This paper reports on experiments where techniques of supervised machine learning are applied to the problem of planning. The input to the learning algorithm is composed of a description of a planning domain, planning problems in this domain, and solutions for them. The output is an eecient algorithm | a strategy | for solving problems in that domain. We test the strategy on an independent set ...

Erklärung Hiermit versichere ich, dass ich die vorliegende Arbeit selbstständig und nur unter Verwendung der angegebenen Hilfsmittel angefertigt habe.

Planning has been found to have a powerful effect on human actions (e.g., Gollwitzer & Sheeran, 2006 ). But how do people plan? In this chapter we fi rst introduce implementation intentions (e.g., Gollwitzer, 1999 ) as an effi cient way of planning. Implementation intentions refer to specifi c plans in which individuals and groups can specify when, where, and how they intend to act using an if-...

The field of Artificial Intelligence (AI) has become extremely prominent in recent times, with the integration of different intelligent components into devices and services we use in everyday life. As the capabilities of such systems become more and more complex, one branch of AI that becomes relevant is that of automated planning or sequential decision making, in order for these components to ...

One of the oldest dreams in the research area of Artificial Intelligence is the design of autonomous robots with problem-solving ability comparable to that of humans. Beginning of the 70's was the Stanford Research Institute Planning System STRIPS (Fikes & Nilsson, 1971). Its formalism for the description of action planning domains is fundamental. STRIPS is motivated by mapping plans to robots,...

The action language Golog allows specifying the behavior of autonomous systems with very flexible programs that leave certain aspects open to be resolved by the system. Such open aspects are often planning tasks, where the system needs to find a suitable course of actions to reach a given goal. The first part of this thesis aims to make highly efficient planning systems available to the Golog s...

AI planning has become more and more important in many real-world domains such as military applications and intelligent scheduling. However, planning systems require complete specifications of domain models, which can be difficult to encode, even for domain experts. Thus, research on effective and efficient methods to construct domain models or applicability conditions for planning automaticall...

Most AI planners work on the assumption that they have complete knowledge of their problem domain and situation so that planning an action consists of searching for an action sequence that achieves some desired goal. In actual planning situations, we rarely know enough to map out a detailed plan of action when we start out. Instead, we initially draw up a sketchy plan and fill in details as we ...

AI planning algorithms have addressed the problem of generating sequences of operators that achieve some input goal, usually assuming that the planning agent has perfect control over and information about the world. Relaxing these assumptions requires an extension to the action representation that allows reasoning both about the changes an action makes and the information it provides. This pape...

AbstrAct This chapter introduces planning and knowledge representation in the declarative action language K. Rooted in the area of Knowledge Representation & Reasoning, action languages like K allow the formalization of complex planning problems involving non-determinism and incomplete knowledge in a very flexible manner. By giving an overview of existing planning languages and comparing these ...