1,721,482 research outputs found
Learning the Suitability of Simple Behaviors to Obtain Composite Behaviors for Autonomous Agents
No full textthis paper, we present S-ELF, a reinforcement learning approach that learns to coordinate pre-defined basic behaviors and is derived from ELF (Evolutonary Learning of Fuzzy rules) (Bonarini, 1993), (Bonarini, 1996a). S-ELF (Symboli
Learning to Compose Fuzzy Behaviors for Autonomous Agents
No full textIn this paper, we present S-ELF, an evolutionary algorithm that we have developed to learn the context of activation of fuzzy logic controllers implementing fuzzy behaviors for autonomous agent. S-ELF learns context metarules that are used to coordinate basic behaviors in order to perform complex tasks in a partially and imprecisely known environment. Context metarules are expressed in terms of positive and negated fuzzy predicates. We also show how S-ELF can learn robust and portable behaviors, thus reducing the time and effort to design behavior-based agents . 1. Introduction Since the first Brooks's seminal papers [11] [12], many autonomous agents have been implemented following the behavior-based paradigm, where the Address correspondence to Andrea Bonarini Dipartimento di Elettronica e Informazione Politecnico di Milano Piazza Leonardo da Vinci, 32 - 20133 Milano - Italy Phone: +39 2 2399 3525 - Fax: +39 2 2399 3411 E-mail: [email protected] We would like to thank A. ..
Images and Icons in Artificial Intelligence and Robotics
No full textArtificial Intelligence and Robotics are Engineering disciplines that took their origin in highly multi-disciplinary environments, and still get contributions from different disciplines of both Engineering, Science, and Humanities. Images in
AI and Robotics play many different roles, spanning from support for research and technical achievements, to object itself of research and technical activities, to support for emotion exchange and relationship between people and machines, to icons used as recognizable markers to vulgarize achievements both in the scientific community, and on media aimed at general public information. We discuss these roles and give examples for them
Making playing robots for persons with disabilities
Full text linkPlay enables the development of skills and abilities in a way that brings satisfaction and enjoyment. Play is a right for everyone, but it is often negated to persons with disabilities both because their time is dedicated to other activities, such as therapies, and because of lack of appropriate tools and companions to play with. Robots have been proven as effective means to support development in persons with disabilities, since they provide unique opportunities and strong engagement. We present a framework to develop play situations based on robots and its application on some settings, with the aim of showing how effective, playful robots can be developed also using low-level technology at a relatively low cost. This may be a way to produce ad-hoc tools, adapted to specific situations, and, at the same time, to share experiences and ideas to foster the development of robots that can hardly reach a real market
Evolutionary learning, reinforcement learning, and fuzzy rules for knowledge acquisition in agent-based systems
No full textThe behavior of agents in complex and dynamic environments
cannot be programmed a priori, but needs to self-adapt to the spe-
cific situations. We present some approaches based on evolutionary,
reinforcement learning algorithms, able to evolve in real-time fuzzy
models that control behaviors. We discuss an application where an
agent learns how to adapt its behavior to the different behaviors
of the other agents it is interacting with, and another application
where a group of agents coevolve cooperative behaviors also by
using explicit communication to propose the cooperation and to dis-
tribute reinforcement to the others.The behavior of agents in complex and dynamic environments
cannot be programmed a priori, but needs to self-adapt to the spe-
cific situations. We present some approaches based on evolutionary,
reinforcement learning algorithms, able to evolve in real-time fuzzy
models that control behaviors. We discuss an application where an
agent learns how to adapt its behavior to the different behaviors
of the other agents it is interacting with, and another application
where a group of agents coevolve cooperative behaviors also by
using explicit communication to propose the cooperation and to dis-
tribute reinforcement to the others
Anytime learning and adaptation of fuzzy logic behaviors
No full textWe present an approach to support effective learning and adaptation of behaviors for autonomous agents with reinforcement learning algorithms. These methods can identify control systems that optimize a reinforcement program, which is, usually, a straightforward representation of the designer's goals. Reinforcement learning algorithms usually are too slow to be applied in real time on embodied agents, although they provide a suitable way to represent the desired behavior. We have tackled three aspects of this problem: the speed of the algorithm, the learning procedure, and the control system architecture. The learning algorithm we have developed includes features to speed up learning, such as niche-based learning, and a representation of the control modules in terms of fuzzy rules that reduces the search space and improves robustness to noisy data. Our learning procedure exploits methodologies such as learning from easy missions and transfer of policy from simpler environments to the more complex. The architecture of our control system is layered and modular, so that each module has a low complexity and can be learned in a short time. The composition of the actions proposed by the modules is either learned or predefined. Finally, we adopt an anytime learning approach to improve the quality of the control system on-line and to adapt it to dynamic environments.
The experiments we present in this article concern learning to reach another moving agent in a real, dynamic environment that includes nontrivial situations such as that in which the moving target is faster than the agent and that in which the target is hidden by obstacles
Robot attorno a noi: Dove sono, cosa fanno, cosa faranno?
Full text linkStiamo assistendo all’invasione silenziosa dei robot nella vita di tutti i giorni. Dall’originale impiego in fabbrica, i robot vedono ormai una crescita esponenziale al di fuori dell’ambito della produzione industriale. È importante quindi capire come fa un robot autonomo a svolgere il proprio compito, quali sono le possibilità reali di applicazione in questo momento, e quali potranno essere in un prossimo futuro. Presentiamo una rapida rassegna dello stato della robotica ad oggi, e degli sviluppi in un futuro prossimo. Infine, mostriamo alcune implicazioni sociali ed etiche che stanno emergendo di conseguenza.We are witnessing the silent invasion of robots in everyday's life. From the original use in the factory, the robots diffusion is now growing exponentially outside the scope of industrial production. It is therefore important to understand how an autonomous robot can carry out its task, what are the real possibilities of application at this time, and which ones will be possible in a near future. We present a quick review of the state of the robotics to date, and developments in the near future. Finally, we show some social and ethical implications that are about to emerge
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