On the Complexity of Case-Based Planning

We analyze the computational complexity of problems related to case-based planning: planning when a plan for a similar instance is known, and planning from a library of plans. We prove that planning from a single case has the same complexity than generative planning (i.e., planning "from scratch"); using an extended definition of cases, complexity is reduced if the domain stored in the case is similar to the one to search plans for. Planning from a library of cases is shown to have the same complexity. In both cases, the complexity of planning remains, in the worst case, PSPACE-complete

Exploiting a graphplan framework in temporal planning

Graphplan (Blum and Furst 1995) has proved a popular and successful basis for a succession of extensions. An extension to handle temporal planning is a natural one to consider, because of the seductively time-like structure of the layers in the plan graph. TGP (Smith and Weld 1999) and TPSys (Garrido, Onaindía, and Barber 2001; Garrido, Fox, and Long 2002) are both examples of temporal planners that have exploited the Graphplan foundation. However, both of these systems (including both versions of TPSys) exploit the graph to represent a uniform flow of time. In this paper we describe an alternative approach, in which the graph is used to represent the purely logical structuring of the plan, with temporal constraints being managed separately (although not independently). The approach uses a linear constraint solver to ensure that temporal durations are correctly respected. The resulting planner offers an interesting alternative to the other approaches, offering an important extension in expressive power

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

The FF Planning System: Fast Plan Generation Through Heuristic Search

We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP

Cross organisational compatible workflows generation and execution

With the development of internet and electronics, the demand for electronic and online commerce has increased. This has, in turn, increased the demand for business process automation. Workflow has established itself as the technology used for business process automation. Since business organisations have to work in coordination with many other business organisations in order to succeed in business, the workflows of business organisations are expected to collaborate with those of other business organisations. Collaborating organisations can only proceed in business if they have compatible workflows. Therefore, there is a need for cross organisational workflow collaboration. The dynamism and complexity of online and electronic business and high demand from the market leave the workflows prone to frequent changes. If a workflow changes, it has to be re-engineered as well as reconciled with the workflows of the collaborating organisations. To avoid the continuous re-engineering and reconciliation of workflows, and to reuse the existing units of work done, the focus has recently shifted from modeling workflows to automatic workflow generation. Workflows must proceed to runtime execution, otherwise, the effort invested in the build time workflow modeling is wasted. Therefore, workflow management and collaboration systems must support workflow enactment and runtime workflow collaboration. Although substantial research has been done in build-time workflow collaboration, automatic workflow generation, workflow enactment and runtime workflow collaboration, the integration of these highly inter-dependent aspects of workflow has not been considered in the literature. The research work presented in this thesis investigates the integration of these different aspects. The main focus of the research presented in this thesis is the creation of a framework that is able to generate multiple sets of compatible workflows for multiple collaborating organisations, from their OWLS process definitions and high level goals. The proposed framework also supports runtime enactment and runtime collaboration of the generated workflows

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

Uma contribuição ao estudo do planejamento temporal em inteligência artificial

In this work it is studied the main methods of temporal planning. It is proposed solutions based on graph of plans as well as solutions based on the translation of graph of plans into time Petri nets. A review is presented about the algorithms for the treatment of classical planning and temporal planning. This review aims at to present the context of this work. Subsequently, it is proposed a new method of temporal treatment for the graph of plans and its translation into a time Petri net.Neste trabalho se estuda os principais métodos de planejamento temporal. Propõe soluções baseadas no grafo de planos, bem como soluções baseadas na tradução deste em Redes de Petri Temporais. Objetivando a contextualização do cenário em que o presente trabalho se insere, é apresentada uma revisão dos algoritmos que fazem o tratamento de problemas de planejamento clássico e o planejamento temporal. Propõe-se um novo método de tratamento temporal sobre o grafo de planos e sua tradução para uma Rede de Petri Temporal

Ignoring Irrelevant Facts and Operators in Plan Generation

It is traditional wisdom that one should start from the goals when generating a plan in order to focus the plan generation process on potentially relevant actions. The graphplan system, however, which is the most efficient planning system nowadays, builds a "planning graph" in a forward-chaining manner. Although this strategy seems to work well, it may possibly lead to problems if the planning task description contains irrelevant information. Although some irrelevant information can be filtered out by graphplan, most cases of irrelevance are not noticed. In this paper, we analyze the effects arising from "irrelevant" information to planning task descriptions for different types of planners. Based on that, we propose a family of heuristics that select relevant information by minimizing the number of initial facts that are used when approximating a plan by backchaining from the goals ignoring any conflicts. These heuristics, although not solution-preserving, turn out to be v..