Linguistic evolution through language acquisition: Formal and computational models
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Vogt, Paul Bootstrapping grounded symbols by minimal autonomous robots. Gopych, Petro M. Mueller, Erik T. Cangelosi, Angelo Modeling the evolution of communication: From stimulus associations to grounded symbolic associations. Fulda, Joseph S. Kashkin, Vyacheslav B. Vogt, Paul The evolution of a lexicon and meaning in robotic agents through self-organization.
Hurford, Jim The interaction between numerals and nouns. Jorion, Paul Thought as word dynamics. Freedman, David A. Zellner, Brigitte Pauses and the temporal structure of speech. Muskens, Reinhard Anaphora and the Logic of Change. A simple modeling scheme confirms this scenario opening the way to further predictions. PloS one, 10, 5, Public Library of Science, Language universals have long been attributed to an innate Universal Grammar. An alternative explanation states that linguistic universals emerged independently in every language in response to shared cognitive or perceptual biases. A computational model has recently shown how this could be the case, focusing on the paradigmatic example of the universal properties of colour naming patterns, and producing results in quantitative agreement with the experimental data.
Here we investigate the role of an individual perceptual bias in the framework of the model. We study how, and to what extent, the structure of the bias influences the corresponding linguistic universal patterns. We show that the cultural history of a group of speakers introduces population-specific constraints that act against the pressure for uniformity arising from the individual bias, and we clarify the interplay between these two forces.
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Journal of the Audio Engineering Society, 62, 10, October, Language and Cognition, 6, 02, Cambridge University Press, Long-distance dependencies are notoriously diffi cult to analyze in a formally explicit way because they involve constituents that seem to have been extracted from their canonical position in an utterance. This paper rejects the filler? The proposal is supported by a computational implementation in Fluid Construction Grammar that works for both parsing and production. Computational experiments in cultural language evolution are important because they help to reveal the cognitive mechanisms and cultural processes that continuously shape and reshape the structure and knowledge of language.
However, understanding the intricate relations between these mechanisms and processes can be a daunting challenge. This paper proposes to recruit the concept of fitness landscapes from evolutionary biology and computer science for visualizing the? Through a case study on the German paradigm of definite articles, the paper shows how such landscapes can shed a new and unexpected light on non-trivial cases of language evolution.
More specifically, the case study falsifies the widespread assumption that the paradigm is the accidental by-product of linguistic erosion. Instead, it has evolved to optimize the cognitive and perceptual resources that language users employ for achieving successful communication. This thesis is devoted to the study of transportation systems by means of Complex Systems and Complex Network Theories.
Complex Networks are a tools of inestimable value in human transportation studies since in most of the cases the means of transportation used by individuals to move in space are bounded to move on a complex network. The topological properties of transportation networks can influence both the ability of individuals to move as well as their behavior in the environment, thus a characterization of the network is mandatory in order to understand the properties of the considered system.
The two transportation systems that have been studied in this work are the Air Transport System and the mobility of cars in a urban environment. The analysis and modeling of the Air Transport System is the first and most extensive part of this thesis. In particular we will try to characterize and study the networks in which aircraft fly, exploiting these results to build a data-driven model of Air Traffic Control. The second part of the thesis is a continuation of the studies performed during by Pierpaolo Mastroianni during his Master Thesis. His work concerned the analysis of GPS tracks data in the City of Rome and the inference of statistical laws characterizing the behavior of car drivers.
My contribution to his work is the development of a model capable of explaining some of the results presented in the Master Thesis. The understanding of the features and the limits of the current system could be crucial in order to improve and design the structure of the future ATM.
The ATC model is applied in order to study the efficiency of this new planned trajectories when subject to external perturbations and to compare them to the current situation. Novelties are a familiar part of daily life. They are also fundamental to the evolution of biological systems, human society, and technology. The dynamics of correlated novelties, however, have yet to be quantified empirically or modeled mathematically. Here we propose a simple mathematical model that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs.
We test these predictions on four data sets of human activity: the edit events of Wikipedia pages, the emergence of tags in annotation systems, the sequence of words in texts, and listening to new songs in online music catalogues. By quantifying the dynamics of correlated novelties, our results provide a starting point for a deeper understanding of the adjacent possible and its role in biological, cultural, and technological evolution.
Human languages are rule governed, but almost invariably these rules have exceptions in the form of irregularities. Since rules in language are efficient and productive, the persistence of irregularity is an anomaly. How does irregularity linger in the face of internal endogenous and external exogenous pressures to conform to a rule?
Here we address this problem by taking a detailed look at simple past tense verbs in the Corpus of Historical American English. The data show that the language is open, with many new verbs entering. At the same time, existing verbs might tend to regularize or irregularize as a consequence of internal dynamics, but overall, the amount of irregularity sustained by the language stays roughly constant over time.
Despite continuous vocabulary growth, and presumably, an attendant increase in expressive power, there is no corresponding growth in irregularity. We analyze the set of irregulars, showing they may adhere to a set of minority rules, allowing for increased stability of irregularity over time. These findings contribute to the debate on how language systems become rule governed, and how and why they sustain exceptions to rules, providing insight into the interplay between the emergence and maintenance of rules and exceptions in language.
Fluid Construction Grammar FCG is an open-source computational grammar formalism that is becoming increasingly popular for studying the history and evolution of language. This demonstration shows how FCG can be used to operationalise the cultural processes and cognitive mechanisms that underly language evolution and change. Constructions and Frames, 5, Construction Grammar has reached a stage of maturity where many researchers are looking for an explicit formal grounding of their work.
Unfortunately, like playing a music instrument, the formalisms used by SBCG and FCG take time and effort to master, and linguists who are unfamiliar with them may not always appreciate the far-reaching theoretical consequences of adopting this or that approach.
This paper undresses SBCG and FCG to their bare essentials, and offers a linguist-friendly comparison that looks at how both approaches define constructions, linguistic knowledge and language processing. Language Dynamics and Change, 3, The German definite article paradigm, which is notorious for its case syncretism, is widely considered to be the accidental by-product of diachronic changes. This paper argues instead that the evolution of the paradigm has been motivated by the needs and constraints of language usage.
This hypothesis is supported by experiments that compare the current paradigm to its Old High German ancestor OHG; ? Such a comparison has been made possible by? The experiments demonstrate that syncretism has made the New High German system more efficient for processing, pronunciation and perception than its historical predecessor, without harming the language? Paris France , July, The naming game NG describes the agreement dynamics of a population of N agents interacting locally in pairs leading to the emergence of a shared vocabulary.
This model has its relevance in the novel fields of semiotic dynamics and specifically to opinion formation and language evolution. The application of this model ranges from wireless sensor networks as spreading algorithms, leader election algorithms to user-based social tagging systems. In this paper, we introduce the concept of overhearing i. As one increases delta, the population of agents reaches a faster agreement with a significantly low-memory requirement. The convergence time to reach global consensus scales as log N as delta approaches 1. Despite centuries of research, the origins of grammatical case are more mysterious than ever.
This paper addresses some unanswered questions through language game experiments in which a multi-agent population self-organizes a morphosyntactic case system. The experiments show how the formal part of grammatical constructions may pressure such emergent systems to become more economical. Case has fascinated linguists for centuries without however revealing its most important secrets.
This paper offers operational explanations for case through language game experiments in which autonomous agents describe real-world events to each other. The experiments demonstrate a why a language may develop a case system, b how a population can self-organize a case system, and c why and how an existing case system may take on new functions in a language.
German case syncretism is often assumed to be the accidental by-product of historical development. This paper contradicts this claim and argues that the evolution of German case is driven by the need to optimize the cognitive effort and memory required for processing and interpretation. This hypothesis is supported by a novel kind of computational experiments that reconstruct and compare attested variations of the German definite article paradigm. The experiments show how the intricate interaction between those variations and the rest of the German? Linguistic utterances are full of errors and novel expressions, yet linguistic communication is remarkably robust.
This paper presents a double-layered architecture for open-ended language processing, in which? Through concrete operational examples, this paper demonstrates how such an architecture can directly monitor and steer linguistic processing, and how language can be embedded in a larger cognitive system. Almost all languages in the world have a way to formulate commands. Action language involves various competences, in particular i the ability to perform an action and recognize which action has been performed by others the so-called mirror problem , and ii the ability to identify which objects are to participate in the action e.
This chapter describes evolutionary language game experiments exploring how these competences originate, can be carried out and acquired, by real robots, using evolutionary language games and a whole systems approach. Cognitive linguistics has reached a stage of maturity where many researchers are looking for an explicit formal grounding of their work. Unfortunately, most current models of deep language processing incorporate assumptions from generative grammar that are at odds with the cognitive movement in linguistics.
This demonstration shows how Fluid Construction Grammar FCG , a fully operational and bidirectional unification-based grammar formalism, caters for this increasing demand. FCG features many of the tools that were pioneered in computational linguistics in the 70ss, but combines them in an innovative way. This demonstration highlights the main differences between FCG and related formalisms.
This chapter introduces a new experimental paradigm for studying issues in the grounding of language and robots, and the integration of all aspects of intelligence into a single system. The paradigm is based on designing and implementing artificial agents so that they are able to play language games about situations they perceive and act upon in the real world.
The agents are not pre-programmed with an existing language but with the necessary cognitive functions to self-organize communication systems from scratch, to learn them from human language users if there are sufficiently frequent interactions, and to participate in the on-going cultural evolution of language. This chapter introduces very briefly the framework and tools for lexical and grammatical processing that have been used in the evolutionary language game experiments reported in this book.
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This framework is called Fluid Construction Grammar FCG because it rests on a constructional approach to language and emphasizes flexible grammar application. Construction grammar organizes the knowledge needed for parsing or producing utterances in terms of bi-directional mappings between meaning and form. In line with other contemporary linguistic formalisms, FCG uses feature structures and unification and includes several innovations which make the formalism more adapted to implement flexible and robust language processing systems on real robots. This chapter is an introduction to the formalism and how it is used in processing.
This chapter introduces the computational infrastructure that is used to bridge the gap between results from sensorimotor processing and language. It consists of a system called Incremental Recruitment Language IRL that is able to configure a network of cognitive operations to achieve a particular communicative goal. IRL contains mechanisms for finding such networks, chunking subnetworks for more efficient later reuse, and completing partial networks as possibly derived from incomplete or only partially understood sentences. This chapter describes key aspects of a visual perception system as a key component for language game experiments on physical robots.
The vision system is responsible for segmenting the continuous flow of incoming visual stimuli into segments and computing a variety of features for each segment. This happens by a combination of bottom-up way processing that work on the incoming signal and top-down processing based on expectations about what was seen before or objects stored in memory. This chapter consists of two parts. The first one is concerned with extracting and maintaining world models about spatial scenes, without any prior knowledge of the possible objects involved. The second part deals with the recognition of gestures and actions which establish the joint attention and pragmatic feedback that is an important aspect of language games.
This chapter explores a semantics-oriented approach to the origins of syntactic structure.
It reports on preliminary experiments whereby speakers introduce hierarchical constructions and grammatical markers to express which conceptualization strategy hearers are supposed to invoke. This grammatical information helps hearers to avoid semantic ambiguity or errors in interpretation. A simulation study is performed for spatial grammar using robotic agents that play language games about objects in their shared world. Grounding language in sensorimotor spaces is an important and difficult task.
In order, for robots to be able to interpret and produce utterances about the real world, they have to link symbolic information to continuous perceptual spaces. This requires dealing with inherent vagueness, noise and differences in perspective in the perception of the real world. This paper presents two case studies for spatial language and quantification that show how cognitive operations — the building blocks of grounded procedural semantics — can be efficiently grounded in sensorimotor spaces. Russian requires speakers of the language to conceptualize events using temporal language devices such as Aktionsarten and aspect, which relate to particular profiles and characteristics of events such as whether the event just started, whether it is ongoing or it is a repeated event.
This chapter explores how such temporal features of events can be processed and learned by robots through grounded situated interactions. We use a whole systems approach, tightly integrating perception, conceptualization grammatical processing and learning and demonstrate how a system of Aktionsarten can be acquired.
Basic postures such as sit, stand and lie are ubiquitous in human interaction. In order to build robots that aid and support humans in their daily life, we need to understand how posture categories can be learned and recognized. This paper presents an unsupervised learning approach to posture recognition for a biped humanoid robot. The approach is based on Slow Feature Analysis SFA , a biologically inspired algorithm for extracting slowly changing signals from signals varying on a fast time scale.
Two experiments are carried out: First, we consider the problem of recognizing static postures in a multimodal sensory stream which consists of visual and proprioceptive stimuli. Secondly, we show how to extract a low-dimensional representation of the sensory state space which is suitable for posture recognition in a more complex setting. We point out that the beneficial performance of SFA in this task can be related to the fact that SFA computes manifolds which are used in robotics to model invariants in motion and behavior.
Based on this insight, we also propose a method for using SFA components for guided exploration of the state space. This chapter introduces the modular humanoid robot Myon, covering its mechatronical design, embedded low-level software, distributed processing architecture, and the complementary experimental environment. The Myon humanoid is the descendant of various robotic hardware platforms which have been built over the years and therefore combines the latest research results on the one hand, and the expertise of how a robot has to be built for experiments on embodiment and language evolution on the other hand.
In contrast to many other platforms, the Myon humanoid can be used as a whole or in parts. Both the underlying architecture and the supportive application software allow for ad hoc changes in the experimental setup. This chapter studies how basic spatial categories such as left-right, front-back, far-near or north-south can emerge in a population of robotic agents in co-evolution with terms that express these categories. It introduces various language strategies and tests them first in reconstructions of German spatial terms, then in acquisition experiments to demonstrate the adequacy of the strategy for learning these terms, and finally in language formation experiments showing how a spatial vocabulary and the concepts expressed by it can emerge in a population of embodied agents from scratch.
This chapter investigates how a vocabulary for talking about body actions can emerge in a population of grounded autonomous agents instantiated as humanoid robots. The agents play a Posture Game in which the speaker asks the hearer to take on a certain posture. The speaker either signals success if the hearer indeed performs an action to achieve the posture or he shows the posture himself so that the hearer can acquire the name. The challenge of emergent body language raises not only fundamental issues in how a perceptually grounded lexicon can arise in a population of autonomous agents but also more general questions of human cognition, in particular how agents can develop a body model and a mirror system so that they can recognize actions of others as being the same as their own.
This chapter explores a possible language strategy for verbalizing aspect: the encoding of Aktionsarten by means of morphological markers. Russian tense-aspect system is used as a model. We first operationalize this system and reconstruct the learning operators needed for acquiring it. Then we perform a first language formation experiment in which a novel system of Aktionsarten emerges and gets coordinated between the agents, driven by a need for higher expressivity.
Language change is increasingly recognized as one of the most crucial sources of evidence for understanding human cognition. Unfortunately, despite sophisticated methods for documenting which changes have taken place, the question of why languages evolve over time remains open for speculation. This paper presents a novel research method that addresses this issue by combining agent-based experiments with deep language processing, and demonstrates the approach through a case study on German definite articles.
More specifically, two populations of autonomous agents are equipped with a model of Old High German ? The experiments show that inefficiencies detected in the grammar by the Old High German agents correspond to grammatical forms that have actually undergone the most important changes in the German language. The results thus suggest that the question of language change can be reformulated as an optimization problem in which language users try to achieve their communicative goals while allocating their cognitive resources as efficiently as possible.
Advances in Complex Systems, 15, , The question how a shared vocabulary can arise in a multi-agent population despite the fact that each agent autonomously invents and acquires words has been solved. The solution is based on alignment: Agents score all associations between words and meanings in their lexicons and update these preference scores based on communicative success. A positive feedback loop between success and use thus arises which causes the spontaneous self-organization of a shared lexicon.
The same approach has been proposed for explaining how a population can arrive at a shared grammar, in which we get the same problem of variation because each agent invents and acquires their own grammatical constructions. However, a problem arises if constructions reuse parts that can also exist on their own. This happens particularly when frequent usage patterns, which are based on compositional rules, are stored as such.
The problem is how to maintain systematicity. This paper identifies this problem and proposes a solution in the form of multilevel alignment. Multilevel alignment means that the updating of preference scores is not restricted to the constructions that were used in the utterance but also downward and upward in the subsumption hierarchy. Becoming a proficient speaker of a language requires more than just learning a set of words and grammar rules, it also implies mastering the ways in which speakers of that language typically innovate: stretching the meaning of words, introducing new grammatical constructions, introducing a new category, and so on.
This paper demonstrates that such meta-knowledge can be represented and applied by reusing similar representations and processing techniques as needed for routine linguistic processing, which makes it possible that language processing makes use of computational reflection. The fascinating question of the origins and evolution of language has been drawing a lot of attention recently, not only from linguists, but also from anthropologists, evolutionary biologists, and brain scientists. This groundbreaking book explores the cultural side of language evolution.
It proposes a new overarching framework based on linguistic selection and self-organization and explores it in depth through sophisticated computer simulations and robotic experiments. Each case study investigates how a particular type of language system can emerge in a population of language game playing agents and how it can continue to evolve in order to cope with changes in ecological conditions.
Case studies cover on the one hand the emergence of concepts and words for proper names, color terms, names for bodily actions, spatial terms and multi-dimensional words. The second set of experiments focuses on the emergence of grammar, specifically case grammar for expressing argument structure, functional grammar for expressing different uses of spatial relations, internal agreement systems for marking constituent structure, morphological expression of aspect, and quantifiers expressed as articles.
The book is ideally suited as study material for an advanced course on language evolution and it will be of interest to anyone who wonders how human languages may have originated. Written by leading international experts, this volume presents contributions establishing the feasibility of human language-like communication with robots. The book explores the use of language games for structuring situated dialogues in which contextualized language communication and language acquisition can take place. Within the text are integrated experiments demonstrating the extensive research which targets artificial language evolution.
Language Grounding in Robots uses the design layers necessary to create a fully operational communicating robot as a framework for the text, focusing on the following areas: Embodiment; Behavior; Perception and Action; Conceptualization; Language Processing; Whole Systems Experiments. This book serves as an excellent reference for researchers interested in further study of artificial language evolution. The lexicons of human languages organize their units at two distinct levels.
At a first combinatorial level, meaningless forms typically referred to as phonemes are combined into meaningful units typically referred to as morphemes. Thanks to this, many morphemes can be obtained by relatively simple combinations of a small number of phonemes.
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At a second compositional level of the lexicon, morphemes are composed into larger lexical units, the meaning of which is related to the individual meanings of the composing morphemes. This duality of patterning is not a necessity for lexicons and the question remains wide open regarding how a population of individuals is able to bootstrap such a structure and the evolutionary advantages of its emergence.
Here we address this question in the framework of a multi-agents model, where a population of individuals plays simple naming games in a conceptual environment modeled as a graph. We demonstrate that errors in communication conditions for the emergence of duality of patterning, that can thus be explained in a pure cultural way. Compositional lexicons turn out to be faster to lead to successful communication thanpurely combinatorial lexicons, suggesting that meaning played a crucial role in the evolution of language.
One of the fundamental problems in cognitive science is how humans categorize the visible color spectrum. The empirical evidence of the existence of universal or recurrent patterns in color naming across cultures is paralleled by the observation that color names begin to be used by individual cultures in a relatively fixed order. The origin of this hierarchy is largely unexplained.
Here we resort to multiagent simulations, where a population of individuals, subject to a simple perceptual constraint shared by all humans, namely the human Just Noticeable Difference, categorizes and names colors through a purely cultural negotiation in the form of language games. We found that the time needed for a population to reach consensus on a color name depends on the region of the visible color spectrum. Our results demonstrate a clear possible route to the emergence of hierarchical color categories, confirming that the theoretical modeling in this area has now attained the required maturity to make significant contributions to the ongoing debates concerning language universals.
Geoscientific Model Development, 4, 3, All languages of the world have a way to talk about space and spatial relations of objects. Cross-culturally, immense variation in how people conceptualize space for language has been attested. Different spatial conceptualization strategies such as proximal, projective and absolute have been identified to underlie peoples conception of spatial reality.
This paper argues that spatial conceptualization strategies are negotiated in a cultural process of linguistic selection. Conceptualization strategies originate in the cognitive capabilities of agents. The ecological conditions and the structure of the environment influence the conceptualization strategy agents invent and which corresponding system of lexicon and ontology of spatial relations is selected for. The validity of these claims is explored using populations of humanoid robots.
This paper compares two prominent approaches in artificial language evolution: Iterated Learning and Social Coordination. More specifically, the paper contrasts experiments in both approaches on how populations of artificial agents can autonomously develop a grammatical case marking system for indicating event structure i. The comparison demonstrates that only the Social Coordination approach leads to a shared communication system in a multi-agent population.
The paper concludes with an analysis and discussion of the results, and argues that Iterated Learning in its current form cannot explain the emergence of more complex natural language-like phenomena. Physics of Life Reviews, 8, 4, December, The paper surveys recent research on language evolution, focusing in particular on models of cultural evolution and how they are being developed and tested using agent-based computational simulations and robotic experiments. The key challenges for evolutionary theories of language are outlined and some example results are discussed, highlighting models explaining how linguistic conventions get shared, how conceptual frameworks get coordinated through language, and how hierarchical structure could emerge.
The main conclusion of the paper is that cultural evolution is a much more powerful process that usually assumed, implying that less innate structures or biases are required and consequently that human language evolution has to rely less on genetic evolution. This paper presents a design pattern for handling argument structure and offers a concrete operationalization of this pattern in Fluid Construction Grammar.
Argument structure concerns the mapping between?ufn-web.com/wp-includes/41/installer-logiciel-espion-portable-gratuit.php
This mapping is multilayered and indirect, which poses great challenges for grammar design. In the proposed design pattern, lexico-phrasal constructions introduce their semantic and syntactic potential of linkage. Argument structure constructions, then, select from this potential the values that they require and implement the actual linking. This paper illustrates the use of? Both phenomena involve forms that can be mapped onto multiple, often conflicting values.
This paper illustrates their respective challenges through German case agreement, which has become the litmus test for demonstrating how well a grammar formalism deals with multifunctionality. After reviewing two traditional solutions, the paper demonstrates how complex grammatical categories can be represented as feature matrices instead of single-valued features. Feature matrices allow a free flow of constraints on possible feature-values coming from any part of an utterance, and they postpone commitment to any particular value until sufficient constraints have been identified.
All examples in this paper are operationalized in Fluid Construction Grammar, but the design principle can be extended to other unification-grammars as well. Natural languages are fluid. New conventions may arise and there is never absolute consensus in a population. How can human language users nevertheless have such a high rate of communicative success?
And how do they deal with the incomplete sentences, false starts, errors and noise that is common in normal discourse? Fluidity, ungrammaticality and error are key problems for formal descriptions of language and for computational implementations of language processing because these seem to be necessarily rigid and mechanical. This chapter discusses how these issues are approached within the framework of Fluid Construction Grammar.
Fluidity is not achieved by a single mechanism but through a combination of intelligent grammar design and flexible processing principles. One of the key components for achieving flexible, robust, adaptive and open-ended language-based communication between humans and robots — or between robots and robots — is rich deep semantics. AI has a long tradition of work in the representation of knowledge, most of it within the logical tradition.
This tradition assumes that an autonomous agent is able to derive formal descriptions of the world which can then be the basis of logical inference and natural language understanding or production. This paper outlines some difficulties with this logical stance and reports alternative research on the development of an?
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