Autonomous agents are systems capable of autonomous decision-making in real-time environments. Computation is a valuable resource for such decision-making, and yet the amount of computation that an autonomous agent may carry out will be limited. It follows that an agent must be equipped with a mechanism that enables it to make the best possible use of the computational resources at its disposal. In this paper we review three approaches to the control of computation in resource-bounded agents. In addition to a detailed description of each framework, this paper compares and contrasts the approaches, and lists the advantages and disadvantages of each.

This paper focuses upon the contribution which adequate use of the latest development in IT can make to the enhancement, development and improvement of professional expertise in the construction domain. The paper is based on the author's personal expertise and involvement in several UK- and European-funded research projects. First, the paper gives an overview of the benefits that can be gained from improved information and knowledge management in the construction industry. The latest developments in information and communication technologies are then described and presented as an enabler for effective knowledge management. Finally, the paper reviews the most promising techniques for information and knowledge management in the construction domain, highlighting the advantages and disadvantages of each of them, as well as future directions for knowledge management in construction.

Upon assessing probabilities for Bayesian belief networks, the knowledge and practical experience of experts is often the only available source of probabilistic information. It is important to realise that issues concerning human capabilities with respect to making judgements come into play when relying on experts for probability elicitation. A number of methods for the elicitation of probabilities are known from the field of decision analysis. These methods try, to some extent, to deal with those issues. I present here an overview of the issues to consider when relying on expert judgements and describe the methods that are available for expert elicitation, along with their benefits and drawbacks.

There is little doubt that the strength and breadth of UK research into multi-agent systems continues to grow as we move into the new millennium. In the middle of an extremely cold December in 2000, the Third UK Workshop on Multi-Agent Systems (UKMAS 2001) was held at St Catherine's College, Oxford. This was the fifth such meeting in as many years, generously sponsored by EPSRC, FIPA (The Foundation for Intelligent Physical Agents) and Hewlett Packard.

In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.