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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)., The research reported here was carried out at the Sony Computer Science Laboratory in Paris and partially funded by the FP7 EU Project ALEAR.

This paper studies how quantificational expressions such as few, three and all can be grounded in real-world perception. Based on findings from psycholinguistics, we propose a computational model designed for use in robot-robot interaction scenarios which involve discrimination tasks for objects in the real world. We test the performance of our model and contrast it with a type theory based model. We show that our design choices make our model more suitable for real-world applications.

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.

How can we explain the enormous amount of creativity and flexibility in spatial language use? In this paper we detail computational experiments that try to capture the essence of this puzzle. We hypothesize that flexible semantics which allow agents to conceptualize reality in many different ways are key to this issue. We will introduce our particular semantic modeling approach as well as the coupling of conceptual structures to the language system. We will justify the approach and show how these systems play together in the evolution of spatial language using humanoid robots.

Trabajo presentado a la 21st International Joint Conference on Artifical Intelligence (IJCAI-09), celebrada en Pasadena, California (US) del 11 al 17 de julio de 2009., Open-ended language communication remains an enormous challenge for autonomous robots. This paper argues that the notion of a language strategy is the appropriate vehicle for addressing this challenge. A language strategy packages all the procedures that are necessary for playing a language game. We present a specific example of a language strategy for playing an Action Game in which one robot asks another robot to take on a body posture (such as stand or sit), and show how it effectively allows a population of agents to self-organise a perceptually grounded ontology and a lexicon from scratch, without any human intervention. Next, we show how a new language strategy can arise by exaptation from an existing one, concretely, how the body posture strategy can be exapted to a strategy for playing language games about the spatial position of objects (as in "the bottle stands on the table")., The research described here was sponsored by the Sony Computer Science Laboratory in Paris with additional support from the EU Cognition project ALEAR of the European Commission (IST-FET).

With the advent of the LHC, we will be able to probe New Physics (NP) up to energy scales almost one order of magnitude larger than it has been possible with present accelerator facilities. While direct detection of new particles will be the main avenue to establish the presence of NP at the LHC, indirect searches will provide precious complementary information, since most probably it will not be possible to measure the full spectrum of new particles and their couplings through direct production. In particular, precision measurements and computations in the realm of flavour physics are expected to play a key role in constraining the unknown parameters of the Lagrangian of any NP model emerging from direct searches at the LHC. The aim of Working Group 2 was twofold: on one hand, to provide a coherent, up-to-date picture of the status of flavour physics before the start of the LHC; on the other hand, to initiate activities on the path towards integrating information on NP from high-pT and flavour data. With the advent of the LHC, we will be able to probe New Physics (NP) up to energy scales almost one order of magnitude larger than it has been possible with present accelerator facilities. While direct detection of new particles will be the main avenue to establish the presence of NP at the LHC, indirect searches will provide precious complementary information, since most probably it will not be possible to measure the full spectrum of new particles and their couplings through direct production. In particular, precision measurements and computations in the realm of flavour physics are expected to play a key role in constraining the unknown parameters of the Lagrangian of any NP model emerging from direct searches at the LHC. The aim of Working Group 2 was twofold: on one hand, to provide a coherent, up-to-date picture of the status of flavour physics before the start of the LHC/ on the other hand, to initiate activities on the path towards integrating information on NP from high-pT and flavour data.

Proceeding of the 11th International Conference on the Simulation and Synthesis of Living Systems held in Winchester (UK) del 5 al 8 de agosto de 2008., One of the central themes in autonomous robot research concerns the question how visual images of body movements by others can be interpreted and related to one's own body movements and to language describing these body movements. The discovery of mirror neurons has shown that there are brain circuits which become active both in the perception and the re-enactment of bodily gestures, although it is so far unclear how these circuits can form, i.e. how neurons become mirror neurons. We report here further progress with our robot experiments in which a group of autonomous robots play language games in order to coordinate their visual, motor and cognitive body image. We have shown that the right kind of semiotic dynamics can lead to the self-organisation of a successful communication system with which robots can ask each other to perform certain actions. The main contribution of this paper is to show that if the robot has the capacity to 'imagine' the behavior of his own body through selfsimulation, he is better able to guess what action corresponds to a visual image produced by another robot and thus guess the meaning of an unknown word. This leads to a significant speed-up in the way individual agents are able to coordinate visual categories, motor behaviors and language., This research has been carried out at the Sony Computer Science Laboratory in Paris with partial support from the ECAgents project, funded by the EU Future and Emerging Technologies program (IST-FET) as IST-1940, and the ALEAR project, funded by the EU Cognitive Systems program.

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