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Conference

Simulation of Adaptive Behavior 

About: Simulation of Adaptive Behavior is an academic conference. The conference publishes majorly in the area(s): Robot & Mobile robot. Over the lifetime, 1034 publication(s) have been published by the conference receiving 18432 citation(s).
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Proceedings Article
14 Feb 1991
TL;DR: A distributed sorting algorithm, inspired by how ant colonies sort their brood, is presented for use by robot teams, offering the advantages of simplicity, flexibility and robustness.
Abstract: A distributed sorting algorithm, inspired by how ant colonies sort their brood is presented for use by robot teams The robots move randomly, do not communicate have no hierarchical organisation, have no global representation can only perceive objects just in front of them, but can distinguish between objects of two or more types with a certain degree of error The probability that they pick up or put down an object is modulated as a function of how many of the same objects they have met in the recent past This generates a positive feed-back that is sufficient to coordinate the robots' activity, resulting in their sorting the objects into common clusters While less efficient than a hierarchically controlled sorting, this decentralised organisation offers the advantages of simplicity, flexibility and robustness

960 citations


Proceedings Article
01 Jul 1994

615 citations


Proceedings Article
14 Feb 1991
TL;DR: It is described how the particular algorithm (as well as similar model-building algorithms) may be augmented by dynamic curiosity and boredom in a natural manner by introducing (delayed) reinforcement for actions that increase the model network's knowledge about the world.
Abstract: This paper introduces a framework for`curious neural controllers' which employ an adaptive world model for goal directed on-line learning. First an on-line reinforcement learning algorithm for autonomousànimats' is described. The algorithm is based on two fully recurrent`self-supervised' continually running networks which learn in parallel. One of the networks learns to represent a complete model of the environmental dynamics and is called thèmodel network'. It provides completècredit assignment paths' into the past for the second network which controls the animats physical actions in a possibly reactive environment. The an-imats goal is to maximize cumulative reinforcement and minimize cumulativèpain'. The algorithm has properties which allow to implement something like the desire to improve the model network's knowledge about the world. This is related to curiosity. It is described how the particular algorithm (as well as similar model-building algorithms) may be augmented by dynamic curiosity and boredom in a natural manner. This may be done by introducing (delayed) reinforcement for actions that increase the model network's knowledge about the world. This in turn requires the model network to model its own ignorance, thus showing a rudimentary form of self-introspective behavior.

400 citations


Proceedings ArticleDOI
01 Jul 1994
TL;DR: The paper describes the results of the evolutionary development of a real, neural-network driven mobile robot, and shows a number of emergent phenomena that are characteristic of autonomous agents.
Abstract: The paper describes the results of the evolutionary development of a real, neural-network driven mobile robot. The evolutionary approach to the development of neural controllers for autonomous agents has been successfully used by many researchers, but most - if not all - studies have been carried out with computer simulations. Instead, in this research the whole evolutionary process takes places entirely on a real robot without human intervention. Although the experiments described here tackle a simple task of navigation and obstacle avoidance, we show a number of emergent phenomena that are characteristic of autonomous agents. The neural controllers of the evolved best individuals display a full exploitation of non-linear and recurrent connections that make them more efficient than analogous man-designed agents. In order to fully understand and describe the robot behavior, we have also employed quantitative ethological tools [13], and showed that the adaptation dynamics conform to predictions made for animals.

383 citations


Proceedings Article
09 Aug 1993

369 citations


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Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
202039
201926
201852
2017261
201641
201432