Induction of decision trees

The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.  

The Gaia Methodology for Agent-Oriented Analysis and Design

This article presents Gaia: a methodology for agent-oriented analysis and design. The Gaia methodology is both general, in that it is applicable to a wide range of multi-agent systems, and comprehensive, in that it deals with both the macro-level (societal) and the micro-level (agent) aspects of systems. Gaia is founded on the view of a multi-agent system as a computational organisation consisting of various interacting roles. We illustrate Gaia through a case study (an agent-based business process management system).  

Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. In this paper we present two algorithms which extend the k-means algorithm to categorical domains and domains with mixed numeric and categorical values. The k-modes algorithm uses a simple matching dissimilarity measure to deal with categorical objects, replaces the means of clusters with modes, and uses a frequency-based method to update modes in the clustering process to minimise the clustering cost function. With these extensions the k-modes algorithm enables the clustering of categorical data in a fashion similar to k-means. The k-prototypes algorithm, through the definition of a combined dissimilarity measure, further integrates the k-means and k-modes algorithms to allow for clustering objects described by mixed numeric and categorical attributes. We use the well known soybean disease and credit approval data sets to demonstrate the clustering performance of the two algorithms. Our experiments on two real world data sets with half a million objects each show that the two algorithms are efficient when clustering large data sets, which is critical to data mining applications.  

Workflow Patterns

Differences in features supported by the various contemporary commercial workflow management systems point to different insights of suitability and different levels of expressive power. The challenge, which we undertake in this paper, is to systematically address workflow requirements, from basic to complex. Many of the more complex requirements identified, recur quite frequently in the analysis phases of workflow projects, however their implementation is uncertain in current products. Requirements for workflow languages are indicated through workflow patterns. In this context, patterns address business requirements in an imperative workflow style expression, but are removed from specific workflow languages. The paper describes a number of workflow patterns addressing what we believe identify comprehensive workflow functionality. These patterns provide the basis for an in-depth comparison of a number of commercially availablework flow management systems. As such, this paper can be seen as the academic response to evaluations made by prestigious consulting companies. Typically, these evaluations hardly consider the workflow modeling language and routing capabilities, and focus more on the purely technical and commercial aspects.  

Learning Logical Definitions from Relations

This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attribute-value learning systems, but extends them to a first-order formalism. This new system has been applied successfully to several tasks taken from the machine learning literature.  

Peer-to-Peer环境下多粒度Trust模型构造

信任是多方面的,在不同的应用场景中,同一节点在不同领域具有不同的可信度.现有信任模型粒度过于粗糙,不能很好地解决同一Peer节点在不同领域、不同方面的可信度计算问题.据此,提出一种新的Peer-to-Peef环境下的多粒度信任模型,并给出该模型的数值分析和分布式实现方法.分析及仿真结果表明,该模型与已有模型相比,在可信度计算的粒度、模型的安全性等方面有较大的提高.  

Non-singular terminal sliding mode control of rigid manipulators

This paper presents a global non-singular terminal sliding mode controller for rigid manipulators. A new terminal sliding mode manifold is first proposed for the second-order system to enable the elimination of the singularity problem associated with conventional terminal sliding mode control. The time taken to reach the equilibrium point from any initial state is guaranteed to be finite time. The proposed terminal sliding mode controller is then applied to the control of n-link rigid manipulators. Simulation results are presented to validate the analysis.  

Feature detection from local energy

A more general definition of features such as edges, shadows and bars is developed, based on an analysis of the phase of the harmonic components. These features always occur at points of maximum phase congruency, the type of feature depending on the value of the phase. Using the image itself and its Hilbert transform, a local energy function is defined and it is shown that the local maxima of this energy function occur at points of maximum phase congruency.