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...

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.

The paper reports on experiments with a population of visually grounded robotic agents capable of bootstrapping their own ontology and shared lexicon without prior design nor other forms of human intervention.

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

this paper. The language learning device clearly does impose constraints directly in a similar fashion --- there are certain types of language that the learner simply cannot acquire --- however these constraints are far less severe than those imposed by the LAD. As can be seen in the initial stages of the simulation, very un-language like systems can be acquired by this learner. The constraints on variation are not built into the learner, but are instead emergent properties of the social dynamics of learned communication systems and the structure of the semantic space that the individuals wish to express. The theory presented here gives us a neat explanation of why human languages use syntactic structure to compositionally derive semantics, have recursive subordination to express infinite distinctions in a digital way, have words with major syntactic categories such as noun and verb, and use structural relations (such as word order) to encode meaning distinctions. However, it does not seem to allow us to understand more specific universals. For example, why particular constituent orders are far more frequent than others across the languages of the world (Hawkins 1983; Dryer 1992). Perhaps the best explanation for these types of universal should look at the effect of parsing and generation in the transmission of replicators (see Kirby 1998a; Kirby 1997 for details). On the other hand, at least some of these word order constraints may eventually be explained in terms of linguistic adaptation without appealing to processing (see, Christiansen 1994; Christiansen & Devlin 1997 for some suggestions along these lines). X-bar theory --- a sub part of UG which constrains the structure of syntactic trees cross categorially (Jackendoff 1977) --- has been implicated in various word...

Human language is a unique ability. It sits apart from other systems of communication in two striking ways: it is syntactic, and it is learned. While most approaches to the evolution of language have focused on the evolution of syntax, this paper explores the computational issues that arise in shifting from a simple innate communication system to an equally simple one that is learned. Associative network learning within an observational learning paradigm is used to explore the computational difficulties involved in establishing and maintaining a simple learned communication system. Because Hebbian learning is found to be sufficient for this task, it is proposed that the basic computational demands of learning are unlikely to account for the rarity of even simple learned communication systems. Instead, it is the problem of observing that is likely to be central -- in particular the problem of determining what meaning a signal is intended to convey. 1 The learning barrier There is a lon...

. We report on a case study in the emergence of a lexicon in a group of autonomous distributed agents situated and grounded in an open environment. Because the agents are autonomous, grounded, and situated, the possible words and possible meanings are not fixed but continuously change as the agents autonomously evolve their communication system and adapt it to novel situations. The case study shows that a complex semiotic dynamics unfolds and that generalisations present in the language are due to processes outside the agent. 1 Introduction In recent years it has become clear that the complex adaptive systems approach pioneered by Artificial Life research can fruitfully be applied to the study of the origins and evolution of language [9], particularly to the emergence of shared sound systems [3], the self-organisation of lexicons [7], [11], grounded word meaning [12], and the origins of grammar [4], [1], [5]. In all this research, the same mechanisms for the generation and mai...

Recent work on viewing language as a complex adaptive system has shown that self-organisation can explain how a group of distributed agents can reach a coherent set of linguistic conventions and how such a set can be preserved from one generation to the next based on cultural transmission. The paper continues these investigations by exploring the presence of stochasticity in the various aspects of lexical communication: stochasticity in the nonlinguistic communication constraining meaning, the transmission of the message, and the retrieval from memory. We show that there is an upperbound on the amount of stochasticity which can be tolerated and that stochasticity causes and maintains language variation. Results are based on the further exploration of a minimal computational model of language interaction in a group of distributed agents, called the naming game. Keywords: origins of language, evolution of language, self-organization. 1 Introduction Exciting recent res...

We explore how evolutionary game dynamics have to be modified to accomodate a mathematical framework for the evolution of language. In particular, we are interested in the evolution of vocabulary, that is associations between signals and objects. We assume that successful communication contributes to biological fitness: individuals who communicate well leave more offspring. Children inherit from their parents a strategy for language learning (a language acquisition device). We consider three mechanisms whereby language is passed from one generation to the next: (i) parental learning: children learn the language of their parents; (ii) role model learning: children learn the language of individuals with a high payoff; and (iii) random learning: children learn the language of randomly chosen individuals. We show that parental and role model learning outperform random learning. Then we introduce mistakes in language learning and study how this process changes language over time. Mistakes increase the overall efficacy of parental and role model learning: in a world with errors evolutionary adaptation is more e$cient. Our model also provides a simple explanation why homonomy is common while synonymy is rare.

this paper is on procedures whereby new (e.g., juvenile) members of a population could learn to communicate with the other members by observing their communicative behavior. Two apparently distinct issues are relevant to the evaluation of such learning procedures. First, the procedure must enable the new members to accurately acquire the communication system of the population, even though their observations may be limited, noisy, or otherwise misleading. Second, the learning procedure used by its new members will affect the population's communication system over time. The use of a particular procedure might result in the population's communication increasing in coordination, ultimately yielding a nearly optimally coordinated system. If a learning procedure were to satisfy both criteria, it could explain how learned communication systems are maintained over time, as well as how they are established in the first place.