What kind of motivation drives child language development? This article presents a computational model and a robotic experiment to articulate the hypothesis that children discover communication as a result of exploring and playing with their environment. The considered robotic agent is intrinsically motivated towards situations in which it optimally progresses in learning. To experience optimal learning progress, it must avoid situations already familiar but also situations where nothing can be learnt. The robot is placed in an environment in which both communicating and non-communicating objects are present. As a consequence of its intrinsic motivation, the robot explores this environment in an organized manner focusing first on non-communicative activities and then discovering the learning potential of certain types of interactive behaviour. In this experiment, the agent ends up being interested by communication through vocal interactions without having a specific drive for communication.

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this paper, we show that compositional language can arise automatically through grounded communication among populations of communicators. The proposed mechanism is the following: if a holistic and a compositional approach are in competition and if both structured (compositional) and atomic meanings need to be communicated, the holistic strategy becomes less successful as it does not recruit already acquired bits of language. We demonstrate the viability of this explanation through computer simulations in which populations of artificial agents perform a communicative task - describing scenes that they have observed. Successful language strategies (that is, those yielding successful transmission of information about a scene) are reinforced while unsuccessful ones are demoted. The simulations show that this reinforcement on the basis of communicative success indeed leads to the dominance of compositional language as long as the fraction of unstructured meaning to be communicated is sufficiently high. Moreover, following Elman (1993), we then show that the same effect can be achieved by, instead of manipulating the world (the fraction of unstructured meaning presented to the agents), letting the agents themselves go through developmental stages. These simulations confirm that simple reinforcement mechanisms applied during communicative interactions can account for the emergence of linguistic compositionality

Computational models have played a central role in the debate over language learnability. This article discusses how they have been used in different “stances”, from generative views to more recently introduced explanatory frameworks based on embodiment, cognitive development and cultural evolution. By digging into the details of certain specific models, we show how they organize, transform and rephrase defining questions about what makes language learning possible for children. Finally, we present a tentative synthesis to recast the debate using the notion of learning bias.

Semantic maps have offered linguists an appealing and empirically rooted methodology for describing recurrent structural patterns in language development and the multifunctionality of grammatical categories. Although some researchers argue that semantic maps are universal and given, others provide evidence that there are no fixed or universal maps. This paper takes the position that semantic maps are a useful way to visualize the grammatical evolution of a language (particularly the evolution of semantic structuring) but that this grammatical evolution is a consequence of distributed processes whereby language users shape and reshape their language. So it is a challenge to find out what these processes are and whether they indeed generate the kind of semantic maps observed for human languages. This work takes a design stance towards the question of the emergence of linguistic structure and investigates how grammar can be formed in populations of autonomous artificial “agents” that play “language games ” with each other about situations they perceive through a sensori-motor embodiment. The experiments reported here investigate whether semantic maps for case markers could emerge through grammaticalization processes without the need for a universal conceptual space.

Aspect is undoubtedly the most capricious grammatical category of the Russian language. It has often been asserted as a mystery accessible only to native speakers, leaving all the others lost in its apparently infinite clutter. Recent work in cognitive linguistics has tried to bring order to the seeming chaos of the Russian aspectual system. But these approaches have not been operationalized so far. This paper demonstrates how the aspectual derivation of Russian verbs can be handled successfully with Fluid Constructional Grammar, a computational formalism recently developed for the representation and processing of constructions. Keywords: Fluid Construction Grammar; Russian aspect.

Although most previous model-based research has not moved beyond first-order semantics, human languages are clearly capable of expressing second-order semantics: the meanings expressed in a sentence do not only consist of conjunctions of first-order predicates but also predicates that take other predicates as an argument. In this paper we report on multi-agent language game experiments in which agents handle second-order semantics. We focus our discussion on how this type of research is able to provide fundamental insights in how properties of humanlanguage-like properties could once have emerged. For recursion, this might have happened as a side-effect of agents trying to reuse previously learned language structure as much as possible. 1.

Abstract. Artificial agents trying to achieve communicative goals in situated interactions in the real-world need powerful computational systems for conceptualizing their environment. In order to provide embodied artificial systems with rich semantics reminiscent of human language complexity, agents need ways of both conceptualizing complex compositional semantic structure and actively reconstructing semantic structure, due to uncertainty and ambiguity in transmission. Furthermore, the systems must be open-ended and adaptive and allow agents to adjust their semantic inventories in order to reach their goals. This paper presents recent progress in modeling open-ended, grounded semantics through a unified software system that addresses these problems. 1

In this paper, we propose a concrete operationalization which incorporates data from the FrameNet database into Fluid Construction Grammar, currently the only computational implementation of construction grammar that can achieve both production and parsing using the same set of constructions. As a proof of concept, we selected an annotated sentence from the FrameNet database and transcribed its frame annotation analysis into an FCG grammar. The paper illustrates the proposed constructions and discusses the value and results of these formalization efforts.

BLUE PAPER: A RESEARCH ROADMAP FOR DEVELOPING ARTIFICIAL EMBODIED AND COMMUNICATING AGENTS............................................................. 1 1. INTRODUCTION.................................................................................................................... 3 2. Communication and language as complex adaptive systems......................................... 4 2.1 Individual Behaviour.................................................................................................... 4 2.2 Collective Behaviour.................................................................................................... 5 2.3 Language and communication..................................................................................... 5