Date and time:
Venue: 10.08.04
Chair: Xiaodong Li
Abstract:
Agent programming languages frequently utilise a logical framework, where a crucial component is the declarative part: initial state axioms, precondition axioms and successor state axioms. The task is to find a sequence of actions that constitute a legal execution of some high-level nondeterministic program and this involves reasoning about preconditions and effects of actions within the body of the program. This approach has been recognised as one of the most successful solutions to non-deterministic programming to date. However, current agent programming languages typically model execution as sequences of states, and are rapidly becoming unworkable due to the large number inter-dependencies frequently encountered: such as the need to synchronise sub-tasks or resource conflicts. A fundamental difficulty presently faced by programmers of multi-agent systems is the lack of coordination algorithms when computational devices have overlapping or common objectives or when agents rely on one-another to complete joints tasks. The coordination problem is especially challenging where agents have limited knowledge about, and control over, other agents. There is a great need for improved methods for capturing and utilising richer dependency information in distributed computation tasks in ways that guarantee more effective and efficient solutions. Graph theoretic principles show great promise for coordination of distributed tasks, since graphs capture richer dependency information. The current states or epistemic beliefs of agents, together with task dependencies, can be modelled by graphs in the form of sets of labelled vertices connected by attributed edges. Although the use of graphs have been studied in the literature, using dependency graphs within and between groups; in terms of Kripke models or possible world structures, formalising specific languages for this purpose is the subject of ongoing work. Coordination algorithms which utilise graphs promise more powerful coordination techniques; at the same time offering better computation and communication efficiencies. Since graph approaches can suffer complexity problems, particular attention must be paid to matching and unification algorithms utilised. Recent developments in computationally efficient graph matching techniques can lead to more powerful programming techniques for multi-agent systems.
About the speaker:
Dr. Pearce received his B.Sc(Hons) in Computer Science from The University of Melbourne in 1991 and PhD degree from Curtin University in 1997. In 1998 he completed a Postdoctoral Research Fellowship with the Australian Defence Science and Technology Organisation (DSTO), where he worked on multi-agent simulation of air missions. From 1998-1999 Dr. Pearce was a Lecturer at Curtin University. Currently a Senior Lecturer at The University of Melbourne in the Department of Computer Science and Software Engineering. His research interests include multi-agent coordination, graph matching and agent programming languages and techniques.
Seminar Organisation
Seminars are free and open to the general public. No booking is necessary. If you are interested in giving a presentation in this seminar series, or to make suggestions for speakers, please contact Xiaodong Li, the seminar co-ordinator.