This Ph.D. Seminar course will investigate the intersection of self-organization and computer-based information systems. It focuses on the emergence and evolution of communication and language as the prototype for self-organizing information systems. We'll examine numerous computational models of language evolution, using them as a basis for thinking about information and self-organization in distributed systems of many kinds. This is a rewarding and fun process, because:
Language is the principal human information system, and a foundation of every computational information system.
Language is an inherently collective, distributed, and social phenomenon - there is no single-agent language.
All languages evolve: language is a complex adaptive system.
Language evolution provides a compelling arena for grappling with thorny issues of grounded meaning and distributed semantics, which impact all distributed information systems.
Language evolution studies are currently very hot research topics. Researchers are working to discover general underlying mathematical, computational, and implementation principles that explain where human language came from, how it changes, and how artificial agents (robots, software agents, dynamic databases, web services, ontology managers) might develop, reconcile, and sustain their own sophisticated representation and communication regimes from the ground up.
Computational language evolution models provide useful accounts of many other critical adaptive information problems, including adaptive information organizations (subject indices; ontologies; resource description/discovery regimes like "ad-words" and web service brokering; P2P information retrieval [IR] systems); schema reconciliation for databases; adaptive genomics; and biochemical signaling networks.
The format of the course will be open ongoing discussion of papers, coupled through the semester with joint project work and writing in areas of personal interest.
Virtually all papers treated in the course will include mathematical and/or computational models. Prospective students should be comfortable with exploring and learning about new mathematical theories, models, and expressions. They should also have a general understanding of computer programming, principles of computation, and probability.
Almost all the papers we will read are contained in the UIUC Language Evolution and Computation web repository, so explore it if you'd like to get a glimpse of the course content and style.
The course is a continuation of an ongoing seminar, and may be repeated for credit since the content differs from semester to semester.