  Distributed search problems are ubiquitous in Artificial Life (ALife). Many
distributed search problems require identifying a rare and previously unseen
event and producing a rapid response. This challenge amounts to finding and
removing an unknown needle in a very large haystack. Traditional computational
search models are unlikely to find, nonetheless, appropriately respond to,
novel events, particularly given data distributed across multiple platforms in
a variety of formats and sources with variable and unknown reliability.
Biological systems have evolved solutions to distributed search and response
under uncertainty. Immune systems and ant colonies efficiently scale up
massively parallel search with automated response in highly dynamic
environments, and both do so using distributed coordination without centralized
control. These properties are relevant to ALife, where distributed, autonomous,
robust and adaptive control is needed to design robot swarms, mobile computing
networks, computer security systems and other distributed intelligent systems.
They are also relevant for searching, tracking the spread of ideas, and
understanding the impact of innovations in online social networks. We review
design principles for Scalable Robust, Adaptive, Decentralized search with
Automated Response (Scalable RADAR) in biology. We discuss how biological RADAR
scales up efficiently, and then discuss in detail how modular search in the
immune system can be mimicked or built upon in ALife. Such search mechanisms
are particularly useful when components have limited capacity to communicate
and social or physical distance makes long distance communication more costly.
