To view a copy of this license, visit or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.Ĭommercial licenses are also available. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Athabasca University: Faculty of Science and Techology: School of Computing and Information Systems, Athabasca, Alberta, Canada. MAS Modelling Environment for Hunting and Evasion Algorithms. Use of any the diagrams from the thesis paper is authorized with appropriate citation (no need to seek additional permission from the author).
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The model can be customized to include more agents (and types of agents), obstacles (terrain types), and search, hunt, and evasion algorithms. ticks) of the HAs onto the PAs (or the PAs onto the fruit trees). The effectiveness of the hunting algorithms can be observed as the degree of convergence in time (ie. These can be placed in specific patterns/locations or can be randomly distributed (again hardcoded).Ĭlicking the "Go Once" or "Go" will drive one (or many) deliberation cycles within the WA, which then cascades to driving the deliberation cycles of the PAs and HAs. Next to be instantiated are the PAs and HAs.
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The stream can be enabled/disabled within the code itself. The WA then brokers deliberations through the interface with the "Go Once", "Go", "Setup/Reset", and "Clear".ĭuring instantiation, the WA first establishes the terrain layout based on the probability density of the features establihsed in the interface. Inactive, the weather and terrain types remain to be developed.Īlso brokered as perceptions through the WA, the PA and HA parameters are set next and these establish the type, class, and quantity of prey and hunters. The densities of food, water, foliage, and rocks are set as percentages of the total patches. The WA brokers the settings at the "interface" with the values set in the terrain parameters (subgroup of the environmental setup options) and, treating these as perceptions, instantiates these as obstacles within the hunting environment.
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HOW TO USE ITĪ key factor in search, hunt, and evasion algorithms is the presence of obstacles. With minimal re-programming, these agents can be re-designed to represent other creatures within the Animal Kingdom (with their respective search, hunt and evasion techniques). The Huning Agent represents lionesses (or wolves) which are enabled with pack cooperation and hunting algorithms to converge on relatively mobile prey. The Prey Agent represents a deer (or gazelle) which is enabled with simple search algorithms to find food and water sources (stationary targets of fruit trees and streams) and evade predators. The Generic Agent offers a method to reuse code between the various types of Prey Agents and Hunting Agents.
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The World Agent and Patch Agent are simply the base NetLogo environment structured into the BDI architecture. The HAPEE consists of five (5) Agent types: World Agent (WA), Patch Agent, Generic Agent (GA), Prey Agent (PA), and Hunting Agent (HA). While the environment was originally designed to evaluate the Lion Optimization Algorithm (LOA) and the Grey Wolf Optimization (GWO) algorithm, the hunting development environment can be easily adapted to evaluate algorithms for searching, hunting, and evasion.
This model provides a MAS framework, structured on the Belief, Desire, and Intent (BDI) architecture, to evaluate hunting algorithms. HUNTING ALGORITHM PERFORMANCE EVALUATION ENVIRONMENT-HAPEE- A MAS HUNTING MODEL DEVELOPMENT ENVIRONMENTįor a detailed description of the model, please consult the attached thesis paper.
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