
Ten Years of AAMAS: Introduction to the Special Issue. AI Mag. 33(3): 11-13 (2012) - [j23]Ehsan M. Nasroullahi, Kagan Tumer:
Combining coordination mechanisms to improve performance in multi-robot teams. Artif. Intell. Res. 1(2): 1-10 (2012) - [j22]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) - [j21]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
- [j20]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
- [j19]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
- [j18]Kagan Tumer, Newsha Khani:
Learning from Actions not Taken in Multiagent Systems. Adv. Complex Syst. 12(4-5): 455-473 (2009) - [j17]Kagan Tumer, Adrian K. Agogino:
Multiagent Learning for Black Box System Reward Functions. Adv. Complex Syst. 12(4-5): 475-492 (2009) - [j16]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) - [j15]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