Constraint satisfaction is a fundamental artificial intelligence technique offering a simple yet powerful representation. An increasing amount of attention has recently been paid to the development of constraint satisfaction techniques, and it has become clear that the original formulation of a static Constraint Satisfaction Problem (CSP) with hard, imperative constraints is insufficient to model many real problems. Two important extensions to the classical CSP framework which address some of these deficiencies are flexible and dynamic constraint satisfaction. This paper examines in detail classical, flexible and dynamic CSP. It reviews the motivations behind both extensions, and describes the techniques used to solve each type of problem. The paper employs a running example throughout to illustrate the ideas presented.

Model-based simulation systems have been created in various fields of engineering to train personnel or students in operation, maintenance and troubleshooting of complex devices and systems. A review of literature indicates a lack of good overviews of the approaches to system design of model-based training simulations in Intelligent Computer Assisted Instruction (ICAI). Though single systems have to some extent been evaluated with regard to their performance, an organised evaluation, especially a comparative evaluation of the systems that have been created within the field is lacking. To be able to successfully conduct an in-depth review under these conditions, we concentrate and centre our review around the design of the domain simulator component and its implications for the other parts of instructional system. For this purpose we have identified features that are in our opinion suitable to characterise the design approach of a model-based simulation in ICAI for training. We have organised these design features in a framework to enable an effective review. Within this framework, we investigate each feature separately and illustrate it by giving examples from existing applications where appropriate.

The new technologies for Knowledge Discovery from Databases (KDD) and data mining promise to bring new insights into a voluminous growing amount of biological data. KDD technology is complementary to laboratory experimentation and helps speed up biological research. This article contains an introduction to KDD, a review of data mining tools, and their biological applications. We discuss the domain concepts related to biological data and databases, as well as current KDD and data mining developments in biology.

The 1998 Workshop of the UK Special Interest Group on Multi-Agent Systems was held in Manchester in December, chaired and organised by Michael Fisher of Manchester Metropolitan University, continuing the series of focussed and constructive meetings in this field. After two very successful workshops on the Foundations of Multi-Agent Systems at the University of Warwick in 1996 (Luck, 1997; Doran et al., 1997; d'Inverno et al., 1997; Fisher et al., 1997) and 1997 (Luck et al., 1998; Aylett et al., 1998; Binmore et al., 1998), the scope was broadened for 1998 to a wider range of issues concerning all aspects of multi-agent systems.

In systems composed of multiple autonomous agents, negotiation is a key form of interaction that enables groups of agents to arrive at a mutual agreement regarding some belief, goal or plan, for example. Particularly because the agents are autonomous and cannot be assumed to be benevolent, agents must influence others to convince them to act in certain ways, and negotiation is thus critical for managing such inter-agent dependencies. The process of negotiation may be of many different forms, such as auctions, protocols in the style of the contract net, and argumentation, but it is unclear just how sophisticated the agents or the protocols for interaction must be for successful negotiation in different contexts. All these issues were raised in the panel session on negotiation.

The discipline of agent technology is becoming a mature research area, both from a scientific and an engineering perspective. Strong theoretical and empirical analysis, together with practical experience of deploying systems to solve real problems, give this community the opportunity to make an impact in the world of business. At UKMAS'98, a panel of academics and industrialists met to discuss exactly how this could be done; how could agents and multi-agent systems be used to actually make money?