Iterated learning and grounding: from holistic to compositional languages

This paper presents a new computational model for studying the origins and evolution of compositional languages grounded through the interaction between agents and their environment. The model is based on previous work on adaptive grounding of lexicons and the iterated learning model. Although the model is still in a developmental phase, the first results show that a compositional language can emerge in which the structure reflects regularities present in the population's environment

A Praxical Solution of the Symbol Grounding Problem

Peer reviewe

Grounding semantics in robots for Visual Question Answering

In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning

Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

The adaptive advantage of symbolic theft over sensorimotor toil: Grounding language in perceptual categories

Using neural nets to simulate learning and the genetic algorithm to simulate evolution in a toy world of mushrooms and mushroom-foragers, we place two ways of acquiring categories into direct competition with one another: In (1) "sensorimotor toil,” new categories are acquired through real-time, feedback-corrected, trial and error experience in sorting them. In (2) "symbolic theft,” new categories are acquired by hearsay from propositions – boolean combinations of symbols describing them. In competition, symbolic theft always beats sensorimotor toil. We hypothesize that this is the basis of the adaptive advantage of language. Entry-level categories must still be learned by toil, however, to avoid an infinite regress (the “symbol grounding problem”). Changes in the internal representations of categories must take place during the course of learning by toil. These changes can be analyzed in terms of the compression of within-category similarities and the expansion of between-category differences. These allow regions of similarity space to be separated, bounded and named, and then the names can be combined and recombined to describe new categories, grounded recursively in the old ones. Such compression/expansion effects, called "categorical perception" (CP), have previously been reported with categories acquired by sensorimotor toil; we show that they can also arise from symbolic theft alone. The picture of natural language and its origins that emerges from this analysis is that of a powerful hybrid symbolic/sensorimotor capacity, infinitely superior to its purely sensorimotor precursors, but still grounded in and dependent on them. It can spare us from untold time and effort learning things the hard way, through direct experience, but it remain anchored in and translatable into the language of experience

How nouns and verbs differentially affect the behavior of artificial organisms

This paper presents an Artificial Life and Neural Network (ALNN) model for the evolution of syntax. The simulation methodology provides a unifying approach for the study of the evolution of language and its interaction with other behavioral and neural factors. The model uses an object manipulation task to simulate the evolution of language based on a simple verb-noun rule. The analyses of results focus on the interaction between language and other non-linguistic abilities, and on the neural control of linguistic abilities. The model shows that the beneficial effects of language on non-linguistic behavior are explained by the emergence of distinct internal representation patterns for the processing of verbs and nouns

Robots that Say ‘No’. Affective Symbol Grounding and the Case of Intent Interpretations

© 2017 IEEE. This article has been accepted for publication in a forthcoming issue of IEEE Transactions on Cognitive and Developmental Systems. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Modern theories on early child language acquisition tend to focus on referential words, mostly nouns, labeling concrete objects, or physical properties. In this experimental proof-of-concept study, we show how nonreferential negation words, typically belonging to a child's first ten words, may be acquired. A child-like humanoid robot is deployed in speech-wise unconstrained interaction with naïve human participants. In agreement with psycholinguistic observations, we corroborate the hypothesis that affect plays a pivotal role in the socially distributed acquisition process where the adept conversation partner provides linguistic interpretations of the affective displays of the less adept speaker. Negation words are prosodically salient within intent interpretations that are triggered by the learner's display of affect. From there they can be picked up and used by the budding language learner which may involve the grounding of these words in the very affective states that triggered them in the first place. The pragmatic analysis of the robot's linguistic performance indicates that the correct timing of negative utterances is essential for the listener to infer the meaning of otherwise ambiguous negative utterances. In order to assess the robot's performance thoroughly comparative data from psycholinguistic studies of parent-child dyads is needed highlighting the need for further interdisciplinary work.Peer reviewe

Symbiotic modeling: Linguistic Anthropology and the promise of chiasmus

Reflexive observations and observations of reflexivity: such agendas are by now standard practice in anthropology. Dynamic feedback loops between self and other, cause and effect, represented and representamen may no longer seem surprising; but, in spite of our enhanced awareness, little deliberate attention is devoted to modeling or grounding such phenomena. Attending to both linguistic and extra-linguistic modalities of chiasmus (the X figure), a group of anthropologists has recently embraced this challenge. Applied to contemporary problems in linguistic anthropology, chiasmus functions to highlight and enhance relationships of interdependence or symbiosis between contraries, including anthropology’s four fields, the nature of human being and facets of being human