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This paper examines the use of bootstrap aggregating (bagging) with classifier learning methods based upon hold-out pruning (or growing) for misclassification cost reduction. Both decision tree and rule set classifiers are used. The paper introduces a “repechange” variation of bagging, that uses, as the hold-out data for cost reduction, the “out of bag” items, which would be unused in standard bagging...
Boosting and Bagging, as two representative approaches to learning classifier committees, have demonstrated great success, especially for decision tree learning. They repeatedly build different classifiers using a base learning algorithm, by changing the distribution of the training set. Sasc, as a different type of committee learning method, can also significantly reduce the error rate of decision...
This paper describes a multi-layer incremental induction algorithm, MLII, which is linked to an existing nonincremental induction algorithm to learn incrementally from noisy data. MLII makes use of three operations: data partitioning, generalization and reduction. Generalization can either learn a set of rules from a (sub)set of examples, or refine a previous set of rules. The latter is achieved through...
A new approach to software design based on an agent-oriented architecture is presented. Unlike current research, we consider software to be designed and implemented with this methodology in mind. In this approach agents are considered adaptively communicating concurrent modules which are divided into a white box module responsible for the communications and learning, and a black box which is the independent...
We present an architecture for multi-agent negotiation for implementing a distributed meeting scheduler. In the scheduling system, an agent is assigned to an user who plans private schedules and events. An agent negotiates with other agents about making an public schedule by referring user’s private schedules and preferences. The multi-agent negotiation we proposed here facilitates reaching an agreement...
This paper presents an AI architecture for multiple robots working collaboratively in a future smart office. This architecture integrates the control, communication, planning, and learning necessary to agentify office robots. Such integration is based on our multiagent robot language (MRL), which is an extension of concurrent logic programming languages (CCL). While the behavior of an agent is specified...
This paper analyzes the roles of problem solving and learning in Organizational-learning oriented Classifier System (OCS) from the viewpoint of organizational learning in organization and management sciences, and validates the effectiveness of the roles through the experiments of large scale problem for Printed Circuit Boards (PCBs) re-design in the Computer Aided Design (CAD). OCS is a novel multiagent-based...
Incremental refinement methods of knowledge bases ease maintenance but fail to uncover the underlying domain model used by the expert. In this paper, we propose a new knowledge representation formalism for incremental acquisition and refinement of knowledge. It guides the expert in expressing his model of the domain during the actual knowledge acquisition process. This knowledge representation scheme,...
A formal way of modeling complex objects such as enterprises is discussed. Information system is indispensable in every enterprise to accomplish various activities required there and, therefore, must be defined as a part of an enterprise model. Programs of information systems are specified based on this model representation. It enables automation of the following programming process. A new modeling...
Requirements engineering is often characterised as the management of conflicts between the viewpoints of different stakeholders. This approach is only useful if there is some benefit in moving a specification from one viewpoint to another. In this study, the value of different viewpoints was assessed using a range of different models (ranging from correct to very incorrect), different fanouts, different...
Various forms of non-monotonic reasoning thrive on minimal change in some form or other. In general, the principle of minimal change prescribes choosing the best from a given set of alternatives. A dual of this principle, which has not drawn much attention from the researchers in the field, is to reject the worst from a given set of alternatives instead. This paper explores the use of this principle...
We address the problem of representing preferences among sets of properties (outcomes, desiderata, etc.) in default logic. In this approach, an ordered default theory consists of default rules, world knowledge, and preferences on sets of default rules. An ordered theory is transformed into a second, standard default theory wherein the preferences are respected, in that defaults are applied in the...
This paper describes a system for problem-solving in complex, dynamic environments while adhering to the principle of frugality. Given a formulation of a problem as a set of hypotheses, any such system must be able to actively search for information to confirm or refute the hypotheses. However, rather than incorporate new information under a philosophy of minimal change, we argue the system should...
Commerical databases often contain critical business information concerning past performance which could be used to predict the future. However, the huge amounts of data can make the extraction of this business information almost impossible by manual methods or standard software techniques. Data mining techniques can analyze, understand and visualize the huge amounts ofstored data gathered from business...
Predictive modelling, in a knowledge discovery context, is regarded as the problem of deriving predictive knowledge from historical/temporal data. Here we argue that neural networks, an established computational technology, can efficaciously be used to perform predictive modelling, i.e. to explore the intrinsic dynamics of temporal data. Infectious-disease epidemic risk management is a candidate area...
Existing research in machine learning and data mining has been focused on finding rules or regularities among the data cases. Recently, it was shown that those associations that are missing in data may also be interesting. These missing associations are the holes or empty regions. The existing algorithm for discovering holes has a number of shortcomings. It requires each hole to contain no data point,...
This paper focuses on how to construct domain ontologies, in particular, a hierarchically structured set of domain concepts without concept definitions, reusing a machine readable dictionary (MRD) and making it adjusted to specific domains. In doing so, we must deal with concept drift, which means that the senses of concepts change depending on application domains. So here are presented the following...
Here is presented a platform for automatic composition of inductive learning systems using ontologies called CAMLET, based on knowledge modeling and ontologies engineering technique. CAMLET constructs an inductive applications with better competence to a given data set, using process and object ontologies. Afterwards, CAMLET instantiates and refines a constructed system based on the following refinement...
In this paper we present the key concepts of an experiences based management system that assists the transformation of individual experiences into usefull knowledge. Such a framework is designed around the notion of capturing and delivering experiences for improving worker’s practices and tasks. The study has been done in the area of customer relationships. In this paper we present our approach to...
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