Using a collection of humans as an execution testbed for swarm algorithms

Palmer, William D.; Kirschenbaum, Marc; Murton, J.; Kovacina, Michael A.; Steinberg, Daniel; Calabrese, Sam N.; Zając, Katarzyna; Hantak, Chad M.; Schatz, Jirka

doi:10.1109/sis.2003.1202248

Cited by 12 publications

(3 citation statements)

References 5 publications

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“…The author argues that computers (robots in this context) should act as intelligent assistants; it should monitor our actions, to shield against human errors. Palmer et al in [13] present a novel approach to swarm intelligence research. They begin with the quote "Smart things Form Teams, Stupid things Swarm" and modify the question to "What can we learn about swarms by having smart things act dumb?"…”

Section: Human Swarm Interaction For Radiation Source Search and Locamentioning

confidence: 99%

Human swarm interaction for radiation source search and localization

Bashyal

Venayagamoorthy

2008

2008 IEEE Swarm Intelligence Symposium

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This study 1 shows that appropriate human interaction can benefit a swarm of robots to achieve goals more efficiently. A set of desirable features for human swarm interaction is identified based on the principles of swarm robotics. Human swarm interaction architecture is then proposed that has all of the desirable features. A swarm simulation environment is created that allows simulating a swarm behavior in an indoor environment. The swarm behavior and the results of user interaction are studied by considering radiation source search and localization application of the swarm. Particle swarm optimization algorithm is slightly modified to enable the swarm to autonomously explore the indoor environment for radiation source search and localization. The emergence of intelligence is observed that enables the swarm to locate the radiation source completely on its own. Proposed human swarm interaction is then integrated in a simulation environment and user evaluation experiments are conducted. Participants are introduced to the interaction tool and asked to deploy the swarm to complete the missions. The performance comparison of the user guided swarm to that of the autonomous swarm shows that the interaction interface is fairly easy to learn and that user guided swarm is more efficient in achieving the goals. The results clearly indicate that the proposed interaction helped the swarm achieve emergence.

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Section: Human Swarm Interaction For Radiation Source Search and Locamentioning

confidence: 99%

Human swarm interaction for radiation source search and localization

Bashyal

Venayagamoorthy

2008

2008 IEEE Swarm Intelligence Symposium

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“…Instead of using the individual programmer (5) to force actions, a virtual human swarm (0) [10] experiment is run. It is possible to limit all sensory input to a virtual human swarm allowing researchers to design experiments enforcing constraints on humans to match agent sensorial capabilities.…”

Section: Control Flow Of Emergence-oriented Programmingmentioning

confidence: 99%

“…These experiments have a human be the controller for each agent in the swarm. Elsewhere, we describe the process of extracting swarm algorithms from observing physical human swarms that take place in a gymnasium (see Figure 4) [10]. To more precisely control the human swarm environm e sily extract information, we developed virtual human swarm (VHS) experiments that take place in a networked virtual environment (see Figures 5,6,7).…”

Section: Virtual Human Swarm (Vhs)mentioning

confidence: 99%

Emergence-Oriented Programming

Palmer

Kirschenbaum

Seiter

2005 IEEE International Conference on Systems, Man and Cybernetics

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In this paper we describe Emergence-Oriented Programming (EOP), a novel, human-centric technique to engineer swarm algorithms at a higher level of complexity than those developed with simple reactive agents. The process is iterative, building modules of behavior that can be layered to produce solutions that converge faster than reactive swarms to the desired emergent goal. The layers are modular and can be independently applied, mirroring the arbitrarily nested cognitive model proposed by Baas and Emmeche. The layers are produced by external observers recognizing and reinforcing patterns within swarms that are not visible at lower levels. Each layer builds upon the previous one leading to emergence, but the entire hierarchy can be mechanically collapsed into executable if-then rules based on robot primitives. We demonstrate portions of this technique to improve on the reactive swarm approach for solving the 4-color mapping problem.

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