In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, planetary rover exploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating force behind the progress that has been made in planning since 1998. The third competition (held in 2002) set the planning community the challenge of handling time and numeric resources. This necessitated the development of a modelling language capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, pddl2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that pddl2.1 has considerable modelling power — exceeding the capabilities of current planning technology — and presents a number of important challenges to the research community.

O-Plan is an AI planner based on previous experience with the Nonlin planner and its derivatives. Nonlin and other similar planning systems had limited control architectures and were only partially successful at limiting their search spaces. O-Plan is a design and implementation of a more flexible system aimed at supporting planning research and development, opening up new planning methods and supporting strong search control heuristics. O-Plan takes an engineering approach to the construction of an efficient domain independent planning system which includes a mixture of AI and numerical techniques from Operations Research. The main contributions of the work are centred around the control of search within the OPlan planning framework, and this paper outlines the search control heuristics employed within the planner. These involve the use of condition typing, time and resource constraints and domain constraints to allow knowledge about an application domain to be used to prune the searc...

Most practical work on AI planning systems during the last fteen years has been based on hierarchical task network (HTN) decomposition, but until now, there has been very little analytical work on the properties of HTN planners. This paper describes how the complexity of HTN planning varies with various conditions on the task networks.

We present a representation of events and action based on interval temporal logic that is significantly more expressive and more natural than most previous AI approaches. The representation is motivated by work in natural language semantics and discourse, temporal logic, and AI planning and plan recognition. The formal basis of the representation is presented in detail, from the axiomatization of time periods to the relationship between actions and events and their effects. The power of the representation is illustrated by applying it to the axiomatization and solution of several standard problems from the AI literature on action and change. An approach to the frame problem based on explanation closure is shown to be both powerful and natural when combined with our representational framework. We also discuss features of the logic that are beyond the scope of many traditional representations, and describe our approach to difficult problems such as external events and simultaneous action...

this paper appears in Proceedings of AAAI '96, pp. 1215-1222. F. Bacchus and F. Kabanza / Temporally Extended Goals 2 Yet this flexibility also poses a problem: how do we communicate to such an agent the task we want accomplished in a sufficiently precise manner so that it does what we really

Many planning domains require a richer notion of time in which actions can overlap and have different durations. The key to fast performance in classical planners (e.g., Graphplan, ipp, and Blackbox) has been the use of a disjunctive representation with powerful mutual exclusion reasoning. This paper presents TGP a new algorithm for temporal planning. TGP operates

One big obstacle to understanding the nature of hierarchical task network (htn) planning has been the lack of a clear theoretical framework. In particular, no one has yet presented a clear and concise htn algorithm that is sound and complete.

We present zeno, a least commitment planner that handles actions occurring over extended intervals of time. Deadline goals, metric preconditions, metric effects, and continuous change are supported. Simultaneous actions are allowed when their effects do not interfere. Unlike most planners that deal with complex languages, the zeno planning algorithm is sound and complete. The running code is a complete implementation of the formal algorithm, capable of solving simple problems (i.e., those involving less than a dozen steps). Introduction We have built a least commitment planner, zeno, that handles actions occuring over extended intervals of time and whose preconditions and effects can be temporally quantified. These capabilities enable zeno to reason about deadline goals, piecewise-linear continuous change, external events and to a limited extent, simultaneous actions. While other planners exist with some of these features, zeno is different because it is both sound and complete. As a...

Agents situated in dynamic and uncertain environments require several capabilities for successful operation. Such agents must monitor the world and respond appropriately to important events. The agents should be able to accept goals, synthesize complex plans for achieving those goals, and execute the plans while continuing to be responsive to changes in the world. As events render some current activities obsolete, the agents should be able to modify their plans while continuing activities unaffected by those events. The Cypress system is a domain-independent framework for defining persistent agents with this full range of behavior. Cypress has been used for several demanding applications, including military operations, real-time tracking, and fault diagnosis. ii Contents 1 Introduction 1 1.1 Research Strategy : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.2 A New Technology : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 2 Overview of C...

This paper describes the O-Plan2 agent oriented architecture and describes the communication which takes place between planning and execution monitoring agents built upon the architecture. Separate modules of such a system are identified along with internal and external interface specifications that form a part of the design. Time constraints, resource usage, object selection and condition/effect causal constraints are handled as an integral part of the overall system structure by treating specialised constraint management as supporting the core decision making components in the architecture. A close coupling of planning and time or resource scheduling is therefore possible within a system employing an activity based plan representation. 2 History and Technical Influences