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* Multi-agent Based Simulation: Where Are the Agents?
https://link.springer.com/chapter/10.1007/3-540-36483-8_1


*MABE (Modular Agent Based Evolver): A framework for digital evolution research (2017)
https://www.mitpressjournals.org/doi/pdf/10.1162/isal_a_016
MABE is a modular and reconfigurable digital evolution research tool designed to minimize the time from hypotheses generation to hypotheses testing. MABE provides an accessible framework which seeks to increase collaborations and to facilitate reuse by implementing only features that are common to most experiments, while leaving experimentally dependent details up to the user.
"One  difficulty  in  Digital  Evolution  research  stems  from the  need  to  develop  the  software  used  to  conduct  the  re-search"
*Artificial Intelligence Techniques to Enhance Actors’ Decision Strategies in Socio-­ecological Agent-­ Based Models (2016)
https://scholarsarchive.byu.edu/iemssconference/2016/Stream-D/19/
Title is pretty self-explanatory.
Provides an analysis of the types of AI learning algorithms employed in various application domains which use Agent-Based Models, their specific operationalization in an agent’s decision-­making for various tasks, treatment of spatial and social environment in the design of AI learning algorithms, and the level of empirical information used in ABM. Also highlights the trends in the current practice of AI learning algorithms used to enhance ABMs.
*Agent-based model calibration using machine learning surrogates (2018)
https://www.sciencedirect.com/science/article/pii/S0165188918301088
Tackles parameter space exploration and calibration of agent based models by combining machine-learning and intelligent iterative sampling. Results domanstrate that machine learning surrogates obtained using the proposed iterative learning procedure provide a quite accurate proxy of the true model and dramatically reduce the computation time necessary for large scale parameter space exploration and calibration.
*Representing the acquisition and use of energy by individuals in agent‐based models of animal populations (2012)
https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210x.12002#
Exactly as the title suggests. Suggestion and evaluation of how to model animal energy needs in agent-based models.
*Using stylized agent-based models for population–environment research: a case study from the Galápagos Islands (2010)
https://link.springer.com/article/10.1007/s11111-010-0110-4
More about the utility of ABM's : here they are named useful for sharpening conceptualizations of population–environment systems, testing alternative scenarios, and uncovering critical data gaps. (Also about  trade-offs between model complexity and abstraction.)
*Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence (2018)
https://www.nature.com/articles/s41370-018-0052-y/
Context: HUMANS exposure to a chemical. Because descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments, and the existing method is difficult and labor-intensive, a simulation of longitudinal patterns in human behaviour was created. This is an agent-based model with a needs-based AI. Needs-based because humans make their decisions to take actions in order to fulfil needs. The paper describes how it is implemented. Meets critical need in field of exposure assessment. Only addresses a few needs, and not the complex ones.
* this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress.
https://ieeexplore.ieee.org/abstract/document/4141061
* This paper describes a novel system for creating virtual creatures that move  and behave in simulated three-dimensional physical worlds. A genetic language  is presented that uses nodes and connections as its primitive elements to represent directed graphs, which are used to describe both the morphology and the neural circuitry of these creatures.
http://www.karlsims.com/papers/siggraph94.pdf
* This paper explores selecting for evolvability in neural networks.  Evolvability Search enables generating evolvability more easily and directly, facilitating its study and understanding, and may inspire future practical algorithms that increase evolvability without significant computational overhead.
http://www.evolvingai.org/mengistu-lehman-clune-2016-evolvability-search-directly
* In this paper digital organisms were used to investigate the ability of natural selection to adjust and optimize mutation rates.
http://www.evolvingai.org/clune-misevic-ofria-lenski-2008-natural-selection-fails
* This paper explores novelty search, a new type of Evolutionary Algorithm, has shown much promise in the last few years. A common criticism of Novelty Search is that it is effectively random or exhaustive search because it tries solutions in an unordered manner until a correct one is found. Its creators respond that over time Novelty Search accumulates information about the environment in the form of skills relevant to reaching uncharted territory, but to date no evidence for that hypothesis has been presented.
http://www.evolvingai.org/velez-clune-2014-novelty-search-creates-robots
*Evolutionary computing (2002)
https://www.cs.vu.nl/~gusz/papers/ec-intro-Eiben-Schoenauer.pdf
This paper gives a general overview into evolutionary computing.
*Introduction to evolutionary computing (2003)
http://cslt.riit.tsinghua.edu.cn/mediawiki/images/e/e8/Introduction_to_Evolutionary_Computing.pdf
This book gives an insight into how evolutionary computing works and how it can be implemented.
===Specific animal population simulation===
* A Generalized Computer Simulation Model for Fish Population Studies
https://afspubs.onlinelibrary.wiley.com/doi/abs/10.1577/1548-8659(1969)98[505:AGCSMF]2.0.CO;2
* Application of Multi-agent Simulation in Animal Epidemic Emergency Management: Take an Example of AFS (Africa Fever Swine) Policy
http://www.dpi-proceedings.com/index.php/dtetr/article/view/31843
* VORTEX: a computer simulation model for population viability analysis
https://www.publish.csiro.au/WR/WR9930045
* An artificial intelligence modelling approach to simulating animal habitat interactions
https://www.researchgate.net/profile/Jane_Packard2/publication/237332438_An_artificial_intelligence_modelling_approach_to_simulating_animalhabitat_interactions/links/5b882577a6fdcc5f8b72005e/An-artificial-intelligence-modelling-approach-to-simulating-animal-habitat-interactions.pdf
* An overview of a simulation of an ecosystem housing predators and prey. The simulation has much hard coded behavior, allowing the simulation to get more realistic.
https://www.youtube.com/watch?v=r_It_X7v-1E
===Populations===
* An overview of a simulation of an ecosystem housing creatures based on neural networks. With robust neural networks and no hard coded behaviours, this simulation allows for more emergent behaviour and potential realism at the cost of current realism.
https://www.youtube.com/watch?v=myJ7YOZGkv0
* An overview of a simulation of an ecosystem with a complex environment. This AI has hard coded features for interacting with the environment, however it can still evolve a neural network, striking a balance between the previous two simulations.
https://www.youtube.com/watch?v=E-zcUzK8k7U
* A Study of AI Population Dynamics with Million-agent Reinforcement Learning (2018)
https://dl.acm.org/doi/10.5555/3237383.3238096
*Population based training of neural networks. Population based training discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training.
https://arxiv.org/abs/1711.09846 (the paper)
https://deepmind.com/blog/article/population-based-training-neural-networks (a blogpost about the paper)
https://www.youtube.com/watch?v=l-Ga0E9vldg (a talk about the paper)
*A talk by Jeff Clune (http://jeffclune.com/) about recent (2019) avancements in population-based search. Focusing on explicitly searching for behavioral diversity, open-ended search and indirect encoding.
https://www.youtube.com/watch?v=g6HiuEnbwJE
*Exploring the Relationship between Experiences with Digital Evolution and Students' Scientific Understanding and Acceptance of Evolution (2018)
https://avida-ed.msu.edu/files/curricula/ABT_Exploring_Relationship__Understanding_Acceptance_Evo.pdf
Uses a research-based platform for digital evolution in the classroom, found that engagement in lessons with Avida-ED both supported studentlearning of fundamental evolution concepts and was associated with an increase in student acceptance of evolution as evidence-based science. Also found a significant, positive association between increased understanding and acceptance. --> arguments for education as one of the users
*Effects of mass extinction on community stability and emergence of coordinated stasis with digital evolution (2018)
http://en.cnki.com.cn/Article_en/CJFDTotal-NJNY201801012.htm
Research based on digital evolution, can be used as application example.
=== Ecological risk assessment===
*Next-generation ecological risk assessment: Predicting risk from molecular initiation to ecosystem service delivery
https://www.sciencedirect.com/science/article/pii/S0160412016300824
There have been exciting developments in in vitro testing and high-throughput systems that measure responses to chemicals at molecular and biochemical levels of organization, but the linkage between such responses and impacts of regulatory significance – whole organisms, populations, communities, and ecosystems – are not easily predictable. This article describes some recent developments that are directed at bridging this gap and providing more predictive models that can make robust links between what we typically measure in risk assessments and what we aim to protect.
*The role of agent-based models in wildlife ecology and management (2011)
https://www.sciencedirect.com/science/article/pii/S0304380011000524
Wildlife-management ABMs disentangle habitat use and quality, and represent dynamic environments. Using adaptive movement ecology in changing landscapes permits scenario planning of future habitats. ABMs are excellent tools encompassing multiple disciplines and stakeholder interests.
Can be used to substantiate arguments about why evolution simulations are useful in wildlife ecology and management.

Latest revision as of 18:08, 3 April 2020

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