Potential seminar topics [updated: 24. 10. 2023]

1. recreate an existing collective behaviour model that has been published fairly recently (last 5 years), and expand on that research; possible starting points (expand your research by searching for the title on http://scholar.google.com and following “cited by” and “related articles”):
• Mohapatra & Mahapatra, 2023. Flock response to sustained asynchronous predator attacks. https://doi.org/10.1101/2023.11.14.567144
• Ruan, et al. 2023. Hawk-Pigeon Game Tactics for Unmanned Aerial Vehicle Swarm Target Defense. https://doi.org/10.1109/TII.2023.3248075
• Martinez, et al. 2023. A deep reinforcement learning strategy for autonomous robot flocking. http://doi.org/10.11591/ijece.v13i5.pp5707-5716
• Li, et al. 2023. Predator-prey survival pressure is sufficient to evolve swarming behaviors. https://doi.org/10.1088/1367-2630/acf33a 
• Puy, et al. 2023. Selective social interactions and speed-induced leadership in schooling fish. https://arxiv.org/abs/2305.17108
• Krongauz & Lazebnik 2023. Collective evolution learning model for vision-based collective motion with collision avoidance. https://doi.org/10.1371/journal.pone.0270318
• Takahashi & Komeyama 2023. Development of a feeding simulation to evaluate how feeding distribution in aquaculture affects individual differences in growth based on the fish schooling behavioral model. https://doi.org/10.1371/journal.pone.0280017
• King, et al. 2023. Biologically inspired herding of animal groups by robots. [REVIEW]. https://doi.org/10.1111/2041-210X.14049
• Cai, et al. 2023. Behavior-Based Herding Algorithm for Social Force Model Based Sheep Herd. https://doi.org/10.3390/electronics12020285
• Dachner, et al. 2023. The visual coupling between neighbours explains local interactions underlying human ‘flocking’. https://doi.org/10.1098/rspb.2021.2089
• Missila & Mahore, 2022. Emergence of intelligent collective motion in a group of agents with memory. https://arxiv.org/abs/2212.07362
• Papadopoulou, et al. 2022. Emergence of splits and collective turns in pigeon flocks under predation. https://doi.org/10.1098/rsos.211898
  
• Ranganathan, et al. 2022. Optimal shepherding and transport of a flock, https://arxiv.org/abs/2211.04352
  
• Qi, et al. 2022. The emergence of collective obstacle avoidance based on a visual perception mechanism, https://doi.org/10.1016/j.ins.2021.10.039
• Filella, et al. 2017. Hydrodynamic interactions influence fish collective behavior, https://arxiv.org/abs/1705.07821 
• Tashiro, et al. 2022. Guidance by multiple sheepdogs including abnormalities, https://doi.org/10.1007/s10015-022-00807-1
• Fujioka, et al. 2022. Shepherding Heterogeneous Flock with Model-Based Discrimination, https://arxiv.org/abs/2210.11055
• Krongauz & Lezebnik 2022. Collective Evolution Learning Model for Vision-Based Collective Motion with Collision Avoidance, https://doi.org/10.1101/2022.06.09.495429
• Hartono, et al. 2022. A Stochastic Differential Equation Model for Predator-Avoidance Fish Schooling, https://arxiv.org/abs/2210.03989
• Ivanov & Palamas. 2022. Collective Adaptation in Multi-Agent Systems: How Predator Confusion Shapes Swarm-Like Behaviors, https://arxiv.org/abs/2209.06338
• Yu, et al. 2022. Simulation of collective pursuit-evasion behavior with runtime situational awareness, https://doi.org/10.1002/cav.2124
• Cai, et al. 2022. Cooperative Driven Algorithm for Couzin Model Based Fish School by Multiple Predators, https://doi.org/10.1155/2022/4708496
• Liu, et al. 2021. Sheepdog Driven Algorithm for Sheep Herd Transport, https://doi.org/10.23919/CCC52363.2021.9549396
• Bastien & Romanczuk. 2020. A model of collective behavior based purely on vision, http://dx.doi.org/10.1126/sciadv.aay0792
• Yang, et al. 2020. A review on crowd simulation and modeling, https://doi.org/10.1016/j.gmod.2020.101081
• Collignon, et al. 2016. A stochastic vision-based model inspired by zebrafish collective behaviour in heterogeneous environments, https://royalsocietypublishing.org/doi/10.1098/rsos.150473
• El-Fiqi, et al. 2020. A preliminary study towards an improved shepherding model, https://dl.acm.org/doi/pdf/10.1145/3377929.3390067
• Zhow, et al. 2016, Modeling of Crowd Evacuation With Assailants via a Fuzzy Logic Approach, https://doi.org/10.1109/TITS.2016.2521783
• Markowska-Kaczmar & Marcinkowski, 2020. Markov network versus recurrent neural network in forming herd behavior based on sight and simple sound communication, https://doi.org/10.1016/j.asoc.2020.106177
• Calovi, et al. 2018, Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors. https://doi.org/10.1371/journal.pcbi.1005933
• Luo, et al. 2018. ProactiveCrowd: Modelling Proactive Steering Behaviours for Agent‐Based Crowd Simulation. https://doi.org/10.1111/cgf.13303
• Xue, et al. 2017. Fuzzy Logic-Based Model That Incorporates Personality Traits for Heterogeneous Pedestrians. https://doi.org/10.3390/sym9100239
• Pearce, et al. 2014. Role of projection in the control of bird flocks. https://doi.org/10.1073/pnas.1402202111. 
• Klamser, Romanczuk. 2021. Collective predator evasion: Putting the criticality hypothesis to the test. https://doi.org/10.1371/journal.pcbi.1008832
• Davidson, et al. 2021. Collective detection based on visual information in animal groups. https://doi.org/10.1098/rsif.2021.0142 
2. refactor the FRIsheeping source code to the ECS stack and use of computeShaders; you will be given access to the codebase of FRIsheeping including all assets; expand the environment, we aim for simulation of larger numbers of sheep (500-1000, anything more is a plus); the end goal should be also to develop a herding algorithm (possibly even collaborative) that is based purely on visual information and is better than the one in the current codebase (simplicity is the key). The topic can also be worked on by two groups, one working on the optimization of rendering, the other on the herding algorithm. 
   
3. reimplement the current herding algorithm by means of Fuzzy Logic, attempt learning the behaviour by means of genetic algorithms
   
4. use genetic algorithms, ml-agents, deep learning, or any other approach for learning the herding task; an extension of topic 1; the end goal is that the behaviour of the shepherd is not scripted, but learned; since learning takes time, refactoring of the FRIsheeping source code is a must
   
5. implementation of a predator prey model for the study of fish schooling and hunting tactics in Unity; must be besed on the ECS stack (see also Boids in https://github.com/Unity-Technologies/EntityComponentSystemSamples) and should extend works from Demšar et al. (we will cover these in detail during the course)
   
6. extension of Demšar et al. https://doi.org/10.1016/j.ecolmodel.2015.02.018, in view of Lambert et al. https://doi.org/10.1101/2021.05.25.445573, evolve choice of one of described targeting tactics.
7. a topic by a group's own choice; this should have a clear foundation based on existing research - present a 10 sentence paragraph explaining the problem that is supported by at least two scientific references.

Related literature and tools:

1. Escaping from Predators: An Integrative View of Escape Decisions, https://publicism.info/science/predators/
   
2. Wu W. et al. 2021. Visual information based social force model for crowd evacuation, 10.26599/TST.2021.9010023
3. Ishikawa Y. et al. 2021. Foids: bio-inspired fish simulation for generating synthetic datasets, 10.1145/3478513.3480520
4. Wolter Jolles J. et al. 2021. Both Prey and Predator Features Determine Predation Risk and Survival of Schooling Prey, 10.1101/2021.12.13.472101
5. Millington I. & Funge J. 2009. Artificial Intelligence for Games.
6. Richmond P. et al. 2023. Fast Large-Scale Agent-based Simulations on NVIDIA GPUs with FLAME GPU.