1 / 11

Plant-Moose-Predator System Simulation

Plant-Moose-Predator System Simulation. Anastasia Mironova CS 470. Project Overview. Goal: Develop an agent-based simulation to model plant-moose-predator system behavior Client is Dr. Donald Spalinger, Department of Biological Sciences, Professor and Chair Why:

pierce
Download Presentation

Plant-Moose-Predator System Simulation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Plant-Moose-Predator System Simulation Anastasia Mironova CS 470

  2. Project Overview • Goal: • Develop an agent-based simulation to model plant-moose-predator system behavior • Client is Dr. Donald Spalinger, Department of Biological Sciences, Professor and Chair • Why: • To assist scientists in codifying current hypotheses and axioms regarding the growth and defense responses of plants to herbivore foraging patterns and foraging efficiency, herbivore productivity and habitat carrying capacity, and moose-predator interactions • Limited capabilities of existing systems

  3. Desirable vs. Possible Priority list for the desirable version: • Flexible Design • 2D Landscape implementation • Agent “Plant” implementation • Agent “Moose” implementation • Plant-Moose interaction implementation • Expansion of scope (space and number of agents) • Optimization (memory, run-time) • Agent “Predator” implementation • Moose-Predator interaction implementation • Complex Landscape Patterns • Artificial Intelligence Behavior Patterns for Agents Main decision: little of everything vs. thorough specifics

  4. Implementation Decisions Effort was geared toward more thorough implementation of specific components according to the initial priority list: • Flexible, reliable system architecture, capable of fully supporting all the functional specifications known at the time and allowing for future expansions • Emphasized Simulation Components: • Landscape model • Plants

  5. Simulation – PMPSim.java • Timer • Landscape: LandscapeSegment[][] • Plants • PlantsHashTable: Plant, hashkey = plantID • PlantsArray: plantID • Moose • MooseHashTable: Moose, hashkey = mooseID • MooseArray: mooseID • Predators • PredatorsHashTable: Predator, hashkey = predatorID • PredatorsArray: predatorID • Other parameters • GrowthSeason, Precipitation, etc.

  6. Simplified System Architecture SimulationParametersMainFrame SimpleLandscapeSpecificationsPanel SimulationMainFrame SimulationMenuBar AdvancedLandscapeSpecificationsFrame SimulationToolBar LandscapeToolBar IndividualSegmentDisplay SimplePlantsSpecificationsPanel SimpleMooseSpecificationsPanel SimplePredatorsSpecificationsPanel PresetPlantPropertiesFrame CustomPlantPropertiesFrame

  7. GUI Representation of the System

  8. Landscape Model • Main design decision: • Partitioning • Characteristics: • Dimensions • Elevation Contours • Bogs/Wetlands • Forest Habitat • Riparian Habitat • Plant species-specific patches • Custom View

  9. Plants • Preset vs. Custom • Groups vs. Individual • Characteristics: • Plant ID • Species Name • Coordinates • Elevation Range • Aspect Preference • Special Habitat Associations • Habitat Restrictions

  10. Potential Extensions Existing system design allows for: • Intuitive implementation of any additional types of agents, such as • Moose • Predators • Potentially, supporting very high levels of complexity, necessary for incorporating extensive amounts of research data and, hence, giving the simulation the capabilities to represent a more realistic model • Future optimization in both memory usage and run-time via • Landscape partitioning • Threading

  11. Conclusion • Developed a prototype of fundamental components for customizing and running plant-moose-predators simulations • Lessons Learned: • More about working with a real client • More about design • More about simulations

More Related