StaRAI 2012 - 2nd International Workshop on Statistical Relational AI
Topics/Call fo Papers
Much has been achieved in the field of AI, yet much remains to be done
if we are to reach the goals we all imagine. One of the key challenges
with moving ahead is closing the gap between logical and statistical
AI. Logical AI has mainly focused on complex representations, and
statistical AI on uncertainty. Intelligent agents, however, must be
able to handle both the complexity and the uncertainty of the real
world.
Recent years have seen an explosion of successes in combining
probability and (subsets of) first-order logic respectively
programming languages and databases in several subfields of AI:
Reasoning, Learning, Knowledge Representation, Planning, Databases,
NLP, Robotics, Vision, etc. Nowadays, we can learn probabilistic
relational models automatically from millions of inter-related
objects. We can generate optimal plans and learn to act optimally in
uncertain environments involving millions of objects and relations
among them. Exploiting shared factors can speed up message-passing
algorithms for relational inference but also for classical
propositional inference such as solving SAT problems. We can even
perform lifted probabilistic inference avoiding explicit state
enumeration by manipulating first-order state representations
directly.
So far, however, the researchers combining logic and probability in
each of these subfields have been working mostly independently. We
believe the current situation actually provides us with an opportunity
for attempts at synthesis, forming a common core of problems and
ideas, and cross-pollinating across subareas. We would like to explore
the minimal perturbations required for each of the AI subfields to
start using statistical relational (SR) techniques.
The goal of the StarAI workshop is to reach out to the general field
of AI and to explore what might be called Statistical Relational AI.
We seek to invite researchers in all subfields of AI to attend the
workshop and to explore together how to reach the goals imagined by
the early AI pioneers.
Submission
We anticipate a one-day workshop with about 40 participants, position
and paper statements, three invited speakers, and a panel discussion.
Those interested in attending should submit either a technical paper
(UAI style, 8 pages maximum) or a position statement (UAI style, 3
pages maximum) in PDF format. Submissions through E-Mail will not be
accepted. All submitted papers will be carefully peer-reviewed by
multiple reviewers and low-quality or off-topic papers will not be
accepted.
The papers will be selected either for a short oral presentation or a
poster presentation. The workshop will include a 2 hour poster session
to allow enough time for the attendees to interact with each other.
For more details, please refer to
http://tsi.wfubmc.edu/labs/strait/StaRAI/starai.ht...
Paper Submission June 10th, 2012
Notification of Acceptance June 30, 2012
Camera-Ready Papers due July 15, 2012
Date of Workshop 18 Aug 2012
Henry Kautz, Kristian Kersting, Sriraam Natarajan and David Poole.
if we are to reach the goals we all imagine. One of the key challenges
with moving ahead is closing the gap between logical and statistical
AI. Logical AI has mainly focused on complex representations, and
statistical AI on uncertainty. Intelligent agents, however, must be
able to handle both the complexity and the uncertainty of the real
world.
Recent years have seen an explosion of successes in combining
probability and (subsets of) first-order logic respectively
programming languages and databases in several subfields of AI:
Reasoning, Learning, Knowledge Representation, Planning, Databases,
NLP, Robotics, Vision, etc. Nowadays, we can learn probabilistic
relational models automatically from millions of inter-related
objects. We can generate optimal plans and learn to act optimally in
uncertain environments involving millions of objects and relations
among them. Exploiting shared factors can speed up message-passing
algorithms for relational inference but also for classical
propositional inference such as solving SAT problems. We can even
perform lifted probabilistic inference avoiding explicit state
enumeration by manipulating first-order state representations
directly.
So far, however, the researchers combining logic and probability in
each of these subfields have been working mostly independently. We
believe the current situation actually provides us with an opportunity
for attempts at synthesis, forming a common core of problems and
ideas, and cross-pollinating across subareas. We would like to explore
the minimal perturbations required for each of the AI subfields to
start using statistical relational (SR) techniques.
The goal of the StarAI workshop is to reach out to the general field
of AI and to explore what might be called Statistical Relational AI.
We seek to invite researchers in all subfields of AI to attend the
workshop and to explore together how to reach the goals imagined by
the early AI pioneers.
Submission
We anticipate a one-day workshop with about 40 participants, position
and paper statements, three invited speakers, and a panel discussion.
Those interested in attending should submit either a technical paper
(UAI style, 8 pages maximum) or a position statement (UAI style, 3
pages maximum) in PDF format. Submissions through E-Mail will not be
accepted. All submitted papers will be carefully peer-reviewed by
multiple reviewers and low-quality or off-topic papers will not be
accepted.
The papers will be selected either for a short oral presentation or a
poster presentation. The workshop will include a 2 hour poster session
to allow enough time for the attendees to interact with each other.
For more details, please refer to
http://tsi.wfubmc.edu/labs/strait/StaRAI/starai.ht...
Paper Submission June 10th, 2012
Notification of Acceptance June 30, 2012
Camera-Ready Papers due July 15, 2012
Date of Workshop 18 Aug 2012
Henry Kautz, Kristian Kersting, Sriraam Natarajan and David Poole.
Other CFPs
Last modified: 2012-06-02 09:36:57