34275264
SWARMs of Self-Organizing Polymorphic Agents
The field of swarm intelligence is increasingly being seen as providing a framework for solving a wide range of large-scale, distributed, complex problems. Of particular interest are architectures and methodologies that address organization and coordination of a large number of relatively simple agents distributed across the system in a way that encourages some desirable global emergent behavior. This paper describes a SWARM simulation of a distributed approach to fault mitigation within a large-scale data acquisition system for BTeV, a particle accelerator-based high energy physics experiment currently under development at Fermi National Accelerator Laboratory. Incoming data is expected to arrive at a rate of over 1 terabyte every second, distributed across 2500 digital signal processors. Simulation results show how lightweight polymorphic agents embedded within the individual processors use game theory to adapt roles based on the changing needs of the environment. SWARM architecture and implementation methodologies are detailed.
21082870
Environment Organization of Roles Using Polymorphism
In the field of multi-agent systems, there has lately been a growing interest on ways in which the environment can better be exploited to coordinate agent behavior and manage complex problems. This paper describes an environment that is able to organize and adapt agent roles as conditions warrant. Roles are adapted using polymorphism as directed by the environment. The design combines strategies from game theory and other biologically inspired models to address fault mitigation in large-scale, real-time, distributed systems. It is implemented on a prototype of the data acquisition system for BTeV, a High Energy Physics experiment consisting of 2500 digital signal processors. Results show environment organization of roles for the lightweight agents embedded within each of the individual processors.
14162832
Text-independent open-set speaker identification for military missions using genetic rule-based system
We present a genetic classifier system approach to the text-independent open-set speaker identification problem. Classifier systems are widely used in symbolic problem for dynamically changing open-ended learning. Signal processing problems require processing of real-valued parameters that classifier systems are not designed for. On the other hand, the approaches based on common cepstral encoding with clustering algorithms handle the closed-set speaker identification quite well. This research solves the open-set problem by hybridizing these two approaches.
8366022
A rationality-based modeling for coalition support
We present a game theoretic model for multi-agent resource distribution and allocation where agents in the environment must help each other to survive. Each agent maintains a set of two-tuples T=(A, P) called friendship values representing actual friendship and perceived friendship. The model directly addresses problems in reputation management schemes in multi-agent systems and peer-to-peer distributed systems. We present algorithms for maintaining the friendship values as well as a utility equation used in each agent's decision making. For an application problem, we adapted our formal model to the military coalition support problem in peace-keeping missions. Simulation results show that efficient resource allocation and sharing with minimum communication cost is achieved without centralized control.
739
Cooperative game theory within multi-agent systems for systems scheduling
Research concerning organization and coordination within multi-agent systems continues to draw from a variety of architectures and methodologies. The work presented in this paper combines techniques from game theory and multi-agent systems to produce self-organizing, polymorphic, lightweight, embedded agents for systems scheduling within a large-scale real-time systems environment. Results show how this approach is used to experimentally produce optimum real-time scheduling through the emergent behavior of thousands of agents. These results are obtained using a SWARM simulation of systems scheduling within a high energy physics experiment consisting of 2500 digital signal processors.
3206497
Polymorphic self-* agents for stigmergic fault mitigation in large-scale real-time embedded systems
Organization and coordination of agents within large-scale, complex, distributed environments is one of the primary challenges in the field of multi-agent systems. A lot of interest has surfaced recently around self-* (self-organizing, self-managing, self-optimizing, self-protecting) agents. This paper presents polymorphic self-* agents that evolve a core set of roles and behavior based on environmental cues. The agents adapt these roles based on the changing demands of the environment, and are directly implementable in computer systems applications. The design combines strategies from game theory, stigmergy, and other biologically inspired models to address fault mitigation in large-scale, real-time, distributed systems. The agents are embedded within the individual digital signal processors of BTeV, a High Energy Physics experiment consisting of 2500 such processors. Results obtained using a SWARM simulation of the BTeV environment demonstrate the polymorphic character of the agents, and show how this design exceeds performance and reliability metrics obtained from comparable centralized, and even traditional decentralized approaches.
18176797
User evaluations on form factors of tangible magic lenses
Magic Lens is a small inset window embedded in a large context display, which provides an alternative view to the region of interest selected from the context view. This metaphor is used for 3D visualization in our Augmented Virtual Environment infrastructure, SCAPE (Stereoscopic Collaboration in Augmented and Projective Environments), which is composed of an immersive room display for a high level of detail, life-size virtual world and a workbench display for simplified god-like view to the world. A tangible Magic Lens is used on the workbench display to allow direct and intuitive selection of continuous levels of detail, bridging the gap between the two extreme levels of detail in SCAPE. This paper presents our first step to the user evaluations of tangible Magic Lens. We conducted two sets of user evaluations, one mainly testing the lens aspect ratio, and another for the lens size. For both of the tests, two types of tasks are conducted: information gathering and relating the detailed information with the context. We found that the aspect ratio of a lens plays more important role in user preference for smaller lenses than for larger ones. Meanwhile, the size of a lens is the most important factor that affects the user performance in the two types of tasks.
9965855
SESAME: towards better 3D conceptual design systems
Conceptual design dominates the early stages of most creative design processes. During these stages, the designer makes important decisions about the parameters of a model that are aimed at satisfying a set of design criteria. To do this, the designer produces many sketches of various possible solutions. Meanwhile, computer-aided design (CAD) systems are becoming the dominant visual medium used in design practice. However, these tools evolved as documentation production tools and do not support conceptual thinking. This paper presents a list of guidelines for computer support for conceptual design activities on 3D scenes and presents SESAME (Sketch, Extrude, Sculpt, and Manipulate Easily), a system based on these guidelines. Finally, we present a user study comparing SESAME with a conventional CAD package to demonstrate the effectiveness of SESAME.
8407543
A system for desktop conceptual 3D design
In the traditional design process for a 3D environment, people usually depict a rough prototype to verify their ideas, and iteratively modify its configuration until they are satisfied with the general layout. In this activity, one of the main operations is the rearrangement of single and composite parts of a scene. With current desktop virtual reality (VR) systems, the selection and manipulation of arbitrary objects in 3D is still difficult. In this work, we present new and efficient techniques that allow even novice users to perform meaningful rearrangement tasks with traditional input devices. The results of our work show that the presented techniques can be mastered quickly and enable users to perform complex tasks on composite objects. Moreover, the system is easy to learn, supports creativity, and is fun to use.