Abstract
The use of Multi-Agent Systems as a Distributed AI paradigm for Robotics is the principal aim of our present work. In this paper we consider the needed concepts and a suitable architecture for a set of Agents in order to make it possible for them to cooperate in solving non-trivial tasks. Agents are sets of different software modules, each one implementing a function required for cooperation. A Monitor, an Acquaintance and Self-knowledge Modules, an Agenda and an Input queue, on the top of each Intelligent System, are fundamental modules that guarantee the process of cooperation, while the overall aim is devoted to the community of cooperative Agents. These Agents, which our testbed concerns, include Vision, Planner, World Model and the Robot itself.

Abstract
Distributed problem solving nodes (so-called experts in this paper) may face severe time constraints in some real applications. In such circumstances, experts should not only cooperate with each other to solve large and complicated problems but also meet real-time constraints in order to make the solutions effective. Here we present a blackboard-based monitor which takes full responsibility to control experts distributed in the cooperative problem solving system. We claim this monitor can support different styles of cooperation between experts and sophisticated task scheduling behaviours local to each expert which are crucial for real-time applications. Moreover, this paper highlights the ability of the monitor to dynamically choose cooperation policies and adjust local task scheduling behaviours according to the rapidly changing environment under real-time constraints.

Abstract

Abstract
Inductive Logic Programming (ILP) systems have been largely applied to classification problems with a considerable success. The use of ILP systems in problems requiring numerical reasoning capabilities has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the range of domains where the ILP paradigm may be applied. This paper proposes improvements in numerical reasoning capabilities of ILP systems. It proposes the use of statistical-based techniques like Model Validation and Model Selection to improve noise handling and it introduces a new search stopping criterium based on the PAG method to evaluate learning performance. We have found these extensions essential to improve on results mer statistical-based algorithms for time series forecasting used in the empirical evaluation study.

Abstract
Organ transplantation is a highly complex decision process that requires expert decisions. The major problem in a transplantation procedure is the possibility of the receiver's immune system attack and destroy the transplanted tissue. It is therefore of capital importance to find a donor with the highest possible compatibility with the receiver, and thus reduce rejection. Finding a good donor is not a straight-forward task because a complex network of relations exists between the immunological and the clinical variables that influence the receiver's acceptance of the transplanted organ. Currently the process of analysis of these variables involves a careful study by the clinical transplant team. The number and complexity of causal dependencies among variables make the manual process very slow. In this paper we assess the usefulness of Machine Learning algorithms as a tool to improve and speed up the decisions of a transplant team. We achieve that objective by analysing past real cases and constructing models as set of rules. Such models are accurate and understandable by experts. Copyright 2010 ACM.

Abstract
ILP systems have been largely applied to datamining classification tasks with a considerable success. The use of ILP systems in regression tasks has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the application of ILP to discovery of functional relationships of numeric nature. This paper proposes improvements in numerical reasoning capabilities of ILP systems for dealing with regression tasks. It proposes the use of statistical-based techniques like Model Validation and Model Selection to improve noise handling and it introduces a new search stopping criterium based on the PAC method to evaluate learning performance. We have found these extensions essential to improve on results over machine learning and statistical-based algorithms used in the empirical evaluation study.