The advent of humanoid robots has enabled a new approach to investigating the acquisition of language, and we report on the development of robots able to acquire rudimentary linguistic skills. Our work focuses on early stages analogous to some characteristics of a human child of about 6 to 14 months, the transition from babbling to first word forms. We investigate one mechanism among many that may contribute to this process, a key factor being the sensitivity of learners to the statistical distribution of linguistic elements. As well as being necessary for learning word meanings, the acquisition of anchor word forms facilitates the segmentation of an acoustic stream through other mechanisms. In our experiments some salient one-syllable word forms are learnt by a humanoid robot in real-time interactions with naive participants. Words emerge from random syllabic babble through a learning process based on a dialogue between the robot and the human participant, whose speech is perceived by the robot as a stream of phonemes. Numerous ways of representing the speech as syllabic segments are possible. Furthermore, the pronunciation of many words in spontaneous speech is variable. However, in line with research elsewhere, we observe that salient content words are more likely than function words to have consistent canonical representations; thus their relative frequency increases, as does their influence on the learner. Variable pronunciation may contribute to early word form acquisition. The importance of contingent interaction in real-time between teacher and learner is reflected by a reinforcement process, with variable success. The examination of individual cases may be more informative than group results. Nevertheless, word forms are usually produced by the robot after a few minutes of dialogue, employing a simple, real-time, frequency dependent mechanism. This work shows the potential of human-robot interaction systems in studies of the dynamics of early language acquisition.
}, + number = {6}, + doi = {10.1371/journal.pone.0038236} +} + + + + diff --git a/tests/data/martius.bib b/tests/data/martius.bib new file mode 100644 index 0000000..63f62c9 --- /dev/null +++ b/tests/data/martius.bib @@ -0,0 +1,19 @@ + +@article{10.1371_journal.pone.0063400, + author = {Georg Martius AND Ralf Der AND Nihat Ay}, + journal = {PLoS ONE}, + publisher = {Public Library of Science}, + title = {Information Driven Self-Organization of Complex Robotic Behaviors}, + year = {2013}, + month = {05}, + volume = {8}, + url = {http://dx.doi.org/10.1371%2Fjournal.pone.0063400}, + pages = {e63400}, + abstract = {Information theory is a powerful tool to express principles to drive autonomous systems because it is domain invariant and allows for an intuitive interpretation. This paper studies the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process as a driving force to generate behavior. We study nonlinear and nonstationary systems and introduce the time-local predicting information (TiPI) which allows us to derive exact results together with explicit update rules for the parameters of the controller in the dynamical systems framework. In this way the information principle, formulated at the level of behavior, is translated to the dynamics of the synapses. We underpin our results with a number of case studies with high-dimensional robotic systems. We show the spontaneous cooperativity in a complex physical system with decentralized control. Moreover, a jointly controlled humanoid robot develops a high behavioral variety depending on its physics and the environment it is dynamically embedded into. The behavior can be decomposed into a succession of low-dimensional modes that increasingly explore the behavior space. This is a promising way to avoid the curse of dimensionality which hinders learning systems to scale well.
}, + number = {5}, + doi = {10.1371/journal.pone.0063400} +} + + + + diff --git a/tests/data/turing-mind-1950.pdf b/tests/data/turing-mind-1950.pdf new file mode 100644 index 0000000..4f1ba38 Binary files /dev/null and b/tests/data/turing-mind-1950.pdf differ diff --git a/tests/data/turing1950.bib b/tests/data/turing1950.bib new file mode 100644 index 0000000..2f53fa5 --- /dev/null +++ b/tests/data/turing1950.bib @@ -0,0 +1,10 @@ +@article{turing1950computing, + title={Computing machinery and intelligence}, + author={Turing, Alan M}, + journal={Mind}, + volume={59}, + number={236}, + pages={433--460}, + year={1950}, + publisher={JSTOR} +}