of the decision tree machine-learning techniques in
SkiCat (Fayyad, Weir, and Djorgovski 1993) to semiautomatically classify the second Mount Palomar Sky
Survey, enabling classification of an order of magnitude greater sky objects than by manual means.
Another early advance was the use of Bayesian clustering in the AutoclassAutoClass system (Cheeseman
and Stutz 1996) to classify infrared astronomical
satellite (IRAS) data. From these beginnings has
emerged a plethora of subsequent applications
including automatic classification and detection of
features of interest in earth (Mazzoni et al. 2007a,
2007b) and planetary (Burl et al. 1998, Wagstaff et al.
2012) remote-sensing imagery. More recently, these
techniques are also being applied to radio science signal interpretation (Thompson et al. 2013a).
In this issue the article by José Martínez Heras and
Alessandro Donati studies the problem of telemetry
monitoring and describes a system for anomaly
detection that has been deployed on several European Space Agency (ESA) missions.
Surface missions, such as Mars Pathfinder, Mars
Exploration Rovers (MER), and the Mars Science Laboratory (MSL), also present a unique opportunity and
challenge for AI. The MER mission uses several AI-related systems: The MAPGEN (Ai-Chang et al. 2004,
Bresina et al. 2005) constraint-based planning system
for tactical activity planning, the WATCH (Castano
et al. 2008) system (used operationally to search for
dust devil activity and to summarize information on
clouds on Mars.), and the AEGIS system (Estlin et al.
2012) (used for end-of-sol targeted remote sensing to
enhance MER science).
Many rover operations, such as long- and short-range traverse on a remote surface; sensing;
approaching an object of interest to place tools in
contact with it; drilling, coring, sampling, assembling structures in space, are characterized by a high
degree of uncertainty resulting from the interaction
with an environment that is at best only partially
known. These factors present unique challenges to AI
In this issue, the article by David Wettergreen,
Greydon Foil, Michael Furlong, and David R. Thompson addresses the use of onboard rover autonomy to
improve the quality of the science data returned
through better sample selection, data validation, and
Another challenge for autonomy is to scale up to
multiple assets. While in an Earth-observing context
multiple satellites are already autonomously coordi-
nated to track volcanoes, wildfires, and flooding
(Chien et al. 2005b, Chien et al. 2012b), these sys-
tems are carefully engineered and coordinate assets
in rigid, predefined patterns. In contrast, in this issue,
the article by Logan Yliniemi, Adrian K. Agogino, and
Kagan Tumer tackles the problem of multirobot coor-
dination for surface exploration through the use of
coordinated reinforcement learning: rather than
being programmed what to do, the rovers iteratively
learn through trial and error to take actions that lead
to high overall system return.
The significant role of AI in space is documented in
three long-standing technical meetings focused on
the use of AI in space. The oldest, the International
Symposium on Artificial Intelligence, Robotics, and
Automation for Space (i-SAIRAS) covers both AI and
robotics. I-SAIRAS occurs roughly every other year
since 1990 and alternates among Asia, North Ameri-
ca, and Europe1 with 12 meetings to date. Second, the
International Workshop on Planning and Scheduling
for Space occurs roughly every other year with the
first meeting2 in 1997 with eight workshops thus far.
Finally, the IJCAI3 workshop on AI and space has
occurred at each IJCAI conference beginning in 2007
with four workshops to date.
We hope that readers will find this introduction
and special issue an intriguing sample of the incredible diversity of AI problems presented by space exploration. The broad spectrum of AI techniques, including but not limited to machine learning and data
mining, automated planning and scheduling, multi-objective optimization, and multiagent, present
tremendously fertile ground for both AI researchers
Portions of this work were carried out by the Jet
Propulsion Laboratory, California Institute of Tech-
nology, under a contract with the National Aeronau-
tics and Space Administration.
1. See robotics.estec.esa.int/i-SAIRAS.
2. See robotics.estec.esa.int/IWPSS.
3. See ijcai.org.
Ai-Chang, M.; Bresina, J.; Charest, L.; Chase, A.; Hsu, J. C.-
J.; Jonsson, Ari; Kanefsky, B.; Morris, P.; Rajan, K.; Yglesias,
J.; Chafin, B. G.; Dias, W. C.; Maldague, P. F. 2004. Mapgen:
Mixed Initiative Planning and Scheduling for the Mars
Exploration Rover Mission. IEEE Intelligent Systems 19( 1): 8–
Bresina, J. L.; Jónsson, Ari K.; Morris, P. H.; and Rajan, K.
2005. Activity Planning for the Mars Exploration Rovers. In
Proceedings of the Fifteenth International Conference on
Automated Planning and Scheduling, 40–50. Menlo Park,
CA: AAAI Press.
Bernard, D.; Dorais, G. A.; Gamble, E.; Kanefsky, B.; Kurien,
J.; Millar, W.; Muscettola, N.; Nayak, P.; Rouquette, N.;
Rajan, K.; Smith, B.; Taylor, W.; and Tung, Y.-W. 2000.
Remote Agent Experiment: Final Report. NASA Technical
Report 20000116204. Moffett Field, CA: NASA Ames
Burl, M. C.; Asker, L.; Smyth, P.; Fayyad, U.; Perona, P.;
Crumpler, L.; and Aubele, J. 1998 Learning to Recognize
Volcanoes on Venus. Machine Learning 30( 2–3): 165–194.