| The Intelligent Techniques for Planning presents a number of modern approaches to 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 systems 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 similar approaches done in the past.
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. |
