Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Synthetic ethology and the evolution of cooperative communication
Adaptive Behavior
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
The philosophical computer: exploratory essays in philosophical computer modeling
The philosophical computer: exploratory essays in philosophical computer modeling
Cooperative strategies and the evolution of communication
Artificial Life
Predicting nearest agent distances in artificial worlds
Artificial Life
Information and Meaning: Use-Based Models in Arrays of Neural Nets
Minds and Machines
Simulating grice: emergent pragmatics in spatialized game theory
Language, games, and evolution
A simple metric for turn-taking in emergent communication
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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We work with a large spatialized array of individuals in an environment of drifting food sources and predators. The behavior of each individual is generated by its simple neural net; individuals are capable of making one of two sounds and are capable of responding to sounds from their immediate neighbors by opening their mouths or hiding. An individual whose mouth is open in the presence of food is "fed" and gains points; an individual who fails to hide when a predator is present is "hurt" by losing points. Opening mouths, hiding, and making sounds each exact an energy cost. There is no direct evolutionary gain for acts of cooperation or "successful communication" per se.In such an environment we start with a spatialized array of neural nets with randomized weights. Using standard learning algorithms, our individuals "train up" on the behavior of successful neighbors at regular intervals. Given that simple setup, will a community of neural nets evolve a simple language for signaling the presence of food and predators? With important qualifications, the answer is "yes." In a simple spatial environment, pursuing individualistic gains and using partial training on successful neighbors, randomized neural nets can learn to communicate.