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Journal Articles
Publisher: Journals Gateway
Artificial Life (2017) 23 (2): 119–123.
Published: 01 May 2017
Journal Articles
Publisher: Journals Gateway
Artificial Life (2017) 23 (2): 206–235.
Published: 01 May 2017
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Evolutionary robotics using real hardware has been almost exclusively restricted to evolving robot controllers, but the technology for evolvable morphologies is advancing quickly. We discuss a proof-of-concept study to demonstrate real robots that can reproduce. Following a general system plan, we implement a robotic habitat that contains all system components in the simplest possible form. We create an initial population of two robots and run a complete life cycle, resulting in a new robot, parented by the first two. Even though the individual steps are simplified to the maximum, the whole system validates the underlying concepts and provides a generic workflow for the creation of more complex incarnations. This hands-on experience provides insights and helps us elaborate on interesting research directions for future development.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2017) 23 (1): 80–104.
Published: 01 February 2017
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Evolutionary robotics using real hardware is currently restricted to evolving robot controllers, but the technology for evolvable morphologies is advancing quickly. Rapid prototyping (3D printing) and automated assembly are the main enablers of robotic systems where robot offspring can be produced based on a blueprint that specifies the morphologies and the controllers of the parents. This article addresses the problem of gait learning in newborn robots whose morphology is unknown in advance. We investigate a reinforcement learning method and conduct simulation experiments using robot morphologies with different size and complexity. We establish that reinforcement learning does the job well and that it outperforms two alternative algorithms. The experiments also give insights into the online dynamics of gait learning and into the influence of the size, shape, and morphological complexity of the modular robots. These insights can potentially be used to predict the viability of modular robotic organisms before they are constructed.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2010) 16 (4): 289–309.
Published: 01 October 2010
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We present a model of social learning of both language and skills, while assuming—insofar as possible—strict autonomy, virtual embodiment, and situatedness. This model is built by integrating various previous models of language development and social learning, and it is this integration that, under the mentioned assumptions, provides novel challenges. The aim of the article is to investigate what sociocognitive mechanisms agents should have in order to be able to transmit language from one generation to the next so that it can be used as a medium to transmit internalized rules that represent skill knowledge. We have performed experiments where this knowledge solves the familiar poisonous-food problem. Simulations reveal under what conditions, regarding population structure, agents can successfully solve this problem. In addition to issues relating to perspective taking and mutual exclusivity, we show that agents need to coordinate interactions so that they can establish joint attention in order to form a scaffold for language learning, which in turn forms a scaffold for the learning of rule-based skills. Based on these findings, we conclude by hypothesizing that social learning at one level forms a scaffold for the social learning at another, higher level, thus contributing to the accumulation of cultural knowledge.