Aims and scope

Many problems in Artificial Intelligence show an exponential explosion of the search space. Although stemming from different research areas in AI, such problems are often addressed with algorithms that have a common goal: the effective exploration of huge state spaces. Many algorithms developed in one research area are applicable to other problems, or can be hybridised with techniques in other areas. Artificial Intelligence tools often exploit or hybridise techniques developed by other research communities, such as Operations Research. 

In recent years, research in AI has more and more focussed on experimental evaluation of algorithms, the development of suitable methodologies for experimentation and analysis, the study of languages and the implementation of systems for the definition and solution of problems.

Scope of the workshop is fostering the cross-fertilisation of ideas stemming from different areas, proposing benchmarks for new challenging problems, comparing models and algorithms from an experimental viewpoint, and, in general, comparing different approaches with respect to efficiency, problem modelling, and ease of development.

Topics of interest include, but are not limited to:

• Experimental evaluation of algorithms for
  
  • knowledge representation
  • automated reasoning
  • planning
  • scheduling
  • machine learning
  • model checking
  • Boolean satisfiability (SAT)
  • constraint programming
  • temporal reasoning
  • combinatorial optimization
  • quantified Boolean formulae and quantified constraints
  • modal logics
  • logic programming
  • answer set programming
  • ontological reasoning
    
• Definition and construction of benchmarks
• Experimentation methodologies
• Metaheuristics
• Algorithm hybridization
• Static analysis of combinatorial problems
• Languages and systems for definition and solution of problems
• Comparisons between systems and algorithms
• Application experiences  (visualization, graphics, security, transports, etc.)