Catálogo de publicaciones - libros

The development of intelligent multi-agent systems involves a number of concerns, including mobility, context-awareness, reasoning and mining. Towards ubiquitous intelligence this area of research addresses the intersection between mobile agents, heterogeneous networks, and ubiquitous intelligence. This paper presents a development of hardware and software systems to address the combination of these interests as Location-Aware Service. Our architecture performs the intelligent services to meet the respective requirements. By adding autonomous mobility to the agents, the system becomes more able to dynamically localize around areas of interest and adapt to changes in the ubiquitous intelligence landscape. We also analyze some lessons learned based on our experience in using location-aware multi-agent techniques and methods.

In this paper, we present the CIFF platform for multi-agent systems.

The platform is based on Abductive Logic Programming, with a uniform language for specifying agent policies and interaction protocols. A significant advantage of the computational logic foundation of the CIFF framework is that the declarative specifications of agent policies and interaction protocols can be used directly, at runtime, as the programs for the agent instances and for the verification of compliance.

We also provide a definition of conformance of an agent policy to an interaction protocol (i.e., a property that guarantees that an agent will comply to a given protocol) and a operational procedure to test conformance.

Text search engines are inadequate for indexing and searching XML documents because they ignore metadata and aggregation structure implicit in the XML documents. On the other hand, the query languages supported by specialized XML search engines are very complex. In this paper, we present a simple yet flexible query language, and develop its semantics to enable intuitively appealing of relevant fragments of information while simultaneously falling back on through plain text search if necessary. We also present a simple yet robust relevance ranking for heterogeneous document-centric XML.

The huge amount of image collections connected to multimedia applications has brought forth several approaches to content-based image retrieval, that means retrieving images based on their visual content instead of textual descriptions. In this paper, we present a system, called VIRMA (Visual Image Retrieval by Shape MAtching), which combines different techniques from Computer Vision to perform content-based image retrieval based on shape matching. The architecture of the VIRMA system is portrayed and algorithms underpinning the developed prototype are briefly described. Application of VIRMA to a database of real-world pictorial images shows its effectiveness in visual image retrieval.

Formal Concept Analysis is an unsupervised machine learning technique that has successfully been applied to document organisation by considering documents as objects and keywords as attributes. The basic algorithms of Formal Concept Analysis then allow an intelligent information retrieval system to cluster documents according to keyword views. This paper investigates the scalability of this idea. In particular we present the results of applying spatial data structures to large datasets in formal concept analysis. Our experiments are motivated by the application of the Formal Concept Analysis idea of a virtual filesystem [11,17,15]. In particular the libferris [1] Semantic File System. This paper presents customizations to an RD-Tree Generalized Index Search Tree based index structure to better support the application of Formal Concept Analysis to large data sources.

In our continuing work on ”Blind Signal Separation” this paper focuses on extending our previous work [1] by creating a data set that can successfully perform blind separation of polyphonic signals containing similar instruments playing similar notes in a noisy environment. Upon isolating and subtracting the dominant signal from a base signal containing varying types and amounts of noise, even though we purposefully excluded any identical matches in the dataset, the signal separation system successfully built a resulting foreign set of synthesized sounds that the classifier correctly recognized. Herein, this paper presents a system that classifies and separates two harmonic signals with added noise. This novel methodology incorporates Knowledge Discovery, MPEG7-based segmentation and Inverse Fourier Transforms.

This paper introduces a kernel selection method to automatically choose the best kernel type for a query by using the score distributions of the relevant and non-relevant images given by user as feedback. When applied to our data, the method selects the same best kernel (out of the 12 tried kernels) for a particular query as the kernel obtained from our extensive experimental results.

This paper describes a novel approach to business intelligence and program management for large technology enterprises like the U.S. National Aeronautics and Space Administration (NASA). Two key distinctions of the approach are that 1) standard business documents are the user interface, and 2) a “schema-less” XML database enables flexible integration of technology information for use by both humans and machines in a highly dynamic environment. The implementation utilizes patent-pending NASA software called the NASA Program Management Tool (PMT) and its underlying “schema-less” XML database called Netmark. Initial benefits of PMT include elimination of discrepancies between business documents that use the same information and “paperwork reduction” for program and project management in the form of reducing the effort required to understand standard reporting requirements and to comply with those reporting requirements. We project that the underlying approach to business intelligence will enable significant benefits in the timeliness, integrity and depth of business information available to decision makers on all organizational levels.

This paper presents the advantage that knowledge-intensive activities, such as Scientific Conference Management, can take by the exploitation of expert components in the key tasks. Typically, in this domain the task of scheduling the activities and resources or the assignment of reviewers to papers is performed manually leading therefore to time-consuming procedures with high degree of inconsistency due to many parameters and constraints to be considered. The proposed systems, evaluated on real conference datasets, show good results compared to manual scheduling and assignment, in terms of both accuracy and reduction of runtime.

This paper describes a data replication service for large-scale, data intensive applications whose results can be shared among geographically distributed scientists. We introduce two kinds of data replication techniques, called owner-initiated data replication and client-initiated data replication, that are developed to support data replica consistency without requiring any special file system-level locking functions. Also, we present performance results on Linux clusters located at Sejong University.