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