Source code of the agent-based simulator of underwater sensors for measuring the amount of fishes called ABS-FishCount

This dataset includes the source code of an agent-based simulator (ABS) of underwater sensors for measuring the amount of fishes. This ABS is called ABS-FishCount. Its goal is to provide an agent-oriented framework for defining and assessing different ways of collectively measuring amounts of fishes from a grid of underwater sensors. This tool simulates both the behavior of fishes and the strategies of the underwater sensors for measuring the amount of fishes. The tool can also simulate different ecosystem scenarios with different trends in the number of fishes. ABS-FishCount allows researchers to define new measurement strategies. Each measurement strategy can be defined by defining a new subtype of sensor agent by extending the “SensorAgent” class with a new class named with the “SensorAgent” suffix and overriding the “Measure” method. In order to make this strategy accessible to the user, the developer has to add a new option in the dropdown element named “StrategyDropdown” in the input canvas, with the name of the agent subtype without the suffix. This ABS has been developed following the Process for developing Efficient ABSs (PEABS). In addition, we have defined the stochastic behavior of some agents based on the Technique for developing ABS Apps and Online tools with Nondeterministic Decisions (TABASOND). The current simulator has been implemented with Unity 3D engine, version 5.5.1. This dataset supports the research of the authors about ABS-FishCount. The work about this dataset has been submitted to a scientific journal for consideration for its publication. If other researchers use the current dataset, they can credit the work of the authors by citing any of their works such as the ones about TABSAOND and PEABS. References about TABSAOND and PEABS: García-Magariño, I., Palacios-Navarro, G., Lacuesta, R. (2017). TABSAOND: A technique for developing agent-based simulation apps and online tools with nondeterministic decisions. Simulation Modelling Practice and Theory, 77, 84-107. García-Magariño, I., Gómez-Rodríguez, A., González-Moreno, J. C., Palacios-Navarro, G. (2015). PEABS: a Process for developing Efficient Agent-Based Simulators. Engineering Applications of Artificial Intelligence, 46, 104-112.