Over the years increasingly sophisticated planning algorithms have been developed. These have made for more efficient planners, but unfortunately these planners still suffer from combinatorial complexity even in simple domains. Theoretical results demonstrate that planning is in the worst case intractable. Nevertheless, planning in particular domains can often be made tractable by utilizing additional domain structure. In fact, it has long been acknowledged that domain independent planners need domain dependent information to help them plan effectively. In this

We describe an extension of graphplan to a subset of ADL that allows conditional and universally quantified effects in operators in such away that almost all interesting properties of the original graphplan algorithm are preserved.

Abstract. Flexible support for crisis management can definitely be improved by making use of advanced planning capabilities. However, the complexity of the underlying domain often causes intractable efforts in modeling the domain as well as a huge search space to be explored by the system. A way to overcome these problems is to impose a structure not only according to tasks but also according to relationships between and properties of the objects involved, thereby using so-called decomposition axioms. We outline the prototype of a system that is capable of tackling planning for complex application domains. It is based on a well-founded combination of action and state abstractions. The paper presents the basic techniques and provides a formal semantic foundation of the approach. It introduces the planning system and illustrates its underlying principles by examples taken from the crisis management domain used in our ongoing project. 1

We present a systematic approach to the modular and incremental construction of provably consistent domain models for planning. It is based on a temporal logic framework that in particular allows for the introduction of abstract datatypes which can usefully be applied to mirror the computational aspects of planning problems and their solutions. A formal concept is developed that enables the construction as well as the modification of already existing domain models in a sound manner. This includes well-defined operations for the union, extension, and refinement of domain models.

Many real-world planning applications have to deal with resource allocation problems, and so does planning in the domain of crisis management assistance. In order to support resource allocation in these kind of applications, we present a new approach to the integration of scheduling capabilities and planning. The proposed methodology relies on a hybrid planner, which combines action and state abstraction by integrating hierarchical task network (HTN) planning and state based partial order causal link (POCL) planning into a common framework. We extend the abstraction mechanism of the planner to different kinds of abstraction for resources, namely subsumption, approximation, qualification, and aggregation.

In a previous work, we had analyzed the gaps in the prevalent approaches (i.e., Semantic Web Services and WSDLdescribed Web Services) for the problems of modeling, composing, executing, and verifying Web services, and derived challenges for the AI planning community. The challenges were in representation of complex actions, handling of richly typed messages, dynamic object creation and specification of multi-partner interactions. An important question that constantly arose was how the goals for automatic composition would be derived. In this paper, we revisit this issue in the light of new trends in software engineering towards Model Driven Architecture and early deployment of Web service composition solutions. We argue that Web services composition can not be seen as a one-shot plan synthesis problem defined with explicit goals but rather as a continual process of manipulating complex workflows, which requires to solve synthesis, execution, optimization, and maintenance problems as goals get incrementally refined. We then identify additional issues that become important in applying planning techniques.

In this paper, we introduce a domain modeling tool that supports users in the incremental and modular development of verified models of planning domains. It relies on a logic-based concept for systematic domain model construction that provides well-defined, safe operations for the union, extension, and refinement of already existing models. The system is equiped with a deductive component. It automatically performs the proofs necessary to guarantee both the consistency of single models and the safety of operations on models. By means of detailed examples, it is shown how the system has been used for the structured development of a model for a complex, safety-critical planning domain. 1 Introduction As soon as we aim at using planning systems in the context of realistic applications, the task of generating the underlying domain model becomes increasingly crucial. It is not only difficult to overlook the great amount of object types, relations, and actions involved when specifying such...

Many problems addressed within the field of Cognitive Robotics and related areas can only be solved by complex plans including conditional and recursive actions as well as non--deterministic choice operators. In this paper we present a planning language which allows for the specification of complex plans. We define its semantics and give a provably complete and correct completed equational logic program with an unification complete equational theory. The approach is independent of the representation of states; they may be sets of propositional fluents as in the situation calculus or multisets of resources as in the fluent calculus. Finally, we give an instantiation within the fluent calculus. 1

The search for finite models of temporal logic specifications furnishes a logical, though non-deductive, approch to planning, that opens the way to the exploitation of methodologies and techniques from classical search-based approaches, such as plan-space search, regression and least commitment. In this paper a tableau based decision procedure for finite satisfiability of linear temporal formulae is presented. The method is based on prefixed tableaux, where a partial order on prefixes is built in each branch step by step. A form of soundness and completeness w.r.t. finite model construction are established. Finally, some constraints on tableau construction are established, ensuring that the tableau procedure always terminates and returns a finite model of the initial set of formulae, if there exists one. 2 1 Introduction Model construction in temporal logic is the main concern of executable temporal logics. They are based on an imperative reading of logic, where executing a tempora...

In this work we propose a logical approach to planning, based on the view of a planning activity as the search for a finite model of the specification of the planning problem. Planning problems are represented in a modal temporal language, based on a discrete, linear model of time, whose execution mechanism exploits model construction techniques related to analytic tableaux. The tableau system is based on the use of prefixes that, intuitively, label each formula as being true in a particular state. This approach allows the exploitation of methods and results from search-based approaches, such as plan-space search, regression and least commitment. A partial ordering of time points is in fact allowed in the calculus, by means of a set of temporal constraints that is built up step by step as the construction of a tableau branch proceeds. A form of soundness and completeness with respect to finite model construction are established, constituting the grounds for a decision procedure that is parametric w.r.t. the maximal time length a plan is allowed to reach.