knowledge (for example, a similarity measure), and
the adaptation knowledge. CBR is an active field of
research that is application- and theory-driven, and it
relates to both machine learning and knowledge representation.
Each day of the conference began with an invited
talk. On the first day, Henri Prade presented an introduction to analogical proportions and analogical reasoning in a talk matching the focal topic of the conference. It was noted that analogy is as much a
matter of dissimilarity as it is a matter of similarity.
The next day, Agnar Aamodt and Enric Plaza, the
inventors of the CBR cycle, presented a historical
view of CBR within AI. The historical view was followed by future challenges of AI and the role CBR
could play in answering them. In her talk the last day
of the conference, Mary Lou Maher described computational models of novelty and surprise in CBR as
a tool to encourage user curiosity. Novelty and surprise contrast with the notion of similarity that is so
important in CBR systems to identify solutions of
past problems when solving new problems.
The papers selected by the peer-review process
were presented during six oral sessions and a poster
session and represent CBR in all its diversity. The
poster session included two-minute pitches in front
of the plenum as well as a poster quiz, where all the
participants were encouraged to answer questions
about each poster. The poster quiz not only encouraged attendance, but also guided attendees through
all the posters and facilitated discussions with the
authors of those posters. The winner of the quiz won
a treasured prize: an actual paper copy of the proceedings. The topics of the oral sessions were cased-based recommendations; graph representations for
CBR; CBR and time series; CBR and machine learning; efficient CBR; and textual CBR. Four shorter talks
given by industry representatives broadened the
scope of the conference, allowing the industry representatives to introduce relevant problems and practical work in AI and machine learning. The paper
Running with Cases: A CBR Approach to Running Your Best
Marathon won the Best Paper award. Congratulations
to Barry Smyth and Pádraig Cunningham, who predicted challenging, but achievable, personal best race
times for marathon runners, as well as race plans to
The satellite events were mostly held on the afternoons of June 25th and 26th. These included workshops on CBR and deep learning, computational
analogy, and process-oriented CBR; the doctorial
consortium, which hosted nine students and their
mentors; the Computer Cooking Contest, which
focused on recipe generation and adaptation; and the
first CBR video competition.
Visit the ICCBR-2017 website to view the videos.
The proceedings of the different events are available
online. 2 Although no workshop was held in 2017 on
CBR in the health sciences, several papers related to
this topic were presented at the main conference.
ICCBR is not only an important venue for pre-
senting CBR-related research. It is also an important
event to build and maintain the CBR community.
Generous funding from NTNU, the Norwegian
Research Council, and our other sponsors allowed
the conference to cover all the meals for the atten-
dees during the conference. Good dinners at local
restaurants ensured that each day’s program could be
discussed over a nice meal. The social program also
included a guided tour of the city, a boat trip, and a
walking trip along the fjord in nice sunny weather.
The local and program chairs of the conference are
grateful to everyone who made ICCBR-2017 a successful event: the Advisory and Program Committees, the organizers of the satellite events, the invited speakers, and all the participants. We invite
everyone in the AI community to contribute, participate, and attend ICCBR-2018, 3 which will be held in
Stockholm, July 10–12, 2018.
David W. Aha leads the Adaptive Systems section at the
Naval Research Laboratory’s AI Center. His research focuses
on intelligent agents and machine learning, with emphases
on goal reasoning and case-based reasoning.
Kerstin Bach is an associate professor in computer science
at the Norwegian University of Science and Technology
(NTNU), where she is a scientific co-coordinator of the AI
Lab. Her research areas are intelligent decision support systems focusing on case-based reasoning and machine learning methods for personalized health applications.
Odd Erik Gundersen is leading the AI and machine learning effort at TrønderEnergi and is also an adjunct associate
professor at the Norwegian University of Science and Technology (NTNU). His research focuses on situation and context aware systems, as well as the reproducability of
machine learning research.
Jean Lieber is an assistant professor in computer science at
the Université de Lorraine (France) whose research focuses
on case-based reasoning with an emphasis on knowledge
representation and case adaptation.