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Publications

Publications by LIAAD

1992

IDENTIFICATION OF BILINEAR TIME-SERIES MODELS BL(P,O,P,1)

Authors
RAO, TS; DASILVA, MEA;

Publication
STATISTICA SINICA

Abstract
In this paper, we show how the Yule-Walker type difference equations for higher order moments and cumulants, recently derived for certain types of bilinear time series models, the BL(p,0,p,1) models, by Sesay and Subba Rao (1988, 1991), could be used for tentative identification of the order of the model. The technique we use for identification is canonical correlation analysis, carried out between the linear combination of the observations and linear combination of higher powers of the observations. The methods are illustrated with real and simulated examples.

1991

Learning in multi-agent environments

Authors
Brazdil, P;

Publication
Algorithmic Learning Theory, 2nd International Workshop, ALT '91, Tokyo, Japan, October 23-25, 1991, Proceedings

Abstract

1991

Panel: Learning in Distributed Systems and Multi-Agent Environments

Authors
Brazdil, P; Gams, M; Sian, SS; Torgo, L; de Velde, WV;

Publication
Machine Learning - EWSL-91, European Working Session on Learning, Porto, Portugal, March 6-8, 1991, Proceedings

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.

1991

Learning to Relate Terms in a Multiple Agent Environment

Authors
Brazdil, P; Muggleton, S;

Publication
Machine Learning - EWSL-91, European Working Session on Learning, Porto, Portugal, March 6-8, 1991, Proceedings

Abstract

1991

Shell for cooperating expert systems

Authors
Oliveira, E; Camacho, R;

Publication
Expert Systems

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).

1991

A multi-agent environment in robotics

Authors
Oliveira, EC; Camacho, R; Ramos, C;

Publication
Robotica

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.

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