LAFLang Programme

LAFLang

International Workshop on Learning, Agents and Formal Languages

 Monday, August 22, 2011
PROGRAMME

 8:00-9:00 Registration

 9:00-10:45: Sessions I

 9:00-9:30  OPENING

 9:30-10:00 Leonor Becerra-Bonache and Dana Angluin: An Overview of How Semantics and Corrections Can Help Language Learning

10:00-10:30 Gemma Bel-Enguix and M. Dolores Jiménez-López: Agent-Systems and Formal Languages

10:45-11:15: Coffee break

11:15-13:00: Sessions  II

11:15-11:45 Tom Armstrong and Tim Oates: An Architecture for Bootstrapping Lexical Semantics and Grammatical Structure

11:45-12:15 Sebastien Chipeaux, Fabrice Bouquet, Christophe Lang, and Nicolas Marilleau: Modeling of Complex System with AML as Realized in MIRO Project

12:15-12:45 Mohamed Gaha, Michel Gagnon, and Frédéric Sirois: Answer Set Programming and Blackboard System

13:00-14:00 Lunch

14:30-15:45: Sessions III

14:30-15:00 Taichi Nakamura, Erika Taguchi, Daisuke Hirose, Masahiro Ishikawa, and Akio Takashima : Role-Play Training for Project Management Education Supported by a Mentor Agent

15:00-15:30 Roussanka Loukanova: Constraint Based Syntax of Modifiers

15:45-16:15: Coffee break

16:15-18:00: Sessions IV

16:15-16:45 Toshiyuki Morioka, Hiroaki Ueda, and Kenichi Takahashi:  Efficient Evolutionary Learning of Agent Behavior by Genetic Programming Using the Conditional Probabilities

16:45-17:15  Carmen Navarrete, Marina de la Cruz, Eloy Anguiano, Alfonso Ortega, and José Miguel Rojas Rojas: Parallel simulation of NEPs on clusters

17:15-17:45 Adrian-Horia Dediu: Learning from Extended Answers

Workshop description

The workshop Learning, Agents and Formal Languages LAFLang will be held in Lyon, in the WI-IAT Conference (22-27 August 2011)

For more information about the WI-IAT Conference visit the webpage http://wi-iat-2011.org/.

Our workshop focuses on the common space delimited by three main areas: machine
learning, agent technologies and formal language theory. The main goal of the
workshop is to promote interdisciplinarity among people working in such disciplines,
boosting the interchange of knowledge and viewpoints between specialists. This
interdisciplinary research can provide new models that may improve AI technologies.
Understanding human learning well enough to reproduce aspects of that learning
behaviour in a computer system is a worthy scientific goal. One of the less understood
learning capacities of humans is their ability to acquire a natural language. In order to
better understand natural language acquisition, research in formal models of language
learning, within the field of machine learning, has received significant attention. The
theory of formal language theory is central to the field of machine learning, since the
specific subfield of grammatical inference deals with the process of learning grammars
and languages from data.

The theory of formal languages was mainly originated from mathematics and
generative linguistics as a tool for modelling and investigating syntax of natural
languages, and then it played an important role in the field of computer science. While
the first generation of formal languages was based on rewriting, a further development
in this area has been the idea of several devices collaborating for achieving a common
goal. Formal language theory has taken advantage of the idea of formalizing agent
architectures where a hard task is distributed among several task-specific agents. In
fact, non-standard formal language models have been proposed as grammatical
models of agent systems.

So, the areas of machine learning, agent technologies and formal languages are
clearly related. Therefore we are interested in contributions on any interaction between
those three research areas.

Topics include (but are not limited to):

• Agent systems modelling
• Computational models of language learning
• Theoretical aspects of Grammatical Inference
• Formal models of bio-inspired agent systems
• Theoretical descriptions of languages based on agent systems
• Learning agents: Machine learning and Agent systems
• Applications of machine learning and agent technologies to natural language processing, human-computer interaction and language evolution.
• Intelligent human-computer interaction