Early work on the NONLIN and O-Plan projects indicated a need for a defined methodology which would guide users performing various roles in the acquisition and analysis of domain requirements for planning. This work included links to a requirement analysis methodology, CORE (COntrolled Requirements Expression) , tool support via an intelligent assistant as part of the Task Formalism (TF) Workstation and an initial collection of guidelines and checklists to aid in using the TF domain description language. This paper describes work underway to follow-on from this past research and to infuse it with knowledge gained from recent research related to planning domain development, knowledge modelling, design rationale and ontological and requirements engineering. Introduction The activities involved in discovering, engineering, documenting, and maintaining a set of domain constructs for most AI planning-based projects can be considered ad hoc and disorganised, at best. The current sources for...

O-Plan is a command, planning and control architecture with an open modular structure intended to allow experimentation on, or replacement of, various components. The research is seeking to determine which functions are generally required in a number of application areas and across a number of different command, planning, scheduling and control systems. O-Plan aims to demonstrate how a planner, situated in a task assignment and plan execution (command and control) environment, and using extensive domain knowledge, can allow for flexible, distributed, collaborative, and mixedinitiative planning. The research is seeking to verify this total systems approach by studying a simplified three-level model with separable task assignment, plan generation and plan execution agents. O-Plan has been applied to logistics tasks that require flexible response in changing situations. Summary The O-Plan research and development project is seeking to identify re-usable modules and interfaces within plan...

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

The search space in partial-order planning grows quickly with the number of subgoals and initial conditions, as well as less countable factors such as operator ordering and subgoal interactions. For partial-order planners to solve more than simple problems, the expansion of the search space will need to be controlled. This paper presents four new approaches to controlling search space expansion by exploiting commonalities in emerging plans. These approaches are described in terms of their algorithms, their effect on the completeness and correctness of the underlying planner and their expected performance. The four new and two existing approaches are compared on several metrics of search space and planning overhead. 1 Improving Search Efficiency in Planners Partial order planning is becoming a common method of planning. Unfortunately, but hardly unexpectedly, the search space in partial order planning expands quickly as the problem size increases. Unfortunately, but less expectedly, se...

In this paper we present the design and algorithmic details of a directed search planner to solve benchmark planning problems speci#ed in PDDL+ syntax. The planner copes with durative actions, grounds and groups predicates and instantiates functions. The operators possibly span an in#nite state space whichistackled by guided exploration. The heuristic estimates are inferred by plan relaxation, through pattern databases, or by state-to-goal di#erences of numerical values and are incorporated in scalable heuristic search planning algorithms. 1

Partial order planners are very effective in solving simple problems. However, the search space in planning grows quickly with the number of subgoals and initial conditions, as well as less countable factors such as operator ordering and subgoal interactions. There are certain inherent features of these planners like flaw selection and threat resolution that causes this search space explosion. For partial-order planners to solve more than simple problems, the expansion of the search space will need to be controlled. This paper presents four new approaches to controlling search space expansion by exploiting commonalities in emerging plans and by effective threat resolution. These approaches are described in terms of their algorithms, their effect on the completeness and correctness of the underlying planner and their expected performance. The four new and two existing approaches are compared on several metrics of search space and planning overhead. This research was supported by a Nat...

In order to make planning technology more accessible and usable the planning community may have to adopt standard notations for embodying symbolic models of planning domains.