Corin R. Anderson

Relational Markov models and their application to adaptive web navigation

Relational Markov models (RMMs) are a generalization of Markov models where states can be of different types, with each type described by a different set of variables. The domain of each variable can be hierarchically structured, and shrinkage is carried out over the cross product of these hierarchies. RMMs make effective learning possible in domains with very large and heterogeneous state spaces, given only sparse data. We apply them to modeling the behavior of web site users, improving prediction in our PROTEUS architecture for personalizing web sites. We present experiments on an e-commerce and an academic web site showing that RMMs are substantially more accurate than alternative methods, and make good predictions even when applied to previously-unvisited parts of the site.

Scale-dependent reproduction of pen-and-ink illustrations

This paper describes a representation for pen-and-ink illustrations that allows the creation of high-fidelity illustrations at any scale or resolution. We represent a pen-and-ink illustration as a low-resolution grey-scale image, augmented by a set of discontinuity segments, along with a stroke texture. To render an illustration at a particular scale, we first rescale the grey-scale image to the desired size and then hatch the resulting image with pen-and-ink strokes. The main technical contribution of the paper is a new reconstruction algorithm that magnifies the low-resolution image while keeping the resulting image sharp along discontinuities. CR

Autonomous rovers for Mars exploration

The Pathfinder mission demonstrated the potential for robotic Mars exploration but at the same time indicated the need for more robust rover autonomy. Future planned missions call for long traverses over unknown terrain, robust navigation and instrument placement, and reliable operations for extended periods of time. Ultimately, missions may visit multiple science sites in a single day and perform opportunistic science data collection, as well as complex scouting, construction, and maintenance tasks in preparation for an eventual human presence. Significant advances in robust autonomous operations are needed to enable these types of missions. Towards this end, we have designed an on-board executive architecture that incorporates robust flexible operation, resource utilization, and failure recovery. In addition, we have designed ground tools to produce and refine contingent schedules that take advantage of the on-board architectures flexible execution characteristics. Together, the on-board executive and the ground tools constitute an integrated rover autonomy architecture.

The AIPS-98 Planning Competition

In 1998, the international planning community was invited to take part in the first planning competition, hosted by the Artificial Intelligence Planning Systems Conference, to provide a new impetus for empirical evaluation and direct comparison of automatic domain-independent planning systems. This article describes the systems that competed in the event, examines the results, and considers some of the implications for the future of the field.