The toxicity prediction problem lies squarely at the interface of biology, chemistry and computational domains and will require the integrated application of knowledge and approaches from each domain for its solution. It is not a single modeling problem, but rather multiple levels of modeling compartments derived from the goals of toxicity risk assessment. These compartments include different categories and characteristics of toxicity (e.g., cancer vs. non-cancer, acute vs. delayed) and, therein, different levels of biofunctional organization and multiple mechanisms of toxicity extending to the level of individual chemical structures. A toxicity prediction model should strive to resolve the global toxicity prediction problem to local modeling compartments that reflect coherent biofunctional mechanisms and common modes of action while retaining some level of useful generalizations. This paper attempts to use these general concepts to frame the challenges and opportunities for application of Artificial Intelligence (AI) and computer-based methods to the goal of chemical toxicity prediction.

Predicting or learning numeric features is called regression in the statistical literature, and it is the subject of research in both machine learning and statistics. This paper reviews the important techniques and algorithms for regression developed by both communities. Regression is important for many applications, since lots of real life problems can be modeled as regression problems. The review includes Locally Weighted Regression (LWR), rule-based regression, Projection Pursuit Regression (PPR), instance-based regression, Multivariate Adaptive Regression Splines (MARS) and recursive partitioning regression methods that induce regression trees (CART, RETIS and M5).

Telecommunications infrastructures are a natural application domain for the distributed software agent paradigm. The authors clarify the potential application of software agent technology in legacy and future communications systems, and provide an overview of publicly available research on software agents used for communications management. The authors focus on the intelligent agent type of software agent, although the paper also reviews the reasons why mobile agents have made an impact in this domain. The author's objective is to describe some of the intricacies of using the software agent approach for the management of communications systems. The paper is in four main sections. The first section provides a brief introduction to software agent technology. The second section considers general problems of network management and the reasons why software agents may provide a suitable solution. The third section reviews some selected research on agents in a telecommunications management framework. The final section concludes the paper by discussing some of the problems encountered and some future directions for further research.

For a number of years, researchers in AI and robotics have been sharing organisation principles for software development called “architectures” (Arkin, 1998; Hexmoor et al., 1997; Kortenkamp et al., 1998). Most recently, there has been an interest in extending the software architectures originally designed for robotic applications to accommodate autonomous operation beyond robotics (Pell et al., 1998). These architectures attempt to state principles for organising software for intelligent systems that exhibit cognitive functionalities such as natural language understanding, planning and learning, as well as organism-like functionalities to cope in the world such as reflexive and reactive behaviours.