Digital topology: introduction and survey
Computer Vision, Graphics, and Image Processing
Massively parallel artificial intelligence
Effects of different interaction attitudes on a multi-agent system performance
MAAMAW '96 Proceedings of the 7th European workshop on Modelling autonomous agents in a multi-agent world : agents breaking away: agents breaking away
Cooperative strategies and the evolution of communication
Artificial Life
Advances in Digital and Computational Geometry
Advances in Digital and Computational Geometry
Aggressive Signaling Meets Adaptive Receiving: Further Experiments in Synthetic Behavioural Ecology
Proceedings of the Third European Conference on Advances in Artificial Life
Exploring Agent Cooperation: Studies with a Simple Pursuit Game
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Emergent Properties of Teams of Agents in the Tileworld
PRICAI '96 Proceedings from the Workshop on Intelligent Agent Systems, Theoretical and Practical Issues
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In a number of multi-agent artificial life studies where agents interact over limited distances, the emergence and/or evolution of a specific behavior may depend critically upon interagent distances. Little theoretical analysis has been done previously concerning how to predict such distances. In this paper, we derive a probabilistic method that, for an agent at an arbitrary location in a two-dimensional cellular world, predicts the expected distance to a nearest other agent. Our method works for many world topologies, and we apply it to determine the expected distance for six commonly used ones. Further, the method is readily adapted to handle special restrictions. Over a wide variety of agent densities we show that the theoretically predicted distances are largely in agreement with the distances measured in computational experiments with randomly placed agents. We then utilize our prediction method to interpret recent observations that an imprecise threshold in the density of agents exists for the evolution of communication. We thus illustrate that, despite its conceptual simplicity, our method can aid the analysis and even the design of complex artificial environments populated by agents that have the potential to interact with one another.