Preface to special issue on planning and scheduling

Planning, scheduling and constraint satisfaction are important areas in artificial intelligence (AI)
with broad practical applicability. Many real-world problems can be formulated as AI planning
and scheduling (P&S) problems, where resources must be allocated to optimize overall performance
objectives. Frequently, solving these problems requires an adequate mixture of planning,
scheduling and resource allocation to competing goal activities over time in the presence of
complex state-dependent constraints. Constraint satisfaction plays an important role in solving
such real-life problems, and integrated techniques that manage P&S with constraint satisfaction
are particularly useful. Knowledge engineering supports the solution of such problems by providing
adequate modelling techniques and knowledge extraction techniques for improving the
performance of planners and schedulers. Briefly speaking, knowledge engineering tools serve as a
bridge between the real world and P&S systems.