One day workshop to be held with
2 February, 2018
The program includes Keynote talks, invited presentations and contributed papers .
- Complete schedule posted below.
- Overview slides posted.
The workshop is focused on the problems of Stochastic Planning and Probabilistic Inference and the intimate connections between them.
Both Planning and inference are core tasks in AI and the connections between them have been long recognized. However, much of the work in these subareas is disjoint.
The last decade has seen many exciting developments with explicit constructions and reductions between planning and inference that aim for efficient algorithms for large scale problems and applications. The work in this area is is distributed across many conferences, sub-communities, and sub-topics and varies from discrete to continuous problems, single vs. multi-agent problems, general vs. spatial problems, propositional vs. relational problems, model based planning vs. reinforcement learning, and exact/optimal vs. approximate vs. heuristic solutions. Applications similarly vary for example from scheduling to sustainability and to robot control.
The goal of this workshop is to bring together researchers from all these areas and facilitate synergy and exchange of ideas: to discuss core ideas, techniques and algorithms that take advantage of the connection between planning and inference, identify opportunities and challenges for future work,
and explore applications and how they can inform the development of such work.
The workshop will include invited talks by experts on planning and inference, contributed talks and a poster session, leaving room for discussion and interaction among participants.
The workshop topic is broad and the intention of this first workshop is to enable interaction among the various sub-areas while keeping the focus on the interaction between planning and inference. Some basic questions:
What are effective reductions from planning to inference?
What are effective inference algorithms for such problems?
What are the challenges in planning applications, and how does
their structure help or interfere with the application of planning as
Can generic inference algorithms be used directly for planning? or are we better off tailoring algorithms directly to the planning problem?
Can planning algorithms or ideas developed for them be used for general inference?
How do structured solutions, e.g., lifted inference, lifted planning, spatial MDPs, cooperative multi-agent systems, and approximations in continuous problems, translate across the planning/inference spectrum, and help improve scalability.
Success stories and challenges in using planning for inference or inference for planning.
These questions cut across theoretical foundations and practical applications.
We invite 4 types of submissions (typeset in the AAAI style):
All papers should clearly explain how the work relates planning and inference.
Papers describing current unpublished work (up to 8 pages including references).
Review of mature work (from multiple papers) by the authors
(up to 8 pages including references).
Papers recently published at other venues
(1 page abstract with a link to the full paper).
Position papers (2 pages including references).
Submissions of papers being reviewed for AAAI 2018, or at other venues are welcome since this is a non archival venue (and if published they can be replaced with a 1 page abstract). If such papers are currently under blind review, please anonymize the submission.
Please submit your paper through
Friday, October 13, 2017: Paper submission deadline.
Electronic papers due by 11:59 PM UTC-10 (midnight Hawaii)
Thursday, November 9, 2017: Notifications Sent to Authors
Tuesday, November 21, 2017: Final Workshop Papers Due at AAAI
Workshop date: Friday, February 2, 2018
The program includes keynote talks, invited presentations, and contributed papers. Schedule:
Queries about the workshop should be directed to
- 8:30-10:30 - Session 1
Introductory notes and Workshop overview
8:45- 9:30 - Keynote Talk:
Rina Dechter: On Search solvers for Marginal Map and their applicability to Probabilistic Planning.
9:30- 9:50 -
Generalized Dual Decomposition for Bounding Maximum Expected Utility of Influence Diagrams with Perfect Recall.
Alexander Ihler and
Lifted Stochastic Planning, Belief Propagation and Marginal MAP.
Hao Cui and
R2PG: Risk-Sensitive and Reliable Policy Gradient.
Ji Liu and
- 10:30-11:00 - Coffee Break
- 11:00-12:30 - Session 2
11:00-11:30 - Invited Presentation:
David Wingate Probabilistic Programming for Theory of Mind for Autonomous Agents.
11:30-12:00 - Invited Presentation:
Jan-Willem van de Meent:
Probabilistic programming for planning as inference problems.
12:00-12:30 - Invited Presentation:
Pascal Poupart: Planning as Marginal MAP and Stochastic SAT.
- 12:30-2:00 - Lunch Break (lunch on your own)
- 2:00-3:30 - Session 3
2:00- 2:45 -
Pascal Van Hentenryck: Planning for Energy and Transportation Systems.
(cancelled due to travel delay)
2:45- 3:05 -
Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs.
Yanlin Han and
3:05- 3:25 -
Planning and Learning For Decentralized MDPs With Event Driven Rewards.
Akshat Kumar and
- 3:30-4:00 - Coffee Break
- 4:00-5:30 - Session 4
4:00- 4:45 - Keynote Talk:
Marc Toussaint: Physical Manipulation Planning and Sufficient Symbols.
4:45- 5:15 - Invited Presentation:
Reasoning and Decisions in High Dimensions -- A Unified Approach.
5:15- 5:30 - Conclusion / Discussion.