International Journal of Man-Machine Studies
Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Artificial Life
Evolving collective behavior in an artificial ecology
Artificial Life
Transaction Based Risk Analysis - Using Cognitive Fuzzy Techniques
Proceedings of the IFIP TC11 WG11.1/WG11.2 Eigth Annual Working Conference on Advances in Information Security Management & Small Systems Security
A Learner's Style and Profile Recognition via Fuzzy Cognitive Map
ICALT '04 Proceedings of the IEEE International Conference on Advanced Learning Technologies
AMT'10 Proceedings of the 6th international conference on Active media technology
Correlation between genetic diversity and fitness in a predator-prey ecosystem simulation
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
The emergence of new genes in ecosim and its effect on fitness
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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We present an individual-based predator-prey model with, for the first time, each agent behavior being modeled by a fuzzy cognitive map (FCM), allowing the evolution of the agent behavior through the epochs of the simulation. The FCM enables the agent to evaluate its environment (e.g., distance to predator or prey, distance to potential breeding partner, distance to food, energy level) and its internal states (e.g., fear, hunger, curiosity), and to choose several possible actions such as evasion, eating, or breeding. The FCM of each individual is unique and is the result of the evolutionary process. The notion of species is also implemented in such a way that species emerge from the evolving population of agents. To our knowledge, our system is the only one that allows the modeling of links between behavior patterns and speciation. The simulation produces a lot of data, including number of individuals, level of energy by individual, choice of action, age of the individuals, and average FCM associated with each species. This study investigates patterns of macroevolutionary processes, such as the emergence of species in a simulated ecosystem, and proposes a general framework for the study of specific ecological problems such as invasive species and species diversity patterns. We present promising results showing coherent behaviors of the whole simulation with the emergence of strong correlation patterns also observed in existing ecosystems.