The paper surveys work on the computational modeling of the origins and evolution of language. The main approaches are clarified and some example experiments from the domains of the evolution of communication, phonetics, lexicon formation, and syntax are discussed. 1 Introduction The paper surveys research in which software simulations and experiments with robotic agents are used to explore the viewpoint that language is a complex dynamical system. The main goal of the paper is to outline the approaches and show example experiments. Much more work needs to be done to arrive at a full-fledged theory of the origins of language and even about the work already done much more can be said than is possible in a single paper. Nevertheless, I hope to show that a new exciting approach to the study of the origins and evolution of language is taking shape. The rest of the paper is in four parts. The next section clarifies the notion of a complex system and the multi-agent perspective. Section 3...

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Abstract—A computationally implemented model of the transmission of linguistic behavior over time is presented. In this model [the iterated learning model (ILM)], there is no biological evolution, natural selection, nor any measurement of the success of the agents at communicating (except for results-gathering purposes). Nevertheless, counter to intuition, significant evolution of linguistic behavior is observed. From an initially unstructured communication system (a protolanguage), a fully compositional syntactic meaning-string mapping emerges. Furthermore, given a nonuniform frequency distribution over a meaning space and a production mechanism that prefers short strings, a realistic distribution of string lengths and patterns of stable irregularity emerges, suggesting that the ILM is a good model for the evolution of some of the fundamental features of human language. Index Terms—Cultural selection, evolution, grammar induction, iterated learning, language. I.

Information foraging theory is an approach to understanding how strategies and technologies for information seeking, gathering, and consumption are adapted to the flux of information in the environment. The theory assumes that people, when possible, will modify their strategies or the structure of the environment to maximize their rate of gaining valuable information. The theory is developed by (a) adaptation (rational) analysis of information foraging problems and (b) a detailed process model (adaptive control of thought in information foraging [ACT-IF]). The adaptation analysis develops (a) information patch models, which deal with time allocation and information filtering and enrichment activities in environments in which information is encountered in clusters; (b) information scent models, which address the identification of information value from proximal cues; and (c) information diet models, which address decisions about the selection and pursuit of information items. ACT-IF is instantiated as a production system model of people interacting with complex information technology. Humans actively seek, gather, share, and consume information to a degree unapproached by other organisms. Ours might properly be characterized as a species of informavores (Dennett, 1991). Our adaptive success depends to a large extent on a vast and complex

this paper I put forward a new approach to understanding the origins of some of the key ingredients in a syntactic system. I show, using a computational model, that compositional syntax is an inevitable outcome of the dynamics of observationally learned communication systems. In a simulated population of individuals, language develops from a simple idiosyncratic vocabulary with little expressive power, to a compositional system with high expressivity, nouns and verbs, and word order expressing meaning distinctions.

Language is a shared set of conventions for mapping meanings to expressions. This paper explores self-organization as the primary mechanism for the formation of a vocabulary. It reports on a computational experiment in which a group of distributed agents develop ways to identify each other using names or spatial descriptions. It is also shown that the proposed mechanism copes with the acquisition of an existing vocabulary by new agents entering the community and with the expansion of the set of meanings. 1 Introduction How did language originate and how are agents capable to acquire the existing language of their community? Various theories have been offered in the linguistic and cognitive science literature. One theory, proposed and defended by Chomsky and his school, states that grammar is innate [1]. More precisely, there is an innate language acquisition device based on a universal grammar so that the acquisition of a new language is a matter of setting switches (choosing paramete...

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The paper reports on experiments in which robotic agents and software agents are set up to originate language and meaning. The experiments test the hypothesis that mechanisms for generating complexity commonly found in biosystems, in particular self-organisation, co-evolution, and level formation, also may explain the spontaneous formation, adaptation, and growth in complexity of language.

Introduction As language users humans possess a culturally transmitted system of unparalleled complexity in the natural world. Linguistics has revealed over the past 40 years the degree to which the syntactic structure of language in particular is strikingly complex. Furthermore, as Pinker and Bloom point out in their agenda-setting paper Natural Language and Natural Selection \grammar is a complex mechanism tailored to the transmission of propositional structures through a serial interface" (Pinker and Bloom, 1990, 707). These sorts of observations, along with inuential arguments from linguistics and psychology about the innateness of language (see, e.g. Chomsky, 1986; Pinker, 1994), have led many authors to the conclusion that an explanation for the origin of syntax must invoke neo-Darwinian natural selection. \Evolutionary theory oers clear criteria for when a trait should be attributed to natural selection: complex design for some function, and the absence of alternative proc

A recent report demonstrated that 8-month-olds can segment a continuous stream of speech syllables, containing no acoustic or prosodic cues to word boundaries, into wordlike units after only 2 min of listening experience (Saffran, Aslin, & Newport, 1996). Thus, a powerful learning mechanism capable of extracting statistical information from fluent speech is available early in development. The present study extends these results by documenting the particular type of statistical computation—transitional (conditional) probability—used by infants to solve this word-segmentation task. An artificial language corpus, consisting of a continuous stream of trisyllabic nonsense words, was presented to 8-month-olds for 3 min. A postfamiliarization test compared the infants’ responses to words versus part-words (trisyllabic sequences spanning word boundaries). The corpus was constructed so that test words and part-words were matched in frequency, but differed in their transitional probabilities. Infants showed reliable