Roman Neruda

Learning methods for radial basis function networks

RBF networks represent a vital alternative to the widely used multilayer perceptron neural networks. In this paper we present and examine several learning methods for RBF networks and their combinations. A gradient-based learning, the three-step algorithm with unsupervised part, and an evolutionary algorithms are introduced, and their performance compared on benchmark problems from the Proben 1 database. The results show that the three-step learning is usually the fastest, while the gradient learning achieves better precision. The best results can be achieved by employing hybrid approaches that combine presented methods.

Modeling and Discovery of Data Providing Services

Web Services providing access to datasources with structured data have an important place in the SOA. In this paper we focus on modeling and discovery of generic data providing services (DPS), with the goal of making data providing services available for interactions with service requesters in contexts such as service composition and mediation. In our model RDF Views are used to represent the content provided by the DPS. A characterization of match between description of DPS as RDF Views and the OWL-S service request is specified, based on which we developed a flexible matchmaking algorithm for discovery of data providing services. Finally, we propose a realization of the DPS using a SOAP version of the SPARQL protocol and a dynamic configuration interface allowing easy interactions of service requesters with data providing services.

The process mediation framework for semantic web services

The ability to deal with the incompatibilities of service requesters and providers is a critical factor for achieving interoperability in dynamic open environments. We focus on the problem of process mediation of the semantically annotated process models of the service requester and service provider. We propose an Abstract Process Mediation Framework (APMF) identifying the key functional areas that need to be addressed by process mediation components. Next, we present algorithms for solving the process mediation problem in two scenarios: (1) when the mediation process has complete visibility of the process model of the service provider and service requester (complete visibility scenario) and (2) when the mediation process has visibility only of the process model of the service provider, but not the service requester (asymmetric scenario). The algorithms combine planning and semantic reasoning with the discovery of appropriate external services such as data mediators. Finally, the Process Mediation Agent (PMA) is introduced, which realises an execution infrastructure for runtime mediation.

An agent for asymmetric process mediation in open environments

The ability to deal with incompatibilities of service requesters andproviders is a critical factor for achieving interoperability in dynamic open environments.We propose a Process Mediation Agent (PMA) as a solution to the processmediation problem in situations when the requester does not want to revealits process model completely for privacy reasons. The PMA automatically resolvesencountered incompatibilities by generating mappings between processesof the requester and the provider and applies them for the runtime translations.In the PMA algorithms we combine the AI planing and semantic reasoning withrecovery techniques and the discovery of appropriate external data mediators.

Implementing GP on Optimizing both Boolean and Extended Boolean Queries in IR and Fuzzy IR systems with Respect to the Users Profiles

Rapidly growing amount of the data available on World Wide Web (WWW) as the ultimate collection of public accessible text documents complicates the task of acquiring usable and relevant information from raw data. Several methods of improvement and optimization of information retrieval (IR) systems providing user interaction with WWW documents have been introduced and discussed. In this paper, theory and application of genetic programming (GP) as an optimization method of modern user oriented IR and fuzzy IR (FIR) systems optimization of search queries is presented.

Aggregate meta-models for evolutionary multiobjective and many-objective optimization

Abstract Evolutionary algorithms are among the best multiobjective optimizers. However, they need a large number of function evaluations. In this paper a meta-model based approach to the reduction in the needed number of function evaluations is presented. Local aggregate meta-models are used in a memetic operator. The algorithm is first discussed from a theoretical point of view and then it is shown that the meta-models greatly reduce the number of function evaluations. The approach is compared to a similar one with a single global meta-model as well as to more traditional NSGA-II and ϵ - IBEA . Moreover, it is shown that aggregate meta-models work even for a larger number of objectives and therefore should be considered when designing many-objective evolutionary algorithms.

ASM-MOMA: Multiobjective memetic algorithm with aggregate surrogate model

Evolutionary algorithms generally require a large number of objective function evaluations which can be costly in practice. These evaluations can be replaced by evaluations of a cheaper meta-model (surrogate model) of the objective functions. In this paper we present a novel distance based aggregate surrogate model for multiobjective optimization and describe a memetic multiobjective algorithm based on this model. Various variants of the models are tested and discussed and the algorithm is compared to standard multiobjective evolutionary algorithms. We show that our algorithm greatly reduces the number of required objective function evaluations.

Meta Learning in Multi-agent Systems for Data Mining

In this paper we present the Pikater multi-agent system designed for solving complex data mining tasks. We emphasize the unique intelligent features of the system -- its ability to search the parameter space of the data mining methods to find the optimal configuration, and meta learning -- finding the best possible method for the given data based on the ontological compatibility of datasets.

Bang 3: a computational multi-agent system

A multiagent system targeted toward the area of computational intelligence modeling is presented. The purpose of the system is to allow both experiments and high-performance distributed computations employing hybrid computational models. The focus of the system is the interchangeability of computational components, their autonomous behavior, and emergence of new models.

Role-based design of computational intelligence multi-agent system

In recent works concerning open multi-agent systems (MAS), the emphasis has been laid on organizational aspects of the development of agent societies. Many models of collaboration and cooperation of agents have been proposed which allow reusability of design patterns in MAS and separation of concerns. One of these approaches is the role-based model, inspired by the importance of roles in real human communities. In this paper we apply the concepts of role-based models on a concrete scenario in the field of Computational MAS.