Simulation of behavioral profiles in the plus-maze: A Classification and Regression Tree approach

Molina-Delgado, Mauricio; Padilla-Mora, Michael; Fonaguera, Jaime

doi:10.1016/j.biosystems.2013.07.002

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…In fact, from the statistical point of view, constructing a regression tree can be seen as a type of variable selection, where the leading parameters can be identified through the algorithm. The potential interaction between variables is handled automatically by a monotonic transformation of the variables [33]. In regression tree modeling, we firstly split the data into two regions by one variable, compute the mean value of the response variables in each region, and then split the data further by the other variables.…”

Section: Regression Treementioning

confidence: 99%

“…In Reference [32], a coastal ecosystem was simulated to study the growth behavior of bivalve species, and the regression tree analysis was conducted to investigate the leading factors associated with the water conditions that promote the quality and growth rate of species the most. In Reference [33], a simulation model of a rat in an elevated plus-maze was constructed to measure the anxiety-like behavior. After the computational simulation, the dominant parameters were investigated to improve the model performance through the regression tree method.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway

2019

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For a better understanding of the nature of complex systems modeling, computer simulations and the analysis of the resulting data are major tools which can be applied. In this paper, we study a statistical modeling problem of data coming from a simulation model that investigates the correctness of autonomous agents’ decisions in learning to cross a cellular automaton-based highway. The goal is a better understanding of cognitive agents’ performance in learning to cross a cellular automaton-based highway with different traffic density. We investigate the effects of parameters’ values of the simulation model (e.g., knowledge base transfer, car creation probability, agents’ fear and desire to cross the highway) and their interactions on cognitive agents’ decisions (i.e., correct crossing decisions, incorrect crossing decisions, correct waiting decisions, and incorrect waiting decisions). We firstly utilize canonical correlation analysis (CCA) to see if all the considered parameters’ values and decision types are significantly statistically correlated, so that no considered dependent variables or independent variables (i.e., decision types and configuration parameters, respectively) can be omitted from the simulation model in potential future studies. After CCA, we then use the regression tree method to explore the effects of model configuration parameters’ values on the agents’ decisions. In particular, we focus on the discussion of the effects of the knowledge base transfer, which is a key factor in the investigation on how accumulated knowledge/information about the agents’ performance in one traffic environment affects the agents’ learning outcomes in another traffic environment. This factor affects the cognitive agents’ decision-making abilities in a major way in a new traffic environment where the cognitive agents start learning from existing accumulated knowledge/information about their performance in an environment with different traffic density. The obtained results provide us with a better understanding of how cognitive agents learn to cross the highway, i.e., how the knowledge base transfer as a factor affects the experimental outcomes. Furthermore, the proposed methodology can become useful in modeling and analyzing data coming from other computer simulation models and can provide an approach for better understanding a factor or treatment effect.

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Section: Regression Treementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway

2019

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AnimalEnvNet: A Deep Reinforcement Learning Method for Constructing Animal Agents Using Multimodal Data Fusion

Chen,

Wang,

Zhao

et al. 2024

Applied Sciences

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Simulating animal movement has long been a central focus of study in the area of wildlife behaviour studies. Conventional modelling methods have difficulties in accurately representing changes over time and space in the data, and they generally do not effectively use telemetry data. Thus, this paper introduces a new and innovative deep reinforcement learning technique known as AnimalEnvNet. This approach combines historical trajectory data and remote sensing images to create an animal agent using deep reinforcement learning techniques. It overcomes the constraints of conventional modelling approaches. We selected pandas as the subject of our research and carried out research using GPS trajectory data, Google Earth images, and Sentinel-2A remote sensing images. The experimental findings indicate that AnimalEnvNet reaches convergence during supervised learning training, attaining a minimal mean absolute error (MAE) of 28.4 m in single-step prediction when compared to actual trajectories. During reinforcement learning training, the agent has the capability to replicate animal locomotion for a maximum of 12 iterations, while maintaining an error margin of 1000 m. This offers a novel approach and viewpoint for mimicking animal behaviour.

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Simulation of behavioral profiles in the plus-maze: A Classification and Regression Tree approach

Cited by 2 publications

References 20 publications

Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway

Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway

AnimalEnvNet: A Deep Reinforcement Learning Method for Constructing Animal Agents Using Multimodal Data Fusion

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