Automated Planning is the area of Artificial Intelligence that deals with problems in which we are interested in finding a sequence of steps (actions) to apply to the world in order to achieve a set of predefined objectives (goals) starting from a given initial state. In the past, planning has been successfully applied in numerous areas including robotics, space exploration, transportation logistics, marketing and finance, assembling parts, crisis management, etc. The history of Automated Planning goes back to the early 1960s with the General Problem Solver (GPS) being the first automated planner reported in literature. Since then, it has been an active research field with a large number of institutes and researchers working on the area. Traditionally, planning has been seen as an extension of problem solving and it has been attacked using adaptations of the classical search algorithms. The methods utilized by systems in the ?classical? planning era (until mid-1990s), include state-space or plan-space search, hierarchical decomposition, heuristic and various other techniques developed ad-hoc. The classical approaches in Automated Planning presented over the past years were assessed on toy-problems, such as the ones used in the International Planning Competitions, that simulate real world situations but with too many assumptions and simplifications. In order to deal with real world problems, a planner must be able to reason about time and resources, support more expressive knowledge representations, plan in dynamic environments, evolve using past experience, co-operate with other planners, etc. Although the above issues are crucial for the future of Automated Planning, they have been recently introduced to the planning community as active research directions. However, most of them are also the subject of researchers in other AI areas, such as Constraint Programming, Knowledge Systems, Machine Learning, Intelligent Agents and others, and therefore the ideal way is to utilize the effort already put into them. This edited volume, Intelligent Techniques for Planning, consists of 10 chapters bringing together a number of modern approaches in the area of Automated Planning. These approaches combine methods from classical planning, such as the construction of graphs and the use of domain-independent heuristics, with techniques from other areas of Artificial Intelligence. The book presents in detail a number of state-of-the-art planning systems that utilize Constraint Satisfaction Techniques in order to deal with time and resources, Machine Learning in order to utilize experience drawn from past runs, methods from Knowledge Representation and Reasoning for more expressive representation of knowledge, and ideas from other areas, such as Intelligent Agents. Apart from the thorough analysis and implementation details, each chapter of the book also provides extensive background information about its subject and presents and comments on similar approaches done in the past.
