The Pavlic BEET Lab

Animal Behavior: Collective Decision-Making/Intelligence

Burchill, A.T., T.P. Pavlic, S.C. Pratt, and C.R. Reid. Weaver ants regulate the rate of prey delivery during collective vertical transport. Journal of Experimental Biology (IF=3.2), 226(19):jeb245634. 2023. doi:10.1242/jeb.245634

Navas Zuloaga, M.G., K.M. Baudier, J.H. Fewell, N. Ben-Asher, T.P. Pavlic, and Y. Kang. A Modeling Framework for Adaptive Collective Defense: Crisis Response in Social-Insect Colonies. Journal of Mathematical Biology (IF=2.0), 87:87. 2023. doi:10.1007/s00285-023-01995-5

Baudier, K.M., and T.P. Pavlic. Multi-Level Instrumentation of Bivouac Thermoregulation: Current Methods and Future Directions. Artificial Life and Robotics (IF=1.04), 27:308–315. 2022. doi:10.1007/s10015-022-00759-6

Baudier, K.M., and T.P. Pavlic. Incidental interactions among Neotropical army-ant colonies are met with self-organized walls of ants (Hymenoptera: Formicidae). Myrmecological News (IF=2.6, 15/111 in Entomology), 30:251–258. 2020. doi:10.25849/myrmecol.news_030:251

Baudier, K.M., M.M. Bennett, M.M. Ostwald, S. Hart, T.P. Pavlic, and J.H. Fewell. Age-based changes in kairomone response mediate task partitioning in stingless bee soldiers (Tetragonisca angustula). Behavioral Ecology (IF=3.347, 10/168 in Zoology), 74(10):1–9. 2020. doi:10.1007/s00265-020-02902-4

Baudier, K.M., M. Ostwald, C. Grueter, F. Segers, D. Roubik, T.P. Pavlic, S. Pratt, and J. Fewell. Changing of the guard: flexible specialization and age polyethism in nest defense of the stingless bee Tetragonisca angustula. Behavioral Ecology (IF=3.347, 10/168 in Zoology), 30(4):1041–1049. 2019. doi:10.1093/beheco/arz047

Animal Behavior: Foraging Theory and Nutrition

Pavlic, T.P., and K.M. Passino. The sunk-cost effect as an optimal rate-maximizing behavior. Acta Biotheoretica (IF=0.950), 59(1):53–66, 2011. doi:10.1007/s10441-010-9107-8

Pavlic, T.P., and K.M. Passino. When rate maximization is impulsive. Behavioral Ecology and Sociobiology (IF=2.382), 64(8):1255–1265, August 2010. doi:10.1007/s00265-010-0940-1

Animal Behavior: Biomechanics

Hunter, A.H., T.P. Pavlic, M.J. Angilletta Jr., R.S. Wilson. Identifying the best strategy for soccer penalty success: A predictive model for optimizing behavioural and biomechanical trade-offs. Journal of Biomechanics (IF=1.617), 141:111208. 2022. doi:10.1016/j.jbiomech.2022.111208

Animal Behavior: Methods

X. Guo, M.R. Lin, L.P. Saldyt, A. Azizi, Y. Kang, T.P. Pavlic, and J.H. Fewell. Decoding alarm signal propagation of seed-harvester ants using automated movement tracking and supervised machine learning. Proceedings of the Royal Society B: Biological Sciences (IF=4.637), 289:20212176. 2022. doi:10.1098/rspb.2021.2176

Choi, T., B.P. Pyenson, J. Liebig, and T.P. Pavlic. Beyond Tracking: Using Deep Learning to Discover Novel Interactions in Biological Swarms. In: Proceedings of the 4th International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM-2021), June 1–4, 2021. Kyoto, Japan. Best paper award. doi:10.1007/s10015-022-00753-y

Choi, T., B.P. Pyenson, J. Liebig, and T.P. Pavlic. Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-21), 35(17):15286–15292, February 4–6, 2021. Virtual conference. doi:10.1609/aaai.v35i17.17794

Valentini, G., N. Mizumoto, S.C. Pratt, T.P. Pavlic, and S.I. Walker. Revealing the structure of information flows discriminates similar animal social behaviors. eLife (IF=7.080), 9:e55395. 2020. doi:10.7554/eLife.55395

Burchill, A., and T.P Pavlic. Dude, Where’s my Mark? Creating Robust Animal Identification Schemes Informed by Communication Theory. Animal Behaviour (IF=2.689), 154:203–208. 2019. doi:10.1016/j.anbehav.2019.05.013

Neuroscience

Navas Zuloaga, M.G., T.P. Pavlic, and B.H. Smith. Alternative model systems for cognitive variation: eusocial-insect colonies. Trends in Cognitive Sciences (IF=15.4), 26(10):836–848. 2022. Invited. doi:10.1016/j.tics.2022.06.011

Baudier, K.M., M.M. Bennett, M. Barrett, F.J. Cossio, R.D. Wu, S. O’Donnell, T.P. Pavlic, and J.H. Fewell. Soldier neural architecture is temporarily modality specialized but poorly predicted by repertoire size in the stingless bee Tetragonisca angustula. Journal of Comparative Neurology (IF=3.331), 530(4):672–682. 2022. doi:10.1002/cne.25273

Social Systems

Pavlic, T.P. Social Models from Non-Human Systems. In: P. Davis, J. Pfautz, and Angela O’Mahony (Eds), Social-Behavioral Modeling for Complex Systems, ch. 11, pp. 231–261. John Wiley & Sons, 2019. Invited book chapter. doi:10.1002/9781119485001.ch11

Pavlic, T.P., and S.C. Pratt. Superorganismic Behavior via Human Computation. In: P. Michelucci (Ed.), Handbook of Human Computation, ch. 74, pp. 911–960. Springer, New York, NY, 2013. Invited book chapter. doi:10.1007/978-1-4614-8806-4_74

Information in Living Systems

Valentini, G, T.P. Pavlic, S.I. Walker, S.C. Pratt, D. Biro, and T. Sasaki. Naïve individuals promote collective exploration in homing pigeons. eLife (IF=7.080), 10:e68653. 2022. doi:10.7554/eLife.68653

Caetano-Anollés, K., B. Ewers, S. Iyer, J.R. Lucas, T.P. Pavlic, A.P. Seale, and Y. Zeng. A minimal framework for describing living systems: a multi-dimensional view of life across scales. Integrative and Comparative Biology (IF=2.637), 61(6):2053–2065. 2021. Authors in alphabetical order. doi:10.1093/icb/icab172

Weinstein, S., and T.P. Pavlic. Noise and function. In: S.I. Walker, P.C.W. Davies, and G.F.R. Ellis (Eds), From Matter to Life: Information and Causality, ch. 9, pp. 126–143. Cambridge University Press, 2017. Invited book chapter. doi:10.1017/9781316584200.009

Pavlic, T.P., A. Adams, P.C.W. Davies, and S.I. Walker. Self-referencing cellular automata: A model of the evolution of information control in biological systems. In: Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems (ALIFE 14), July 30 – August 2, 2014. New York, NY, USA. doi:10.7551/978-0-262-32621-6-ch083

Ecological Applications (Conservation, IPM, etc.)

Jobe, N.B., A. Chourasia, B.H. Smith, E. Molins, A. Rose, T.P. Pavlic, K.P. Paaijmans. Using electric fields to control insects: Current applications and future directions. Journal of Insect Science (IF=2.2), 24(1):8. 2024. doi:10.1093/jisesa/ieae007

Wheatley, R., T.P. Pavlic, O. Levy, and R.S. Wilson. Habitat features and performance interact to determine the outcomes of terrestrial predator–prey pursuits. Journal of Animal Ecology (IF=5.07), 89(12):2951–2971. 2020. doi:10.1111/1365-2656.13353

Wilson, R.S., T.P. Pavlic, R. Wheatley, A.C. Niehaus, and O. Levy. Modeling escape success in terrestrial predator–prey interactions. Integrative and Comparative Biology (IF=2.637), 60(2):497–508. 2020. doi:10.1093/icb/icaa070

Robotics: AI

Choi, T., and T.P. Pavlic. Automatic Discovery of Motion Patterns that Improve Learning Rate in Communication-Limited Multi-Robot Systems. In: Proceedings of the 2020 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), September 14–16, 2020. Karlsruhe, Germany. doi:10.1109/MFI49285.2020.9235218

Robotics: Bio-Inspired

Pavlic, T.P., J. Hanson, G. Valentini, S.I. Walker, and S.C. Pratt. Quorum sensing without deliberation: Biological inspiration for externalizing computation to physical spaces in multi-robot systems. Swarm Intelligence (IF=2.556), 15(1):171–203. 2021. doi:10.1007/s11721-021-00196-4

Pavlic, T.P., S. Wilson, G.P. Kumar, and S. Berman. Control of stochastic boundary coverage by multi-robot systems. Journal of Dynamic Systems, Measurement, and Control (IF=1.304), 137(3):034505, October 21, 2014. doi:10.1115/1.4028353

Bowers, K.P., L.G. Strickland, G. Cooke, C. Pippin, and T.P. Pavlic. Trust-based Information Propagation on Multi-robot Teams in Noisy Low-communication Environments. In: Proceedings of the 14th International Symposium on Distributed Autonomous Robotic Systems (DARS 2018; acceptance rate 36%), October 15–17, 2018. Boulder, CO, USA. doi:10.1007/978-3-030-05816-6_17

Strickland, L.G., K. Baudier, K.P. Bowers, T.P. Pavlic, and C. Pippin. Bio-Inspired Role Allocation of Heterogeneous Teams in a Site Defense Task. In: Proceedings of the 14^th International Symposium on Distributed Autonomous Robotic Systems (DARS 2018; acceptance rate 42%), October 15–17, 2018. Boulder, CO, USA. doi:10.1007/978-3-030-05816-6_10

Cooke, G., E. Squires, L. Strickland, K. Bowers, C. Pippin, T.P. Pavlic, and S.C. Pratt. Bio-inspired nest-site selection for distributing robots in low-communication environments. In: Proceedings of the 16th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2018), June 20–22, 2018. Toledo, Spain. doi:10.1007/978-3-319-94779-2_44

Pavlic, T.P., and K.M. Passino. Distributed and Cooperative Task Processing: Cournot Oligopolies on a Graph. IEEE Transactions on Cybernetics (IF=11.079), 44(6):774–784, June 2014. doi:10.1109/TCYB.2013.2271776

Pavlic, T.P., S. Wilson, G.P. Kumar, and S. Berman. An enzyme-inspired approach to stochastic allocation of robotic swarms around boundaries. In: Proceedings of the 16th International Symposium on Robotics Research (ISRR 2013), pp. 631–647, December 16–19, 2013. Singapore. doi:10.1007/978-3-319-28872-7_36

Pavlic, T.P., and K.M. Passino. Generalizing foraging theory for analysis and design. The International Journal of Robotics Research (IF=4.047), 30(5):505–523, 2011. doi:10.1177/0278364910396551

Pavlic, T.P., and K.M. Passino. Foraging theory for autonomous vehicle speed choice. Engineering Applications of Artificial Intelligence (IF=4.201), 22(3):482–489, April 2009. doi:10.1016/j.engappai.2008.10.017

Control Systems: Bio-Inspired

Pavlic, T.P. Using Physical Stigmergy in Decentralized Optimization Under Multiple Non-separable Constraints: Formal Methods and an Intelligent Lighting Example. In: Proceedings of the 2014 Workshop on Nature Inspired Distributed Computing (NIDISC 2014 @ IPDPS 2014), pp. 402–411, May 19, 2014. Phoenix, AZ, USA. doi:10.1109/IPDPSW.2014.52

Computer Vision

Hong, J., K.H. Park, and T.P. Pavlic. Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024), January 4–8, 2024. [WACV Link]

Hong, J., and T.P. Pavlic. Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks. In: Proceedings of the 2023 Workshop on Unifying Representations in Neural Models (UniReps 2023 @ NeurIPS 2023), December 15, 2023. Best UniReps Proceedings Paper Award. [arXiv preprint]

Hong, J., and T.P. Pavlic. Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection. In: Proceedings of the First International Workshop on Combining Learning and Reasoning: Programming Languages, Formalisms, and Representations (CLeaR 2022 @ AAAI-2022), February 28, 2022. [arXiv preprint]

Hong, J., and T.P. Pavlic. An Insect-Inspired Randomly Weighted Neural Network with Random Fourier Features for Neuro-Symbolic Relational Learning. In: Proceedings of the 15th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy ‘20/21 @ IJCLR 2021), October 25–27, 2021. [NeSy link]