KnowProS 2016 - Knowledge-based techniques for problem solving and reasoning (KnowProS 2016)

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.
Workshop topics:
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
Abstraction
Ontologies
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