Abstract
The paper begins with the discussion on why we should be concerned with machine learning in the context of distributed AI. The rest of the paper is dedicated to various problems of multi-agent learning. First, a common framework for comparing different existing systems is presented. It is pointed out that it is useful to distinguish when the individual agents communicate. Some systems communicate during the learning phase, others during the problem solving phase, for example. It is also important to consider how, that is in what language, the communication is established. The paper analyses several systems in this framework. Particular attention is paid to previous work done by the authors in this area. The paper covers use of redundant knowledge, knowledge integration, evaluation of hypothesis by a community of agents and resolution of language differences between agents. © Springer-Verlag Berlin Heidelberg 1991.

Abstract

Abstract
This paper describes a shell for cooperating expert systems that has been developed at the University of Porto. The main goal of this shell is two-fold: to generate a community of cooperative knowledge-based systems and to develop several special reasoning techniques which can be used under a distributed and cooperative paradigm. UPShell is able to convert a set of generated intelligent systems (ISs) into a community of cooperative ISs. In this first version it is already possible to generate different intelligent systems which are able to run 'simultaneously' as separate Unix processes and, using a message-passing mechanism, to communicate among themselves. They can be set to pursue an overall goal in a cooperative way. Moreover, several tasks can be given to each IS to be solved simultaneously, and the IS can switch from task to task according to dynamic priorities reflecting the urgency attached to the specific sub-tasks that emerge. The shell described here may also be used to test, within a distributed environment, some time-bounded reasoning techniques that are presently being developed. The paper has three main parts: a general overview of the UPShell (Section 1); a tutorial explaining, by means of examples, how to use the package (Section 2); and, finally, some considerations on the reasoning techniques used and future improvements (Sections 3-5).

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