This track invites submissions of papers on emerging and deployed applications of LP, describing all aspects of the development, deployment, and evaluation of logic programming systems to solve real-world problems, including interesting case studies and benchmarks, and discussing lessons learned.
The goal of this track is to provide a fresh impulse for the LP community to recast its interests towards solving practical problems and applications, and to attract representatives from the wider academia and industrial communities to discuss their challenges related to using LP in practical problems, applications and industrial products, and their expectations from the development of theory and tools from the LP community.
We welcome LP applications in a wide range of areas, including but not limited to:
- stream reasoning
- composite event recognition
- industrial applications
- commonsense reasoning, knowledge representation
- declarative problem solving
- bioinformatics, computational biology
- life sciences, genetics, medicine, pharmacology
- cognitive robotics, social robotics, human-robot interactions
- intelligent transportation, logistics, maritime situational awareness
- computer vision, sensing, internet of things
- data analysis, machine learning
- creative computing
- digital forensics, cybersecurity, blockchain
- economics, game theory, social choice
- software engineering, intelligent user interfaces
- multi-agent systems, argumentation, epistemic reasoning
- constraint programming, SAT, SMT
- natural language understanding, story telling, question answering
- explanation generation, diagnosis
- spatial/temporal/probabilistic reasoning
- planning and scheduling
- databases, ontologies, knowledge bases, Semantic Web
In addition to the usual evaluation criteria concerning the quality of the presentation, for the Applications track the criteria will include:
- Significance of the real-world problem being addressed.
- Importance and novelty of using LP technologies to solve this problem.
- Evaluation and applicability of the system in the real world.
- Clear evidence of the potential benefits of applying and improving LP tools and techniques.
- Reproducibility of empirical analysis; reusability of datasets, case studies, knowledge repositories and benchmarks.