This work is concerned with the automatic inference of generic types from STRIPS planning domain descriptions. Generic types are higher order types allowing the partition of domains (and components of domains) into different domain classes, including the commonly occurring transportation domain class. We show how the generic type structure of domains can be exploited to increase planner efficiency. We have focussed so far on the generic types typical of transportation domains, but intend to go on to characterise, and identify examples of, other domain classes such as construction domains. One of the most interesting properties of the work described here is that domains which would not be recognised, by the human, as transportation domains can turn out to have an underlying transportation character which can be exploited by the application of heuristics suited to standard transportation domains. We illustrate this by considering both standard transportation domains (such as Logistics) and non-standard ones (the PaintWall domain presented in this paper). The analyses described here are completely planner-independent and contribute to an increasing collection of pre-plannin 9 analysis tools which help to increase performance of planners by decomposing and understanding the structures of planning problems before planners are applied.

Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present and compare two automated methods that learn relevant information from previous experience in a domain and use it to solve new problem instances. Our methods share a common four-step strategy. First, a domain is analyzed and structural information is extracted, then macro-operators are generated based on the previously discovered structure. A filtering and ranking procedure selects the most useful macro-operators. Finally, the selected macros are used to speed up future searches. We have successfully used such an approach in the fourth international planning competition IPC-4. Our system, Macro-FF, extends Hoffmann’s state-of-the-art planner FF 2.3 with support for two kinds of macro-operators, and with engineering enhancements. We demonstrate the effectiveness of our ideas on benchmarks from international planning competitions. Our results indicate a large reduction in search effort in those complex domains where structural information can be inferred.

. Many known planning tasks have inherent constraints concerning the best order in which to achieve the goals. A number of research efforts have been made to detect such constraints and use them for guiding search, in the hope to speed up the planning process. We go beyond the previous approaches by defining ordering constraints not only over the (top level) goals, but also over the sub-goals that will arise during planning. Landmarks are facts that must be true at some point in every valid solution plan. We show how such landmarks can be found, how their inherent ordering constraints can be approximated, and how this information can be used to decompose a given planning task into several smaller sub-tasks. Our methodology is completely domain- and plannerindependent. The implementation demonstrates that the approach can yield significant performance improvements in both heuristic forward search and GRAPHPLAN-style planning. 1

In this paper we present a method for extracting important intermediate planning goals, and for finding orders between them which can then be used during planning. We have implemented this method and have integrated it with an example planning system, and in the paper we present results that support our expectation that these orders can lead to improved planning performance (both in terms of speed and plan quality). 1 Introduction A number of methods have been proposed for identifying orders between goals in a planning problem, the idea being to reduce the inherent complexity of the problem by partitioning it into more manageable chunks. If an order can be identified between a set of goals then this can be used to focus the planner on achieving goals that are placed earlier in the order. The particular type of goal orders that we are interested in, in this paper, have been described as reasonable orders [6] and the central idea behind them is this: a pair of goals A and B can be orde...

This paper presents GRT, a domain-independent heuristic planning system for STRIPS worlds. GRT solves

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...

Our research concerns formal, expressive, objectcentred languages and tools for use in engineering domains for planning applications. In this paper we extend our recent work on an object-centred language for encoding precondition planning domains to a language called OCL h , designed for HTN planning. Domain encodings for HTN planners are particularly troublesome, because they tend to be used in knowledged-based applications requiring a great deal of `domain engineering', and the abstract operators central to an HTN model do not share the fairly clear declarative semantics of concrete pre- and post condition operators. Central to our approach is the development, in parallel, of the abstract operator set and the hierarchical state specification of the objects that the operators manipulate. In this paper we define and illustrate a transparency property, together with a transparency checking tool, which helps the developer to encode a clear planning model in OCL h . Our encoding of the Tr...

Despite recent progress in AI planning, many problems remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present an automated method that learns relevant information from previous experience in a domain and uses it to solve new problem instances. Our approach produces a small set of useful macro-operators as a result of a training process. For each training problem, a structure called a solution graph is built based on the problem solution. Macro-operators with partial ordering of moves are extracted from the solution graph. A filtering and ranking procedure selects the most useful macro-operators, which will be used in future searches. We introduce a heuristic technique that uses only the most promising instantiations of a selected macro for node expansion. Our results indicate an impressive reduction of the search effort in complex domains where structure information can be inferred.

Specifying operator descriptions for planning domain models, especially using standard pre- and post condition symbolism, is a slow and painstaking process. This is because one is trying to capture what is essentially procedural knowledge in a declarative way in a language whose design is influenced by the construction of planning engines. The problem is acute if nonplanning experts are undertaking this task, and/or the operators are complex or hierarchical. In this paper we describe opmaker, a method in which the domain expert specifies the declarative structure of the domain (in terms of an object hierarchy, object descriptions etc) and provides training operator sequences. This input is made in the context of a tools environment supporting planner domain acquisition and modelling. opmaker then induces a set of parameterised operator descriptions from these examples, removing the need for the user to become involved in complex parameter manipulation within the underlying symbolic, logicbased language. We discuss the empirical evaluation of the implemented induction algorithm with the help of a range of domains, and draw conclusions for future work.

... of hand-tailorable planners by compiling each domain description into a separate domain-specific planner. We discuss why and when this approach can be useful, and we present experimental results showing that our approach produces significant increases in the speed of planning.