CP 2019 solicits papers promoting applications based on CP technologies. We especially look for industrial and academic users of constraint technology to submit papers on completed or ongoing practical projects. Papers comparing constraint technology to other optimization techniques (e.g., MIP, SAT, local search…) with a sound empirical evaluation are equally welcome.
The ideal paper will clearly define the application or real-life problem, and if applicable mention if the application is deployed in practice. Expectations for these papers are:
- A clear description of the problem and the way how CP technology was used to solve the problem.
- Quantifiable results, or qualitative impact such as improved maintainability or simpler process integration, including an analysis of the contribution that can be attributed to constraints technology.
While not strictly mandatory, novelty of the application domain or the use of CP technology by itself or within hybrids is a plus and any detail to favor the reproducibility of the experiments is also a plus (open-source code, shared benchmarks, instance generator, etc.)
A non-exhaustive list of possible application domains are:
- transportation, logistics and packing;
- scheduling, rostering and planning;
- computer networks, security and cryptography;
- configuration; and
- design, music or art.
Track: Computational Sustainability
Computational Sustainability aims to apply techniques from computing, information science, and related disciplines (e.g., operations research, applied mathematics, and statistics), to balancing environmental, economic, and societal needs for sustainable development. Sustainability domains and areas include:
- Smart cities (e.g., sensor networks, energy efficient buildings, urban infrastructure, urban flows, traffic management, participatory and direct democracy, etc.);
- Human-Built Systems and Land Use (e.g., transportation systems, data-centres, smart grid, food systems, agriculture, etc.);
- Natural resources and ecosystems (e.g., climate, atmosphere, water, oceans, forest, land, soil, biodiversity, species, etc.);
- Economics and Human Behavior (e.g., human well-being, poverty, infectious diseases, over-population, resource harvesting, etc.);
- Energy resources (e.g., renewable energy, energy management and planning, energy market etc.).
This thematic track invites the submission of research papers on novel computational concepts, models, techniques, and systems to address problems in computational sustainability. Papers should describe computational sustainability research, or explain how the research addresses problems, opportunities or issues underlying computational sustainability challenges, or describe a computational sustainability challenge or application. Papers on challenges in computational sustainability are also welcome.
Track: CP and Life Sciences
During the last two decades, Biology has become a challenging source of problems for Computer Science in general, and for the areas of computational logic and constraint programming in particular. Computational biology can be defined as the study of mathematical and computational problems related to modeling biological processes, designing and controlling experiments, and interpreting the data. Successful approaches to these problems are having significant impact in biology, medicine and environmental sciences.
The Constraint Programming paradigm, in a broad sense, has been applied to a variety of relevant issues ranging from molecular and cell biology to networks structures and dynamics, evolution and ecosystems. This research calls upon bioinformatics (biomolecule sequences, 3D structure), systems biology (regulatory or metabolic networks, population dynamics), computational neuroscience (neurons and networks), bio-image analysis, and experiment design and automation.
This track aims at exchanging ideas between researchers and exploring new techniques and applications for CP in Life Sciences. The topics of interest are all those concerning the treatment by constraint related techniques (CP / Linear Programming / SAT / ASP / Logic Programming, etc.) of problems relevant to biology, medicine, health and the environment, for instance:
- DNA sequence assembly
- multiple sequence alignment
- RNA prediction and motif search
- protein structure and functional prediction
- genetic linkage analysis and haplotype inference
- genomic selection design
- gene regulatory network inference and analysis
- biochemical regulatory and metabolic network simulation, analysis and visualisation
- constraint databases and ontologies in bioinformatics
- web bioinformatics services
- image analysis
- constraint-based machine learning for biological data
- experiment design and automation
Track: CP, Data Sciences and Machine Learning
In the last decade, advances in Data Science methods have seen dramatic improvements in their effectiveness, leading to widespread attention and successful real-world applications. Despite this, there is a limit to how much can be done using learning methods alone. There is a growing recognition that finding effective and efficient approaches to synergize learning and reasoning may enable the solution of far more complex and important problems, and improve the way we solve existing ones.
This thematic track will consider submissions covering both directions of integration. This includes papers exploring the use of constraint solving technology to address data mining tasks, such as:
- constraint-based pattern mining
- constraint-based clustering
- structure learning using solvers
- structured preference elicitation
- neural combinatorial search
as well as papers addressing the use of Machine Learning and Data Science to improve or support constraint reasoning/modeling, for example via:
- algorithm selection and configuration
- automated problem solving through (meta) learning
- learning constraints and preferences
- learning search strategies incl variable/value ordering
- reinforcement learning in constraint solving.
Overall, the track aims to invite research that sits at the border between constraint solving and data science, with the broadest possible scope.
Track: Multi-agent and Parallel CP
The presence of software agents is increasingly prevalent in our society, stimulated by emerging applications powered by the internet of things, smart energy, and novel mobility models. Often, such agents need to cooperate in a distributed manner to solve combinatorial problems that can be formulated as constraint-based models. Further, constraint programming offer unprecedented opportunities for parallelism to speed up execution. The continuous development of novel architectures (e.g., GPU-based computing) and novel meta-search strategies (e.g., those used in portfolio solvers) create new research opportunities, fueling the development of new constraint technologies.
This thematic track solicits papers addressing original research in the intersection of constraint programming and autonomous agents and their interactions as well as parallel methodologies for constraint-based solutions.
The track topics of interests include:
- Portfolio solvers
- Parallel search and propagation solutions
- Parallel multi-agent solutions
- Cloud computing for constrained solutions
- GPU approaches to constraint (logic) technologies
- Applications of parallel CP/COP/SAT/ASP
- Distributed problem solving
- Teamwork, coalition formation
- Agents’ preferences and preference elicitation
- Single and multi-agent planning and scheduling
- Agent cooperation for practical applications
- Cooperative or non-cooperative games
- Game theory for practical applications
Track: Testing and Verification
>The last decade has witnessed a considerable improvement in the efficiency and expressive power of CP solvers, with a consequent impact on (software and hardware) testing and verification application. A deeper integration of solvers and applications is expected with on going research on Constraint Programming (CP) techniques. The Testing and Verification track of CP'2019 will focus on a broad range of topics, without being limited to the ones mentioned below:
- Constraint-based hardware verification
- Constraint-based software testing
- Constraints in formal verification
- Constraints in static and dynamic analysis
- CP solvers for testing applications
- Verification of CP models
- Testing of CP solvers
- Formal verification of CP solvers and optimizers
- Automatic test generation with CP solvers