Abstract
Decentralised co-operative multi-agent systems are computational systems where conflicts are frequent due to the nature of the represented knowledge. Negotiation methodologies, in this case argumentation based negotiation methodologies, were developed and applied to solve unforeseeable and, therefore, unavoidable conflicts. The supporting computational model is a distributed belief revision system where argumentation plays the decisive role of revision. The distributed belief revision system detects, isolates and solves, whenever possible, the identified conflicts. The detection and isolation of the conflicts is automatically performed by the distributed consistency mechanism and the resolution of the conflict, or belief revision, is achieved via argumentation. We propose and describe two argumentation protocols intended to solve different types of identified information conflicts: context dependent and context independent conflicts. While the protocol for context dependent conflicts generates new consensual alternatives, the latter chooses to adopt the soundest, strongest argument presented. The paper shows the suitability of using argumentation as a distributed decentralised belief revision protocol to solve unavoidable conflicts. © Springer-Verlag Berlin Heidelberg 2001.

Abstract
The concepts of Instantaneous Frequency (IFt) and Instantaneous Bandwidth (IBt) have traditionally been considered as independent spectral features. Recent work has suggested, however, that the spectral dynamics of the signal (and, hence, its IFt) may have an impact on IBt and on the achievable spectral resolution. In this article, we consider model based AR spectral estimators. Simulations are done with several distinct time-frequency tools, to confirm the theoretical predictions.

Abstract
This paper describes the 5dpo team. The paper will be divided into three main sections, corresponding to three main blocks: the Global Level, the Local Level and the Interface Level. These Levels, their subsystems and some implementation details will be described next.

Abstract
This paper describes the 5dpo-2000 team, The paper will be divided into three main sections, corresponding to three main blocks: the Global Level, the Local Level and the Interface Level. These Levels, their subsystems and some implementation details will be described next.

Abstract
The ability to solve conflicting beliefs is crucial for multiagent systems where the information is dynamic, incomplete and distributed over a group of autonomous agents. The proposed distributed belief revision approach consists of a distributed truth maintenance system and a set of autonomous belief revision methodologies. The agents have partial views and, frequently, hold disparate beliefs which are automatically detected by system's reason maintenance mechanism. The nature of these conflicts is dynamic and requires adequate methodologies for conflict resolution. The two types of conflicting beliefs addressed in this paper are Context Dependent and Context Independent Conflicts which result, in the first case, from the assignment, by different agents, of opposite belief statuses to the same belief, and, in the latter case, from holding contradictory distinct beliefs. The belief revision methodology for solving Context Independent Conflicts is, basically, a selection process based on the assessment of the credibility of the opposing belief statuses. The belief revision methodology for solving Context Dependent Conflicts is, essentially, a search process for a consensual alternative based on a "next best" relaxation strategy.

Abstract
The complexity of real-life transients, coupled with the incomplete (or absent) knowledge of their statistical structure or defining features has motivated the interest on the use of blind, data driven detection schemes. One such scheme, proposed by Jones and Sayeed, uses Time-Frequency distributions to implement sub-optimal quadratic detectors which, under certain conditions, approach the performance of optimal quadratic detectors. However, their use of Fisher's discriminants to obtain class separation has some drawbacks, which we solve by using a simple perceptron to obtain the discriminant. Also, more often than not, we will have a multiclass situation, implying the use of different Time-Frequency Distributions, each one of them tuned for a given class of transients. The different nature of these distributions (bias, type of cross-terms, time-frequency resolution, etc.) will hamper the performance of the algorithm, forcing the need for experimental validation of its heuristical aspects. These are the issues we will address. The algorithm will be applied to real data, and its performance investigated.