Language Evolution and Computation Bibliography

Rhythm is common in courtship signals of many species. Here we explore whether regularly repeating rhythmic patterns can serve as indicators of underlying mate quality. We find through simulation that rhythmic signals allow the greatest discrimination between high- and low-quality males when low quality is associated with timing errors in artificial songs. However, rhythmic signals are difficult to evolve in our framework, leading to the conclusion that other pressures may have been involved in their appearance.

The main contribution of this paper is twofold. Firstly, it presents a new system for empirical investigations of evolving agent societies in SugarScapelike environments, which improves existing Sugarscape testbeds. Secondly, we introduce a framework for modelling communication and cooperation in an animal society. In this framework the environmental pressure to communicate and cooperate is controllable by a single parameter. We perform several experiments with different values for this parameter and observe some surprising outcomes.

Pure synonymy is rare. By contrast, homonymy is common in languages. Human avoidance of synonymy is plausibly innate, as theorists of differing persuasions have claimed. Innate dispositions to synonymy and homonymy are modelled here, in relation to alternative roles of speaking and hearing in determining fitness.

In the computer model, linguistic signs are acquired via different genetically determined strategies, variously (in)tolerant to synonymy or homonymy. The model defines communicative success as the probability of a speaker getting a message across to a hearer; interpretive success is the probability of a hearer correctly interpreting a speaker's signal. Communicative and interpretive success are compared as bases for reproductive fitness. When communicative success is the basis for fitness, a genotype evolves which is averse to synonymy, while tolerating homonymy. Conversely, when interpretive success is the basis for fitness, a genotype evolves which is averse to homonymy, while tolerating synonymy.

This paper looks at the way signaling behaviour can arise within a population of evolving agents involved in complex task domains where problem-solving behaviours need to be developed and integrated with appropriate signaling strategies. A method is proposed to overcome the difficulties of evolving separate yet compatible parts required by transmitters and receivers that serve no function but communication. The validity of this method is supported by a series of experiments. These not only succeed in evolving agents capable of controlling and enhancing complex behaviours through signaling but also demonstrate how bigger search spaces with more signal channels than might be needed can lead to faster adaptation.

This paper presents an adaptation of Luc Steels's model of Category Formation and Language Sharing. The simple competitive learning algorithm is proposed as a more general means of creating categories from real-world perception. The model is shown to achieve high levels of coherence and to be very robust when two distinct populations are mixed together, with both populations learning each other's words.

Language is a complex phenomenon. Utterances arise from complex interactions between semantics and grammar. Usually, semantics and grammar are studied separately from each other. This paper introduces a model that makes it possible to study the interaction between these two parts of language. The model comprises a population of agents that feature a relatively complex semantic module, and a lexicalisation module that can produce utterances from semantic representations. The language the agents use to communicate is developed through their interactions, without central control. The concrete focus of the model is on determination: whether or not the referents of an utterance are definite.

The mating song of the male Bengalese finch can be described by a finite-state grammar and has the feature that more complex songs are preferred by females [1]-[3]. These facts suggest that complex song grammars may have evolved via sexual selection. How, then, do the female birds gauge a song's complexity? Assuming that they can measure the complexity of a song while communicating with a male, but without making a model of the song, we studied the evolution of song grammars. In our simulation, it was demonstrated that song grammars became more complex through communication between coevolving males and females. Furthermore, when singing and listening were subject to fluctuations, peculiar features were observed in communication and evolution.

This paper proposes a layered systemic architecture for audio signal processing. The described systems consists of several building blocks connected in different ways and thus enabling different behaviour. Three different systems are proposed constructed with almost the same building blocks fulfilling three different tasks of audio processing: learning to hear, learning to reproduce and learning to associate. The systemic architecture facilitates the connection of all three proposed subsystems to get one big system fulfilling all three proposed tasks of audio signal processing.

In this paper, a computational model of a successful negotiated com- munication system is presented, in which language agents develop their own meanings in response to their environment and attempt to infer the meanings of others' utterances. The inherent uncertainty in the process of meaning inference in the system leads to variation in the agents' internal semantic representations, which then itself drives language change in the form of semantic generalisation.

Models of the cultural evolution of language typically assume a very simplified population dynamic. In the most common modelling framework (the Iterated Learning Model) populations are modelled as consisting of a series of non-overlapping generations, with each generation consisting of a single agent. However, the literature on language birth and language change suggests that population dynamics play an important role in real-world linguistic evolution. We aim to develop computational models to investigate this interaction between population factors and language evolution. Here we present results of extending a well-known Iterated Learning Model to a population model which involves multiple individuals. This extension reveals problems with the model of grammar induction, but also shows that the fundamental results of Iterated Learning experiments still hold when we consider an extended population model.

Structural hallmarks of language can be explained in terms of adaptation, by language, to pressures arising during its cultural transmission. Here I present a model which explains the compositional structure of language as an adaptation in response to pressures arising from the poverty of the stimulus available to language learners and the biases of language learners themselves.

This paper describes a framework for studies of the adaptive acquisition and evolution of language, with the following components: language learning begins by associating words with cognitively salient representations (``grounding''); the sentences of each language are determined by properties of lexical items, and so only these need to be transmitted by learning; the learnable languages allow multiple agreements, multiple crossing agreements, and reduplication, as mildly context sensitive and human languages do; infinitely many different languages are learnable; many of the learnable languages include infinitely many sentences; in each language, inferential processes can be defined over succinct representations of the derivations themselves; the languages can be extended by innovative responses to communicative demands. Preliminary analytic results and a robotic implementation are described.

The field of language evolution and computation may benefit from using efficient and robust simulation tools that are based on widely exploited principles within the field. The tool presented in this paper is one that could fulfil such needs. The paper presents an overview of the tool -- THSim v3.2 -- and discusses some research questions that can be investigated with it.

This paper presents a first investigation regarding lexicon grounding and evolution under an iterated learning regime without an explicit transfer of reference. In the original iterated learning framework, a population contains adult speakers and learning hearers. In this paper I investigate the effects of allowing both adults and learners to take up the role of speakers and hearers with varying probabilities. The results indicate that when adults and learners can be selected as speakers and hearers, their lexicons become more similar but at the cost of reduced success in communication.

Compositionality is one of the fundamental properties of natural language. Explaining its evolution remains a challenging problem because most existing explanations require a structured language to be already present in the population before compositionality can successfully spread in a population. In this paper, I study whether a communication system can evolve that shows the preservation of topology between meaning-space and signal-space, without assuming that individuals have any prior processing mechanism for compositionality. I present a formalism to describe a communication system where there is noise in signaling and variation in the values of meanings. In contrast to previous models, both the noise and values depend on the topology of the signal- and meaning spaces. I study a model of a population of agents that each try to optimize their communicative success under these circumstances. The results show that the preservation of topology between follows naturally from the assumptions on noise, values and individual-based optimization.