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Within this chapter we explore the delivery of ambient computing through
the examination of three case studies, namely Gulliver's Genie, Easishop and
Autonomic Wireless Sensor Networks (AWSN). We use these to illustrate how
our agile intentional agents contribute to overcoming the key issues identified
above. These applications are chosen as they represent three challenging though
complimentary points within the ambient intelligence landscape. Gulliver's Ge-
nie necessitates the identification of individual tourist profiles. Easishop demands
the examination and formulation of buyer and seller behavior and ultimately the
understanding of the particular m-commerce macro economy. The AWSN sce-
nario is more far reaching involving large scale sensor topologies of collaboration,
self regulation and self management. Within this chapter we will consider each
in turn and reflect on the deployment of agile agents.
2 Agent Factory
Agent Factory (AF) [3, 4, 27, 28] is a cohesive framework, (illustrated in Figs.
1 and 3), for the development and deployment of agent-oriented applications
that has been developed by the authors. Central to this framework is the Agent
Factory Agent Programming Language (AF-APL), an Agent-Oriented Program-
ming (AOP) language that supports the fabrication of agents that are: au-
tonomous, situated, socially able, intentional, rational, and mobile [3, 27]. How-
ever, Agent Factory differs from other AOP offerings in that AF-APL has been
embedded within a distributed FIPA-compliant [9] Run-Time Environment, and
supports the development and deployment of agents through an integrated de-
velopment environment, and an associated software engineering methodology
(see Fig. 1). Details of these layers are presented in the following sections.
A key concern in the design of AF has been to ensure that AF-APL agents
can be deployed on Personal Digital Assistants (PDAs) and ultimately on more
Fig. 1. The Agent Factory Framework (left) and its associated Development Method-
ology (right).
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