Details of Grant 

EPSRC Reference:	GR/S78674/01
Title:	AIBACS - Social Insects, Simulated Evolution and Biologically Inspired Algorithms
Principal Investigator:	Kovacs, Dr T
Other Investigators:	Franks, Professor N Marshall, Professor JAR
Researcher Co-Investigators:	Dr A Dornhaus Professor JAR Marshall
Project Partners:
Department:	Computer Science
Organisation:	University of Bristol
Scheme:	Standard Research (Pre-FEC)
Starts:	05 January 2004	Ends:	04 July 2006	Value (£):	127,271
EPSRC Research Topic Classifications:	Artificial Intelligence New & Emerging Comp. Paradigms Population Ecology
EPSRC Industrial Sector Classifications:	No relevance to Underpinning Sectors
Related Grants:
Panel History:
Summary on Grant Application Form
Social insects employ ingenious and sophisticated algorithms to assess a variety of attributes of potential new nest sites, and to achieve a collective decision and coordinated emigration. Using methods from artificial life, we will study these house-hunting behaviours and their evolution in ants and honeybees. This research lies at the interface between computer science and biology, and provides exciting benefits to both disciplines. For biology, simulating the evolution of behaviours and their alternatives enables us to assess the adaptive benefit of one behaviour over another and thus to understand why a particular behaviour has evolved. This is not possible by simply analysing extant behaviours; simulation studies are crucial to allow comparisons with the behaviours as they could have been or indeed were in the ancestors of existing species.For computer science, social insects are a source of fascinating and unique insights for distributed decision-making systems in general. Social insects can inspire new solutions to many current problems in computer science for which robust solutions are difficult or impossible to find using traditional approaches, for example search problems in machine learning, control problems in communication networks and scheduling problems in distributed computing. We will develop a novel biologically inspired algorithm based on nest assessment and emigration behaviours, the characteristics of which will be decentralised decision making with an adjustable speed/accuracy trade-off. The design of this algorithm will be improved by the deeper understanding of these behaviours gained through the evolutionary behavioural modelling component of the research.
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Organisation Website:	http://www.bris.ac.uk