Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to the difficulty domain experts have in specifying them, techniques that learn Bayesian networks from data have become indispensable. Recently, however, there have been many important new developments in this field. This work takes a broad look at the literature on learning Bayesian networks—in particular their structure—from data. Specific topics are not focused on in detail, but it is hoped that all the major fields in the area are covered. This article is not intended to be a tutorial—for this, there are many books on the topic, which will be presented. However, an effort has been made to locate all the relevant publications, so that this paper can be used as a ready reference to find the works on particular sub-topics.

Informal logic (IL) is an area of philosophy rich in models of communication and discourse with a heavy focus on argument and ‘dialogue games’. Computational dialectics is a maturing strand of research that is focused on implementing these dialogue games. The aim of this paper is to review research on applying IL dialogue games into human–computer dialogue design. We argue that IL dialogue games tend to have a number of attractive properties for human computer dialogue and that their computational utilization in this area has been increasing recently. Despite the strength of the case for IL, a number of important barriers need to be overcome if the potential of IL is to be fulfilled. These barriers are examined and means of overcoming them discussed.

We analyze the problem of modeling and developing multiagent systems (MASs) from the organizational theory point of view. In particular, we focus on the critical issue of adapting MASs organizations whenever changes in the structure of the MAS are required. We survey different relevant agent-oriented methodologies and discuss their suitability in dealing with adaptation in multiagent organizations. Finally, we present some critical considerations about the analyzed methodologies together with some open issues related to the process of modeling organizations for facilitating their adaptations.

This article revises the state of the art of the application of agent technology within the scope of surveillance systems. Thus, the potential of the practical use of the concepts and technologies of the agent paradigm can be identified and evaluated in this domain. Current surveillance systems are noted for using several devices, heterogeneous in many instances, distributed along the observed scenario, while incorporating a certain degree of intelligence to alert the operator proactively to what is going on in the observed scenario and prevent the operator from having to observe the monitors continuously. The basic characteristics of the agents (autonomy, reactivity, proactiveness and social ability), along with multiagent systems’ characteristics (distributed data management, low coupling, robustness, communication and coordination between autonomous entities), suggest that the agency is a good choice for solving problems which appear and are dealt with in surveillance systems.

One of the major stumbling blocks for artificial intelligence remains the commonsense knowledge problem. It is not clear how we could go about building a program which has all the commonsense knowledge of the average human adult. This has led to growing interest in the ‘developmental’ approach, which takes its inspiration from nature (especially the human infant) and attempts to build a program which could develop its own knowledge and abilities through interaction with the world. The challenge here is to find a learning program which can continuously build on what it knows, to reach increasingly sophisticated levels of knowledge. This survey reviews work in this area, with the emphasis on those that focus on early learning, for example, sensorimotor learning. The concluding discussion assesses the progress thus far and outlines some key problems which have yet to be addressed, and whose solution is essential to achieve the goals of the developmental approach.