Mair Allen-Williams: Peer-reviewed publications
Overview:Multi-agent systems draw together a number of
significant trends in modern technology: ubiquity, decentralisation,
openness, dynamism and uncertainty. As work in these fields develops,
such systems face increasing challenges. Two particular challenges are
decision making in uncertain and partially-observable environments,
and coordination with other agents in such environments. Although
uncertainty and coordination have been tackled as separate problems,
formal models for an integrated approach are typically restricted to
simple classes of problem and are not scalable to problems with tens
of agents and millions of states.
- PhD thesis: Bayesian learning for multiagent coordination
- Allen-Williams, M., & Jennings, N. R. (2009). Bayesian adaptation for complex dynamic systems. In M. Wang & Z. Sun (Eds.), Handbook of research on complex dynamic process management: Techniques for adaptability in turbulent environments. Hershey, Pennsylvania: IGI Global.
- 2009: Allen-Williams, M., & Jennings, N. R. (2009). Bayesian learning for cooperation in multi-agent systems. In C. L. Mumford & L. C. Jain. (Eds.), Studies in computational intelligence: collaboration, fusion and emergence. London, England: Springer-Verlag.
Also contributed to:
Acute: high-level programming language design for distributed
computation This work is exploring the design space of high-level
languages for distributed computation, focussing on typing, naming,
and version change. We have designed, formally specified and
implemented an experimental language, Acute.
- 2007: Peter Sewell, James J. Leifer, Keith Wansbrough, Francesco Zappa Nardelli, Mair Allen-Williams, Pierre Habouzit, Viktor Vafeiadis (2007). Acute: High-level programming language design for distributed computation. J. Funct. Program. 17(4-5): 547-612
- 2005: Peter Sewell, James J. Leifer, Keith Wansbrough, Francesco Zappa Nardelli, Mair Allen-Williams, Pierre Habouzit, Viktor Vafeiadis (2005): Acute: high-level programming language design for distributed computation. ICFP 2005: 15-26