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This paper analyzes the emergent behaviors of pedestrian groups that learn through the multiagent reinforcement learning model developed in our group. Five scenarios studied in the pedestrian model literature, and with different levels of complexity, were simulated in order to analyze the robustness and the scalability of the model. Firstly, a reduced group of agents must learn by interaction with...
Small-sided and conditioned games (SSCGs) in sport have been modelled as complex adaptive systems. Research has shown that the relative space per player (RSP) formulated in SSCGs can impact on emergent tactical behaviours. In this study we adopted a systems orientation to analyse how different RSP values, obtained through manipulations of player numbers, influenced four measures of interpersonal coordination...
Within this paper we present a cooperative adaptive algorithm for the management of a team of Autonomous Underwater Vehicles (AUVs) that are the mobile nodes of an ad-hoc underwater network and share the mission objective of protecting an asset while maintaining reliable acoustic communication links among themselves. Each vehicle is considered to be equipped with a detection sonar for asset protection...
We explore the emergence of behaviours in a chaos driven robot. A two neurons neural network exhibits a rich variety of dynamics when stimulated with input. We linked the phase space of the network to two behaviour activation levels. A people detector is the input to the network. The results are surprising and they contradict the expectations, which is a clear signal of the emergence of non-programmed...
Synthesis of state machine designs from scenarios must cope with two main problems, namely, generalizing partial behaviours of scenarios and preventing from overgeneralization that produces spurious emergent behaviours. The challenge is a trade-off between automatic generalization in one hand, and the effort and time spent for resolving spurious emergent behaviours on the other hand. In this paper,...
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