A bottom-up mechanism for behavior selection in an artificial creature
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Using reactive multi-agent systems in simulation and problem solving
Distributed artificial intelligence
On why better robots make it harder
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Evolving action selection and selective attention without actions, attention, or selection
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
From Natural to Artificial Swarm Intelligence
From Natural to Artificial Swarm Intelligence
Interference as a tool for designing and evaluating multi-robot controllers
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Competitive foraging, decision making, and the ecological rationality of the matching law
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
EPICURE: Spatial and Knowledge Limitations in Group Foraging
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
An elementary social information foraging model
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Multi-robot task allocation through vacancy chain scheduling
Robotics and Autonomous Systems
Collective energy homeostasis in a large-scale microrobotic swarm
Robotics and Autonomous Systems
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In this article a series of agent-based models support the hypothesis that behaviors adapted to a group situation may be suboptimal (or "irrational") when expressed by an isolated individual. These models focus on two areas of current concern in behavioral ecology and experimental psychology: the "interference function" (which relates the intake rate of a focal forager to the density of conspecifics) and the "matching law" (which formalizes the observation that many animals match the frequency of their response to different stimuli in proportion to the reward obtained from each stimulus type). Each model employs genetic algorithms to evolve foraging behaviors for multiple agents in spatially explicit environments, structured at the level of situated perception and action. A second concern of this article is to extend the understanding of both matching and interference per se by modeling at this level.