185 research outputs found
Analysis of Dynamic Task Allocation in Multi-Robot Systems
Dynamic task allocation is an essential requirement for multi-robot systems
operating in unknown dynamic environments. It allows robots to change their
behavior in response to environmental changes or actions of other robots in
order to improve overall system performance. Emergent coordination algorithms
for task allocation that use only local sensing and no direct communication
between robots are attractive because they are robust and scalable. However, a
lack of formal analysis tools makes emergent coordination algorithms difficult
to design. In this paper we present a mathematical model of a general dynamic
task allocation mechanism. Robots using this mechanism have to choose between
two types of task, and the goal is to achieve a desired task division in the
absence of explicit communication and global knowledge. Robots estimate the
state of the environment from repeated local observations and decide which task
to choose based on these observations. We model the robots and observations as
stochastic processes and study the dynamics of the collective behavior.
Specifically, we analyze the effect that the number of observations and the
choice of the decision function have on the performance of the system. The
mathematical models are validated in a multi-robot multi-foraging scenario. The
model's predictions agree very closely with experimental results from
sensor-based simulations.Comment: Preprint version of the paper published in International Journal of
Robotics, March 2006, Volume 25, pp. 225-24
Interaction and Intelligent Behavior
We introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage
A Comparative Analysis of Reinforcement Learning Methods
This paper analyzes the suitability of reinforcement learning (RL) for both programming and adapting situated agents. We discuss two RL algorithms: Q-learning and the Bucket Brigade. We introduce a special case of the Bucket Brigade, and analyze and compare its performance to Q in a number of experiments. Next we discuss the key problems of RL: time and space complexity, input generalization, sensitivity to parameter values, and selection of the reinforcement function. We address the tradeoffs between the built-in and learned knowledge and the number of training examples required by a learning algorithm. Finally, we suggest directions for future research
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