Automatic synthesis and use of generic types in planning

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 domains 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 of typical of transportation domains, but instead 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 domains presented in this paper). The analyses described here are completely planner-independent and contribute to an increasing collection of pre-planning analysis tools which help to increase performance of planners by decomposing and understanding the structures of planning problems before planners are applied

Marvin : macro-actions from reduced versions of the instance

Marvin is a forward-chaining heuristic-search planner. The basic search strategy used is similar to FF's enforced hill-climbing with helpful actions (Hoffmann and Nebel 2001); Marvin extends this strategy, adding extra features to the search and preprocessing steps to infer information from the domain

Reformulation in planning

Reformulation of a problem is intended to make the problem more amenable to efficient solution. This is equally true in the special case of reformulating a planning problem. This paper considers various ways in which reformulation can be exploited in planning

Progress in AI Planning Research and Applications

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning

The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning

This paper presents GRT, a domain-independent heuristic planning system for STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase, it estimates the distance between each fact and the goals of the problem, in a backward direction. Then, in the search phase, these estimates are used in order to further estimate the distance between each intermediate state and the goals, guiding so the search process in a forward direction and on a best-first basis. The paper presents the benefits from the adoption of opposite directions between the preprocessing and the search phases, discusses some difficulties that arise in the pre-processing phase and introduces techniques to cope with them. Moreover, it presents several methods of improving the efficiency of the heuristic, by enriching the representation and by reducing the size of the problem. Finally, a method of overcoming local optimal states, based on domain axioms, is proposed. According to it, difficult problems are decomposed into easier sub-problems that have to be solved sequentially. The performance results from various domains, including those of the recent planning competitions, show that GRT is among the fastest planners

A Diagrammatic Inter-Lingua for Planning Domain Descriptions

Sentential and diagrammatic representations are two different formalisms for describing domains and problems. Sentential descriptions are usually more expressive than diagrammatic ones, but tend to present a more complex and less intuitive notation. All modern planning domain description languages are sentential. The complexity of sentential formalisms has been of hindrance to the wider dissemination and take up of planning technology beyond the planning research community. This paper proposes a diagrammatic “meta-language” for planning domain descriptions based on setGraphs as an alternative to sentential languages. SetGraphs represent actions, states and goals in terms of set- and graph theoretic constructs. Through various practical examples, setGraphs are shown to yield simpler and more intuitive domain encodings, and to offer a high degree of elaboration tolerance. A theoretical analysis shows how the representation can be easily encoded using formal languages, and demonstrates that setGraphs are at least as expressive as a standard modern propositional planning domain description language. The model proposed constitutes a “core” representation that can be adopted as a basis for developing different planning domain description languages; it is suitable to be used across different levels of abstraction during the processes of language development and domain knowledge engineering, and it facilitates the elicitation, maintenance and re-use of planning domain descriptions

Object-orientated planning domain engineering

The development of domain independent planners focuses on the creation of generic problem solvers. These solvers are designed to solve problems that are declaratively described to them. In order to solve arbitrary problems, the planner must possess efficient and effective algorithms; however, an often overlooked requirement is the need for a complete and correct description of the problem domain. Currently, the most common domain description language is a prepositional logic, state-based language called STRIPS. This thesis develops a new object-orientated domain description language that addresses some of the common errors made in writing STRIPS domains. This new language also features powerful semantics that are shown to gready ease the description of certain domain features. A common criticism of domain independent planning is that the requirement of being domain independent necessarily precludes the exploitation of domain specific knowledge that would increase efficiency. One technique used to address this is to recognise patterns of behaviour in domains and abstract them out into a higher-level representations that are exploitable. These higher-level representations are called generic types. This thesis investigates the ways in which generic types can be used to assist the domain engineering process. A language is developed for describing the behavioural patterns of generic types and the ways in which they can be exploited. This opens a domain independent channel for domain specific knowledge to pass from the domain engineer to the planner

Where 'Ignoring Delete Lists' Works: Local Search Topology in Planning Benchmarks

Between 1998 and 2004, the planning community has seen vast progress in terms of the sizes of benchmark examples that domain-independent planners can tackle successfully. The key technique behind this progress is the use of heuristic functions based on relaxing the planning task at hand, where the relaxation is to assume that all delete lists are empty. The unprecedented success of such methods, in many commonly used benchmark examples, calls for an understanding of what classes of domains these methods are well suited for. In the investigation at hand, we derive a formal background to such an understanding. We perform a case study covering a range of 30 commonly used STRIPS and ADL benchmark domains, including all examples used in the first four international planning competitions. We *prove* connections between domain structure and local search topology -- heuristic cost surface properties -- under an idealized version of the heuristic functions used in modern planners. The idealized heuristic function is called h^+, and differs from the practically used functions in that it returns the length of an *optimal* relaxed plan, which is NP-hard to compute. We identify several key characteristics of the topology under h^+, concerning the existence/non-existence of unrecognized dead ends, as well as the existence/non-existence of constant upper bounds on the difficulty of escaping local minima and benches. These distinctions divide the (set of all) planning domains into a taxonomy of classes of varying h^+ topology. As it turns out, many of the 30 investigated domains lie in classes with a relatively easy topology. Most particularly, 12 of the domains lie in classes where FFs search algorithm, provided with h^+, is a polynomial solving mechanism. We also present results relating h^+ to its approximation as implemented in FF. The behavior regarding dead ends is provably the same. We summarize the results of an empirical investigation showing that, in many domains, the topological qualities of h^+ are largely inherited by the approximation. The overall investigation gives a rare example of a successful analysis of the connections between typical-case problem structure, and search performance. The theoretical investigation also gives hints on how the topological phenomena might be automatically recognizable by domain analysis techniques. We outline some preliminary steps we made into that direction

Engineering Benchmarks for Planning: the Domains Used in the Deterministic Part of IPC-4

In a field of research about general reasoning mechanisms, it is essential to have appropriate benchmarks. Ideally, the benchmarks should reflect possible applications of the developed technology. In AI Planning, researchers more and more tend to draw their testing examples from the benchmark collections used in the International Planning Competition (IPC). In the organization of (the deterministic part of) the fourth IPC, IPC-4, the authors therefore invested significant effort to create a useful set of benchmarks. They come from five different (potential) real-world applications of planning: airport ground traffic control, oil derivative transportation in pipeline networks, model-checking safety properties, power supply restoration, and UMTS call setup. Adapting and preparing such an application for use as a benchmark in the IPC involves, at the time, inevitable (often drastic) simplifications, as well as careful choice between, and engineering of, domain encodings. For the first time in the IPC, we used compilations to formulate complex domain features in simple languages such as STRIPS, rather than just dropping the more interesting problem constraints in the simpler language subsets. The article explains and discusses the five application domains and their adaptation to form the PDDL test suites used in IPC-4. We summarize known theoretical results on structural properties of the domains, regarding their computational complexity and provable properties of their topology under the h+ function (an idealized version of the relaxed plan heuristic). We present new (empirical) results illuminating properties such as the quality of the most wide-spread heuristic functions (planning graph, serial planning graph, and relaxed plan), the growth of propositional representations over instance size, and the number of actions available to achieve each fact; we discuss these data in conjunction with the best results achieved by the different kinds of planners participating in IPC-4