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In the context of e-commerce, it is critical for a retailing com pany to be able to assess the market and respond quickly to changes in competition and/or service levels and availability of its products. If the company operates globally, and the geographical constraints on the supplier are different from those on the consumer, it is even more crucial to assess each market with respect to its characteristics and dynamically price every individual product accordingly. In this paper we report on an agent-based distributed system that was devel oped to support the retailing operations of one of the largest online book sellers in the UK, which has millions of books on offer and operates internationally in a number of market places. The system supports the collection of market data, processing of this data, and applying dynamically one of set of predefined pricing patterns. We describe the principles on which the price adaptation is based, the (long-running and proven successful) distributed scalable software architecture that supports it, illustrate how the mechanism copes with some typical situations system that arise and highlight some of the lessons learned from the development experience.

We describe an approach to the representation of requirements using answer set programming and how this leads to a vision for the role of artificial intelligence techniques in software engineering with a particular focus on adaptive business systems. We outline how the approach has developed over several years through a combination of commercial software development and artificial intelligence research, resulting in: (i) a metamodel that incorporates the notion of runtime requirements, (ii) a formal language for their representation and its supporting computational model (InstAL), and (iii) a software architecture that enables monitoring of distributed systems. The metamodel is the result of several years experience in the development of business systems for e-tailing, while InstAL and the runtime monitor is ongoing research to support the specification, verification and application of normative frameworks in distributed intelligent systems. Our approach derives from the view that in order to build agile systems, the components need to be structured more like software that controls robots, in that it is designed to be relatively resilient in the face of a nondeterministic, dynamic, complex environment about which there is incomplete information. Thus, degrees of autonomy become a strength and an opportunity, but must somehow be constrained by informing these autonomous components what should be done in a certain situation or what system state ought to be achieved through norms as expressions of requirements. Because such a system made up of autonomous components is potentially behaviourally complex and not just complicated, it becomes essential to monitor both whether norms/requirements are being fulfilled and if not why not. Finally, because control over the system can be expressed through requirements in the form of data that can be changed, a route is opened to adjustment and dynamic redirection of running systems.

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