Catálogo de publicaciones - libros

This talk presents a synergistic methodology based on generalized stochastic Petri-nets (SPN) and neural nets for efficiently developing planning strategies. The SPN planning method generates global plans based on the states of the elements of the Universe of Discourse. Each plan includes all the possible conflict free planning paths for achieving the desirable goals under certain constraints occurred at the problem to be solved. The a neural network is used for searching the vectors of markings generated by the SPN reachability graph for the appropriate selection of plans. The SPN model presents high complexity issues, but at the same time offers to the synergic important features, such as stochastic modeling, synchronization, parallelism, concurrency and timing of events, valuable for developing plans under uncertainty. The neural network does contribute to the high complexity, but it offers learning capability to the synergy for future use. An example for coordinating two robotic arms under the constraints of time, space, and placement of the objects will be presented.

What patterns can we find in a bursty web traffic? On the web or on the internet graph itself? How about the distributions of galaxies in the sky, or the distribution of a company’s customers in geographical space? How long should we expect a nearest-neighbour search to take, when there are 100 attributes per patient or customer record? The traditional assumptions (uniformity, independence, Poisson arrivals, Gaussian distributions), often fail miserably. Should we give up trying to find patterns in such settings? Self-similarity, fractals and power laws are extremely successful in describing real datasets (coast-lines, rivers basins, stock-prices, brain-surfaces, communication-line noise, to name a few). We show some old and new successes, involving modeling of graph topologies (internet, web and social networks); modeling galaxy and video data; dimensionality reduction; and more.

In this work, we model speech samples with a two-sided generalized Gamma distribution and evaluate its efficiency for voice activity detection. Using a computationally inexpensive maximum likelihood approach, we employ the Bayesian Information Criterion for identifying the phoneme boundaries in noisy speech.

As the field of Intelligent Virtual Agents evolves and advances, an ever increasing number of functional and useful applications are presented. Intelligent Virtual Agents have become more realistic, intelligent and sociable, with apparent and substantial benefits to domains such as training, tutoring, simulation and entertainment. However, even though many end-users can enjoy these benefits today, the development of such applications is restricted to specialized research groups and companies. Obvious and difficult-to-overcome factors contribute to this. The inherent complexity of such applications results in increased theoretical and technical requirements to their development. Furthermore, Intelligent Virtual Agent systems today typically offer ad hoc, if any, design and development means that lack completeness and a general-purpose character. Significant efforts have been successfully made towards deriving globally accepted standards; nevertheless these mostly focus on communication between heterogeneous systems and not on design and development. In this paper, we present our current efforts towards a novel architecture for Intelligent Virtual Agents which is based on our previous work in the field and encompasses the full range of characteristics considered today as fundamental to achieving believable Intelligent Virtual Agent behaviour. In the spirit of enabling and easing application design and development, as well as facilitating further research, our architecture is tightly coupled with a behaviour specification language that uniformly covers all aspects and stages of the development process. We also present the key guidelines for a minimal but functional implementation, aimed in validation and experimentation.

In this paper a new maximum a posteriori (MAP) approach based on mixtures of multinomials is proposed for discovering probabilistic patterns in sequences. The main advantage of the method is the ability to bypass the problem of overlapping patterns in neighboring positions of sequences by using a Markov random field (MRF) prior. This model consists of two components, the first models the pattern and the second the background. The Expectation-Maximization (EM) algorithm is used to estimate the model parameters and provides closed form updates. Special care is also taken to overcome the known dependence of the EM algorithm to initialization. This is done by applying an adaptive clustering scheme based on the -means algorithm in order to produce good initial values for the pattern multinomial model. Experiments with artificial sets of sequences show that the proposed approach discovers qualitatively better patterns, in comparison with maximum likelihood (ML) and Gibbs sampling (GS) approaches.

A hardware architecture is presented, which accelerates the per- formance of intelligent applications that are based on logic programming. The logic programs are mapped on hardware and more precisely on FPGAs (Field Programmable Gate Array). Since logic programs may easily be transformed into an equivalent Attribute Grammar (AG), the underlying model of implementing an embedded system for the aforementioned applications can be that of an AG evaluator. Previous attempts to the same problem were based on the use of two separate components. An FPGA was used for mapping the inference engine and a conventional RISC microprocessor for mapping the unification mechanism and user defined additional semantics. In this paper a new architecture is presented, in order to drastically reduce the number of the required processing elements by a factor of (length of input string). This fact and the fact of using, for the inference engine, an extension of the most efficient parsing algorithm, allowed us to use only one component i.e. a single FPGA board, eliminating the need for an additional external RISC microprocessor, since we have embedded two “PicoBlaze” Soft Processors into the FPGA. The proposed architecture is suitable for embedded system applications where low cost, portability and low power consumption is of crucial importance. Our approach was tested with numerous examples in order to establish the performance improvement over previous attempts.

Decision making is widely considered as a fundamental organizational activity that comprises a series of knowledge representation and processing tasks. Admitting that the quality of a decision depends on the quality of the knowledge used to make it, it is argued that the enhancement of the decision making efficiency and effectiveness is strongly related to the appropriate exploitation of all possible organizational knowledge resources. Taking the above remarks into account, this paper presents a multidisciplinary approach for capturing the organizational knowledge in order to augment teamwork in terms of knowledge elicitation, sharing and construction, thus enhancing decision making quality. Based on a properly defined ontology model, our approach is supported by a web-based tool that serves as a forum of reciprocal knowledge exchange, conveyed through structured argumentative discourses, the ultimate aim being to support the related decision making process. The related knowledge is represented through a Discourse Graph, which is structured and evaluated according to the knowledge domain of the problem under consideration.

The market clearing prices in deregulated electricity markets are volatile. Good market clearing price forecasting will help producers and consumers to prepare their corresponding bidding strategies so as to maximize their profits. Market clearing price prediction is a difficult task since bidding strategies used by market participants are complicated and various uncertainties interact in an intricate way. This paper proposes an adaptively trained neural network to forecast the 24 day-ahead market-clearing prices. The adaptive training mechanism includes a feedback process that allows the artificial neural network to learn from its mistakes and correct its output by adjusting its architecture as new data becomes available. The methodology is applied to the California power market and the results prove the efficiency and practicality of the proposed method.

A random signal-based learning merged with simulated annealing (SARSL), which is serial algorithm, has been considered by the authors. But the serial nature of SARSL degrades its performance as the complexity of the search space is increasing. To solve this problem, this paper proposes a population structure of SARSL (PSARSL) which enables multi-point search. Moreover, adaptive partitioning method (APM) is used to reduce the optimization time. The validity of the proposed algorithm is conformed by applying it to a simple test function example and a general version of fuzzy controller design.

This paper is concerned with cascade fuzzy neural networks and its optimization. These networks come with sound and transparent logic characteristics by being developed with the aid of AND and OR fuzzy neurons and subsequently logic processors (LPs). We discuss main functional properties of the model and relate them to its form of cascade type of systems formed as a stack of LPs. The structure of the network that deals with a selection of a subset of input variables and their distribution across the individual LPs is optimized with the use of genetic algorithms (GA). We discuss random signal-based learning (RSL), a local search technique, aimed at further refinement of the connections of the neurons (GA-RSL). We elaborate on the interpretation aspects of the network and show how this leads to a Boolean or multi-valued logic description of the experimental data. Two kinds of standard data sets are discussed with respect to the performance of the constructed networks and their interpretability.