Bruno Feijó

Control centers evolve with agent technology

Energy management system (EMS) architectures are deeply influenced by power system and information system scenarios. The computer industry has been evolving continuously, and the power industry, which remained relatively stable for decades, is now undergoing revolutionary changes that require the special attention of EMS developers. The introduction of new players, the decentralization of production and processing of information, and competition will change the way control centers operate and, consequently, their architecture. Distinct areas of computer science have suggested many different approaches to tackle the problems that arise in the new decentralized scenario. Of particular interest, agent technology, proposed in the context of distributed artificial intelligence, is a very promising approach to support the construction of a new generation of EMS in an open environment.

Automatic Dynamic Task Distribution between CPU and GPU for Real-Time Systems

The increase of computational power of programmable GPU (graphics processing unit) brings new concepts for using these devices for generic processing. Hence, with the use of the CPU and the GPU for data processing come new ideas that deals with distribution of tasks among CPU and GPU, such as automatic distribution. The importance of the automatic distribution of tasks between CPU and GPU lies in three facts. First, automatic task distribution enables the applications to use the best of both processors. Second, the developer does not have to decide which processor will do the work, allowing the automatic task distribution system to choose the best option for the moment. And third, sometimes, the application can be slowed down by other processes if the CPU or GPU is already overloaded. Based on these facts, this paper presents new schemes for efficient automatic task distribution between CPU and GPU. This paper also includes tests and results of implementing those schemes with a test case and with a real-time system.

An agent-based paradigm for building intelligent CAD systems

The present paper proposes a hybrid approach based on reactive agent technology as a post-object paradigm for building intelligent CAD systems. It represents an evolution of previous positions and articles by the authors motivated by a clear need to provide forms of active support for the design process rather than seeking full design automation purposes. Key concepts about agency and reactivity, as well as their usefulness in design systems, are presented. Also a basic tool called XLOG+, for developing agent-based systems, comprising logic and object oriented approaches, is discussed. Finally, the proposed hybrid agents are embedded in a new integrated CAD system architecture and an implemented prototype example in the area of solid modelling is briefly presented.

Agents with emotions in behavioral animation

Abstract This paper presents an innovative architecture for emotional characters in computer animation based on a reactive agent structure. The architecture is based on a cognitive model where both controlled and automatic procedures coexist. The proposed architecture breaks with traditional AI paradigms and establishes an efficient approach to develop behavioral animation systems.

An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU

This article presents a new architecture to implement all game loop models for games and real-time applications that use the GPU as a mathematics and physics coprocessor, working in parallel processing mode with the CPU. The presented model applies automatic task distribution concepts. The architecture can apply a set of heuristics defined in Lua scripts in order to get acquainted with the best processor for handling a given task. The model applies the GPGPU (general-purpose computation on GPUs) paradigm. In this article we propose an architecture that acquires knowledge about the hardware by running tasks in each processor and, by studying their performance over time, finding the best processor for a group of tasks.

A Neighborhood Grid Data Structure for Massive 3D Crowd Simulation on GPU

Simulation and visualization of emergent crowd in real-time is a computationally intensive task. This intensity mostly comes from the

Automatic Video Editing for Video-Based Interactive Storytelling

O(n^2)

Genetic adaptation of segmentation parameters

complexity of the traversal algorithm, necessary for the proximity queries of all pair of entities in order to compute the relevant mutual interactions. Previous works reduced this complexity by considerably factors, using adequate data structures for spatial subdivision and parallel computing on modern graphic hardware, achieving interactive frame rates in real-time simulations. However, the performance of existent proposals are heavily affected by the maximum density of the spatial subdivision cells, which is usually high, yet leading to algorithms that are not optimal. In this paper we extend previous neighborhood data structure, which is called neighborhood grid, and a simulation architecture that provides for extremely low parallel complexity. Also, we implement a representative flocking boids case-study from which we run benchmarks with simulation and rendering of up to 1 million boids at interactive frame-rates. We remark that this work can achive a minimum spee up of 2.94 when compared to traditional spatial subdivision methods with a similar visual experience and with lesser use of memory.

Kuaba ontology: design rationale representation and reuse in model-based designs

The development of interactive narratives with the quality of feature films is the central challenge of what we can name Video-Based Interactive Storytelling. A promising approach to this question is the use of prerecorded videos with real actors. Amongst several technical challenges, this approach firstly requires automatic video editing methods for interactive narratives. However, this is a critical issue not fully covered in the literature. In this paper, we present a real-time editing method for interactive storytelling systems, which automatically generates the most adequate shot transitions, swaps video segments to avoid jump cuts, and creates adequate looping scenes. Moreover, these features consider the characteristics of the ongoing story.

A bidimensional data structure and spatial optimization for supermassive crowd simulation on GPU

This work presents a method for the automatic adaptation of segmentation parameters based on Genetic Algorithms. An intuitive and computationally simple fitness function, which expresses the similarity between the segmentation result and a reference provided by the user, is proposed. The method searches the solution space for a set of parameter values that minimizes the fitness function. A prototype including an implementation of a widely used segmentation algorithm was developed to assess the performance of the method. A set of experiments with medium and high spatial resolution remote sensing image data was carried out and the method was able to come close to the ideal solutions.