Bill Hibbard

The VIS-5D system for easy interactive visualization

The VIS-5D system provides highly interactive visual access to five-dimensional data sets containing up to 50 million data points. VIS-5D runs on the Stardent ST-1000 and ST-2000 workstations and generates animated three-dimensional graphics from gridded data sets in real time. It provides a widget-based user interface and fast visual response which allows scientists to interactively explore their data sets. VIS-5D generates literal and intuitive depictions of data, has user controls that are data oriented rather than graphics oriented, and provides a WYSIWYG (what-you-see-is-what-you-get) response. The result is a system that enables scientists to produce and direct their own animations.<<ETX>>

VisAD: connecting people to computations and people to people

In our last Computer Graphics issue, Greg Johnson contributed an interesting survey article on collaborative visualization. Another perspective on this hard problem is discussed by Bill Hibbard in the following article. Most of you in the visualization community know Bill from his pioneering work on Vis5D and more recently VisAD. As always, I welcome your comments and thoughts on this column.

Bias and No Free Lunch in Formal Measures of Intelligence

Bias and No Free Lunch in Formal Measures of Intelligence This paper shows that a constraint on universal Turing machines is necessary for Leggs and Hutters formal measure of intelligence to be unbiased. Their measure, defined in terms of Turing machines, is adapted to finite state machines. A No Free Lunch result is proved for the finite version of the measure.

Model-based Utility Functions

Abstract Orseau and Ring, as well as Dewey, have recently described problems, including self-delusion, with the behavior of agents using various definitions of utility functions. An agents utility function is defined in terms of the agents history of interactions with its environment. This paper argues, via two examples, that the behavior problems can be avoided by formulating the utility function in two steps: 1) inferring a model of the environment from interactions, and 2) computing utility as a function of the environment model. Basing a utility function on a model that the agent must learn implies that the utility function must initially be expressed in terms of specifications to be matched to structures in the learned model. These specifications constitute prior assumptions about the environment so this approach will not work with arbitrary environments. But the approach should work for agents designed by humans to act in the physical world. The paper also addresses the issue of self-modifying agents and shows that if provided with the possibility to modify their utility functions agents will not choose to do so, under some usual assumptions.

Avoiding unintended AI behaviors

Artificial intelligence (AI) systems too complex for predefined environment models and actions will need to learn environment models and to choose actions that optimize some criteria. Several authors have described mechanisms by which such complex systems may behave in ways not intended in their designs. This paper describes ways to avoid such unintended behavior. For hypothesized powerful AI systems that may pose a threat to humans, this paper proposes a two-stage agent architecture that avoids some known types of unintended behavior. For the first stage of the architecture this paper shows that the most probable finite stochastic program to model a finite history is finitely computable, and that there is an agent that makes such a computation without any unintended instrumental actions.

Panel 1: Can We Determine the Top Unresolved Problems of Visualization?

Many of us working in visualization have our own list of our top 5 or 10 unresolved problems in visualization. We have assembled a group of panelists to debate and perhaps reach concensus on the top problems in visualization that still need to be explored. We include panelists from both the information and scientific visualization domains. After our presentations, we encourage interaction with the audience to see if we can further formulate and perhaps finalize our list of top unresolved problems in visualization.

Measuring agent intelligence via hierarchies of environments

Under Leggs and Hutters formal measure [1], performance in easy environments counts more toward an agents intelligence than does performance in difficult environments. An alternate measure of intelligence is proposed based on a hierarchy of sets of increasingly difficult environments, in a reinforcement learning framework. An agents intelligence is measured as the ordinal of the most difficult set of environments it can pass. This measure is defined in both Turing machine and finite state machine models of computing. In the finite model the measure includes the number of time steps required to pass the test.

Confessions of a visualization skeptic

There is no doubt that visualization is very useful, by enabling people to understand that masses of data and information otherwise hidden inside of computers. However, after many years developing visualization systems I have to confess to skepticism about many of the hottest (i.e., coolest) visualization ideas.

Visualization in earth system science

Traditionally, climate is defined as the statistical collective of the weather conditions of a specified area during a specified interval of time, usually several decades. This definition is currently undergoing a change to place more emphasis on the exchange of energy, momentum, and mass between the different compartments of the Earth System. Although weather is experienced as a pure atmospheric phenomenon with high temporal variability, the long-term changes of mean weather conditions are driven by the dynamics of slowly changing components of the climate system: e.g. the ocean, sea and land ice, and the biosphere.

The top five problems that motivated my work [data visualisation]

Theresa-Marie Rhyne has lately been organizing efforts for a number of researchers to produce lists of top visualization problems. This visualization viewpoints column is a look back at the top five problems that drove our work developing Vis5D, Cave5D, and VisAD. Some of these problems are high-minded and some are grubby and gritty.