Motivation | Proceedings | Program | Topics | Submissions| Dates | Committee
Despite recent attempts in various subareas of AI to integrate technologies to solve complex problems such as autonomous cars, there are still gaps between research communities that prevent efficient transfer of knowledge. For example, knowledge representation techniques focus on formal semantics and flexibility of modeling frameworks and put less emphasis on actual problem solving that requires efficient tools. Other communities such as planning and search put emphasis on efficiency of problem solving, but less attention is given to how the real problem is modeled, the connection between modeling and efficiency of problem solving, and the capability of the models to support other important features like plan revision and adaptation. This workshop attempts to bridge these particular communities with the goal to exchange information leading to more efficient problem solving starting with the the problem requirements and finishing with the solved problem.
All the papers are available for download separately from the workshop program below. The complete proceedings are published as Vol-1648 in CEUR Workshop Proceedings.
Workshop Program :
|09:20 – 09:30
||Welcome and opening notes
|09:30 – 10:30
||The Modelling Beauty of Constraint Solving (keynote) [PDF]
|10:30 – 11:00
|11:00 – 12:15
||Grammar Induction as Automated Transformation between Constraint Solving Models of Language [PDF]
Ife Adebara and Veronica Dahl
|Multiple-Origin-Multiple-Destination Path Finding with Minimal Arc Usage: Complexity and Models [PDF]
Roman Barták, Agostino Dovier, and Neng-Fa Zhou
|On Structural Properties to Improve FMEA-Based Abductive Diagnosis [PDF]
Roxane Koitz and Franz Wotawa
|12:15 – 14:15
||Lunch break (on your own)
|14:15 – 15:30
||Using Knowledge Representation and Reasoning Tools in the Design of Robots [PDF]
Mohan Sridharan and Michael Gelfond
|Decomposing Minimal Models [PDF]
Rachel Ben-Eliyahu-Zohary, Fabrizio Angiulli, Fabio Fassetti, and Luigi Palopoli
|Belief State Estimation for Planning via Approximate Logical Filtering and Smoothing [PDF]
Brent Mombourquette, Christian Muise, and Sheila McIlraith
|15:30 – 16:00
|16:00 – 17:40
||Assumption-Based Planning with Sensing via Contingent Planning [PDF]
Pamela Calvo and Jorge Baier
|Non-Deterministic Planning with Temporally Extended Goals: Completing the story for finite and infinite LTL (Amended Version) [PDF]
Alberto Camacho, Eleni Triantafillou, Christian Muise, Jorge Baier, and Sheila McIlraith
|Strong-Cyclic Planning when Fairness is Not a Valid Assumption [PDF]
Alberto Camacho and Sheila McIlraith
|Numeric Planning via Search Space Abstraction [PDF]
León Illanes and Sheila McIlraith
Formal problem modeling is a critical step during problem solving. A good modeling framework should be flexible enough to describe important properties of problems solved and should allow application of efficient problem solving techniques. This workshop attracts papers at the frontier between formal problem modeling and problem solving. Papers should see the formal models from the perspective of problem solving and vice versa — problem solving techniques are seen in relation to models of the problem. For example, the paper can discuss the relation between planning domain models and planning algorithms or show how to enhance the domain model by extra information such as control knowledge. Papers discussing methods on how to obtain information that is useful for efficient problem solving are welcome.
We are in particular interested in papers addressing some of the following questions. How do the formal models relate to efficiency of problem solving? How do various modeling frameworks compare from the perspective of problem solving? How can the model be acquired? How can the model be verified and validated? How can the formal model be reformulated to get an efficiently-solvable model? How can the solution be checked with respect to the model? How does the model evolve in time? How can the model support solution revisions at execution time?
Application papers are also welcome, if they highlight the relation between the formal model of the problem and the solving approach. Description of specific models for specific problems is also possible, if the particular modeling techniques are studied from the perspective of problem solving.
Possible topics of papers:
- Modeling approaches (problem modeling, knowledge engineering)
- Formalisms to describe (real-life) problems
- Languages for problem description
- Relations between modeling and solving
- Automated transformations between formal models
- Problem re-formulation
- Formats for specification of heuristics, parameters and control knowledge for solvers
- Validation of models and solutions
- Visualization of models
- Automated model acquisition
- Tools and applications
- Examples of particular modeling techniques
Submitted papers must be formatted according to IJCAI guidelines and submitted electronically through the KnowProS 2016 paper submission site. Authors are required to submit their electronic papers in PDF format. Submitted technical papers should be no longer than six pages in total (if the length of your paper is longer, please contact the organizers prior submission). Include your names and affiliations (but no page numbers) in the submission.
- Formatting Guidelines, LaTeX Styles and Word Template can be downloaded here.
- Submission Site: https://easychair.org/conferences/?conf=knowpros2016.
At least one author of each accepted paper is required to attend the workshop to present the work.