Assessing Human Judgment of Computationally Generated Swarming Behavior
Computer-based swarm systems, aiming to replicate the flocking behavior of birds, were first introduced by Reynolds in 1987. In his initial work, Reynolds noted that while it was difficult to quantify the dynamics of the behavior from the model, observers of his model immediately recognized them as...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-02-01
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| Series: | Frontiers in Robotics and AI |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/frobt.2018.00013/full |
| _version_ | 1819000440395661312 |
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| author | John Harvey Kathryn Elizabeth Merrick Hussein A. Abbass |
| author_facet | John Harvey Kathryn Elizabeth Merrick Hussein A. Abbass |
| author_sort | John Harvey |
| collection | DOAJ |
| description | Computer-based swarm systems, aiming to replicate the flocking behavior of birds, were first introduced by Reynolds in 1987. In his initial work, Reynolds noted that while it was difficult to quantify the dynamics of the behavior from the model, observers of his model immediately recognized them as a representation of a natural flock. Considerable analysis has been conducted since then on quantifying the dynamics of flocking/swarming behavior. However, no systematic analysis has been conducted on human identification of swarming. In this paper, we assess subjects’ assessment of the behavior of a simplified version of Reynolds’ model. Factors that affect the identification of swarming are discussed and future applications of the resulting models are proposed. Differences in decision times for swarming-related questions asked during the study indicate that different brain mechanisms may be involved in different elements of the behavior assessment task. The relatively simple but finely tunable model used in this study provides a useful methodology for assessing individual human judgment of swarming behavior. |
| first_indexed | 2024-12-20T22:33:21Z |
| format | Article |
| id | doaj.art-2fe86877185b400ab62c3150f73af453 |
| institution | Directory Open Access Journal |
| issn | 2296-9144 |
| language | English |
| last_indexed | 2024-12-20T22:33:21Z |
| publishDate | 2018-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj.art-2fe86877185b400ab62c3150f73af4532022-12-21T19:24:41ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442018-02-01510.3389/frobt.2018.00013332144Assessing Human Judgment of Computationally Generated Swarming BehaviorJohn Harvey0Kathryn Elizabeth Merrick1Hussein A. Abbass2School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, AustraliaSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT, AustraliaSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT, AustraliaComputer-based swarm systems, aiming to replicate the flocking behavior of birds, were first introduced by Reynolds in 1987. In his initial work, Reynolds noted that while it was difficult to quantify the dynamics of the behavior from the model, observers of his model immediately recognized them as a representation of a natural flock. Considerable analysis has been conducted since then on quantifying the dynamics of flocking/swarming behavior. However, no systematic analysis has been conducted on human identification of swarming. In this paper, we assess subjects’ assessment of the behavior of a simplified version of Reynolds’ model. Factors that affect the identification of swarming are discussed and future applications of the resulting models are proposed. Differences in decision times for swarming-related questions asked during the study indicate that different brain mechanisms may be involved in different elements of the behavior assessment task. The relatively simple but finely tunable model used in this study provides a useful methodology for assessing individual human judgment of swarming behavior.http://journal.frontiersin.org/article/10.3389/frobt.2018.00013/fullswarmingflockingperception of biological motionswarm intelligencehuman perception |
| spellingShingle | John Harvey Kathryn Elizabeth Merrick Hussein A. Abbass Assessing Human Judgment of Computationally Generated Swarming Behavior Frontiers in Robotics and AI swarming flocking perception of biological motion swarm intelligence human perception |
| title | Assessing Human Judgment of Computationally Generated Swarming Behavior |
| title_full | Assessing Human Judgment of Computationally Generated Swarming Behavior |
| title_fullStr | Assessing Human Judgment of Computationally Generated Swarming Behavior |
| title_full_unstemmed | Assessing Human Judgment of Computationally Generated Swarming Behavior |
| title_short | Assessing Human Judgment of Computationally Generated Swarming Behavior |
| title_sort | assessing human judgment of computationally generated swarming behavior |
| topic | swarming flocking perception of biological motion swarm intelligence human perception |
| url | http://journal.frontiersin.org/article/10.3389/frobt.2018.00013/full |
| work_keys_str_mv | AT johnharvey assessinghumanjudgmentofcomputationallygeneratedswarmingbehavior AT kathrynelizabethmerrick assessinghumanjudgmentofcomputationallygeneratedswarmingbehavior AT husseinaabbass assessinghumanjudgmentofcomputationallygeneratedswarmingbehavior |