Fractals show machine intentions

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Fractals show machine intentions

By Eric Smalley, Technology Research News

There has been much research and musing about how autonomous machines like robots and intelligent software agents should interact with people. Much of the work focuses on giving machines a degree of social intelligence that will allow people to understand and communicate with them on human terms.

A sense of internal states is integral to human communications: it's useful to have a sense of when a human is annoyed. In contrast, it's often impossible to determine whether a robot is processing data, awaiting instruction or in need of repair.

Researchers from Switzerland and South Africa have designed a visual interface that would give autonomous machines the equivalent of body language.

The interface represents a machine's internal state in a way that makes it possible for observers to interpret the machine's behavior. "Our idea of communication has a strong focus on learning and interpretation -- trying to create relationships between the internal machine variables and the macroscopic behavior," said Jan-Jan van der Vyver, a researcher at the University of Zürich and the Swiss Federal Institute of Technology.

The researchers' autonomous machine interface consists of a clustering algorithm that groups the machine's many internal states into a manageable number of representations, and a fractal generator.

Clustering algorithms organize data like that contained in genes into groups with similar traits, and analyze raw data without any sense of the data's meaning or assumptions about how it should be structured.

In the researchers' scheme, snapshots of a machine's sensory input, computational processing and output are clustered and the clusters are displayed as fractal images. The fractal generator produces a fractal pattern in the center of the display and patterns move outward in concentric rings, giving observers a sense of change over time.

Fractal generators produce a large variety patterns that people are quick to distinguish. A set of snapshots corresponding to a high degree of sensory stimulation could be clustered into a representation of the machine that people learn to associate with the machine observing a change in its environment, for example.

In coming up with a way to convey the data, the researchers were careful to avoid any anthropomorphic representations that human observers might associate with particular behaviors or intentions, according to van der Vyver. Those associations are not likely to correspond to the machine's behavior, he said.

The fractal display served as the interface to a neural network that controlled the input and output devices of a smart room at the Swiss national exposition Expo.02 from May to October 2002. Exposition goers were able to interact with the room through the room's cameras, microphones, pressure sensors, light projectors and speakers.

Observers were able to correlate the room's behavior with the fractal display, said van der Vyver. "What we found surprising was that the general public so quickly gravitated toward our chosen implementation of the communication interface, and so quickly learned to interpret it," he said.

The smart room, dubbed Ada -- The Intelligent Space, was not a fully autonomous system, but demonstrated the viability of the fractal display, said van der Vyver. Truly autonomous systems are likely to emerge in the future, in part due to self-developing technologies like genetic algorithms that evolve optimized designs, he said.

Given the prospect of self-evolving machines, the researchers argue for a broad definition of autonomous systems as systems developing according to their own dynamics through interaction with their environment. The ultimate in autonomous machines is a system that develops intelligent behavior simply as a result of participating in a society, van der Vyver said.

It's not clear that the researchers' approach is necessary, said Jeffrey Nickerson, an associate professor of computer science at Stevens Institute of Technology. Autonomous machines could be programmed to explicitly represent their intentions, he said. "If understanding intentions is hard, then why not force the machine to provide indications of intentions, or at least a trace of reasoning?"

Initial practical applications of the researchers' work are about five years away, said van der Vyver. "As the development and deployment of more autonomous machines takes place, this research comes into play," he said. However, self-evolving, self-repairing machines are a long way off, he said.

Van der Vyver's research colleagues were Markus Christen and Thomas Ott of the University of Zürich and the Swiss Federal Institute of Technology, Norbert Stoop of the Swiss Federal Institute of Technology, Willi-Hans Steeb of the Rand Afrikaans University, the International School for Scientific Computing in South Africa and the University of Applied Sciences of Northwestern Switzerland, and Ruedi Stoop of the Swiss Federal Institute of Technology and the University of Applied Sciences of Northwestern Switzerland. The work appeared in the March 31, 2004 issue of Robotics and Autonomous Systems. The research was funded by the researchers' institutions.

Timeline: 5 years
Funding: University
TRN Categories: Robotics; Human-Computer Interaction
Story Type: News
Related Elements: Technical paper, "Towards Genuine Machine Autonomy," Robotics and Autonomous Systems, March 31, 2004

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