Graphplan Research Articles

Research on the Access-Authorizing-Problem in a Collaborative Manufacturing Platform

Access control on shared resources or information is a high complex problem in a collaborative manufacturing platform since so many design information and manufacturing resources will be created and shared, Meanwhile, security rules or polices from different partners should be obeyed when accessing on these shared resource. Non intuitional semantic of security policies leads to the Access Authorizing Problem (AAP): how do administrators configure an authorizing action sequence to perform the specified assignment task without disobeying these security policies and constraints from different partners or provider? In this paper, we define this problem formally at first and model it to a classic planning problem, then employ the GraphPlan algorithm to resolve it. A case is cited to testify the effectiveness of our method and system in the end. The main contribution of the research is to present definition and resolving method to the access authorizing problem, which can help administrators to make correct authorizing decision.

A Cost‐Effective Planning Graph Approach for Large‐Scale Web Service Composition

Web Service Composition (WSC) problems can be considered as a service matching problem, which means that the output parameters of a Web service can be used as inputs of another one. However, when a very large number of Web services are deployed in the environment, the service composition has become sophisticated and complicated process. In this study, we proposed a novel cost‐effective Web service composition mechanism. It utilizes planning graph based on backward search algorithm to find multiple feasible solutions and recommends a best composition solution according to the lowest service cost. In other words, the proposed approach is a goal‐driven mechanism, which can recommend the approximate solutions, but it consumes fewer amounts of Web services and less nested levels of composite service. Finally, we implement a simulation platform to validate the proposed cost‐effective planning graph mechanism in large‐scale Web services environment. The simulation results show that our proposed algorithm based on the backward planning graph has reduced by 94% service cost in three different environments of service composition that is compared with other existing service composition approaches which are based on a forward planning graph.

A Justified Plan Graph Analysis of the Early Houses (1975-1982) of Glenn Murcutt

The Justified Plan Graph (JPG) technique was developed in the late 1970s and refined in the following two decades as a means of undertaking qualitative and quantitative research into the spatial structure or permeability of buildings. Famously used by Space Syntax researchers to uncover the social logic of architectural types, the technique remains an important, if not widely understood, approach to the analysis of the built environment. This paper uses the JPG method to undertake a three-stage analysis of the early houses of Pritzker Prize winning architect Glenn Murcutt; the stages are visual analysis, mathematical analysis and theoretical analysis. Through this process the paper offers a rare application of the JPG method to multiple works by the same architect and demonstrates the construction of a series of “inequality genotypes”, a partial “statistical genotype” and a partial “statistical archetype” for these houses. Instead of seeking to uncover the social structure of Murcutt’s housing, the paper analyses the architect’s distinctive approach to ordering space within otherwise simple volumes or forms. The ultimate purpose of this analysis is to offer an alternative space-based, rather than form-based, insight into this architect’s work.

Probabilistic Plan Graph Heuristic for Probabilistic Planning

This work focuses on developing domain-independent heuristics for probabilistic planning problems characterized by full observability and non-deterministic effects of actions that are expressed by probability distributions. The approach is to first search for a high probability deterministic plan using a classical planner. A novel probabilistic plan graph heuristic is used to guide the search towards high probability plans. The resulting plans can be used in a system that handles unexpected outcomes by runtime replanning. The plans can also be incrementally augmented with contingency branches for the most critical action outcomes. This abstract will describe the steps that we have taken in completing the above work and the obtained results.

The Mathematics of Spatial Configuration: Revisiting, Revising and Critiquing Justified Plan Graph Theory

The justified plan graph (JPG) was the first practical analytical method developed as part of the theory of Space Syntax, which purported to provide a graphical, mathematical and associated theoretical model for analysing the spatial configuration of buildings. In spite of early interest, in recent years relatively little research using this method has been published, perhaps because the JPG method is rarely explained in its totality and when it is, the descriptions are often inconsistent or unclear. Although it is now embedded in several software programs and its use may be more widespread, it is no better understood and after processing there is a marked lack of consistency in how the results are interpreted. This paper provides a historical background for the development of the JPG and a discussion of its conceptual or theoretical origins, followed by a “worked example” of the mathematics of the JPG. In combination with the results for two further cases, the paper identifies some important interpretative limits in the method and uses the examples to explain its potential use in design analysis. Finally, the paper discusses how the consistent application of this method to sets of related buildings is likely to produce a more valuable, and statistically viable, basis for future work.

LPRPG-P: Relaxed Plan Heuristics for Planning with Preferences

In this paper we present a planner, LPRPG-P, capable of reasoning with the non-temporal subset of PDDL 3 preferences. Our focus is on computation of relaxed plan based heuristics that effectively guide a planner towards good solutions satisfying preferences. We build on the planner LPRPG, a hybrid relaxed planning graph (RPG)--linear programming (LP) approach. We make extensions to the RPG to reason with propositional preferences, and to the LP to reason with numeric preferences. LPRPG-P is the first planner with direct guidance for numeric preference satisfaction, exploiting the strong numeric reasoning of the LP. We introduce an anytime search approach for use with our new heuristic, and present results showing that LPRPG-P extends the state of the art in domain-independent planning with preferences.

Automated Trajectory and Path Planning Analysis Based on Ultra Wideband Data

This work studies the utility of ultra wideband (UWB) sensor technology for optimizing construction site operations. In particular, recorded trajectory signals provide important behavioral information regarding site usage that can be exploited for planning and logistical purposes. Here, post processed trajectory information from UWB signals populate an occupancy grid, which is a spatial histogram of workers, travel patterns. The occupancy grid is used for path planning and serves two purposes. The first suggests optimal paths for workers while the second generates optimal paths for vehicles. The path generation algorithm provides multiple paths, and optimality scores for a variety of path metrics, including distance, time, and worker sparsity. To generate the optimal paths, a path planning graph is generated from the occupancy grid and any automatically determined or manually specified obstacles. The algorithm applies equally to any mobile, major construction resource: workers, equipment, and materials that are tagged with a UWB tag. Vital statistics obtained from the calculated paths provide important operational information to site operations and safety managers.

OHCP: A Local Search Planner Based on Ordered Hill Climbing Algorithm and Local-Minimal Restart Strategy

This paper describes OHCP, a fast planner using local search based on Ordered Hill Climbing (OHC) search algorithm and local-minimal restart strategy. OHC is used as a basis of a heuristic planner in conjunction with relaxed planning graph heuristic. A novel approach is proposed in OHC framework to extract useful information from relaxed plans to preorder all promising neighborhoods, which can cut down the frequency of calling the heuristic evaluation procedure. In order to preserve completeness and improve search effort, a new restart strategy for complete search from local minimal is proposed when the local search guided by OHC fails. The ideas are implemented in our planner OHCP. Experimental results show strong performance of the proposed planner on recent international planning competition domains.

조건부 계획수립을 위한 효과적인 그래프 기반의 휴리스틱

In order to derive domain-independent heuristics from the specification of a planning problem, it is required to relax the given problem and then solve the relaxed one. In this paper, we present a new planning graph, Merged Planning Graph(MPG), and GD heuristics for solving contingent planning problems with both uncertainty about the initial state and non-deterministic action effects. The merged planning graph is an extended one to be applied to the contingent planning problems from the relaxed planning graph, which is a common means to get effective heuristics for solving the classical planning problems. In order to get heuristics for solving the contingent planning problems with sensing actions and non-deterministic actions, the new graph utilizes additionally the effect-merge relaxations of these actions as well as the traditional delete relaxations. Proceeding parallel to the forward expansion of the merged planning graph, the computation of GD heuristic excludes the unnecessary redundant cost from estimating the minimal reachability cost to achieve the overall set of goals by analyzing interdependencies among goals or subgoals. Therefore, GD heuristics have the advantage that they usually require less computation time than the overlap heuristics, but are more informative than the max and the additive heuristics. In this paper, we explain the experimental analysis to show the accuracy and the search efficiency of the GD heuristics.

A weighted CSP approach to cost-optimal planning

For planning to come of age, plans must be judged by a measure of quality, such as the total cost of actions. This paper describes an optimal-cost planner which guarantees global optimality whenever the planning problem has a solution. We code the extraction of an optimal plan, from a planning graph with a fixed number k of levels, as a weighted constraint satisfaction problem (WCSP). The specific structure of the resulting WCSP means that a state-of-the-art exhaustive solver was able to find an optimal plan in planning graphs containing several thousand nodes. Thorough experimental investigations demonstrated that using the planning graph in optimal planning is a practical possibility for problems of moderate size, although not competitive, in terms of computation time, with optimal state-space-search planners. Our general conclusion is, therefore, that planning-graph-based optimal planning is not the most efficient method for cost-optimal planning. Nonetheless, the notions of indispensable (sets of) actions and too-costly actions introduced in this paper have various potential applications in optimal planning. These actions can be detected very rapidly by analysis of the relaxed planning graph.

State agnostic planning graphs: deterministic, non-deterministic, and probabilistic planning

Planning graphs have been shown to be a rich source of heuristic information for many kinds of planners. In many cases, planners must compute a planning graph for each element of a set of states, and the naive technique enumerates the graphs individually. This is equivalent to solving a multiple-source shortest path problem by iterating a single-source algorithm over each source. We introduce a data-structure, the state agnostic planning graph, that directly solves the multiple-source problem for the relaxation introduced by planning graphs. The technique can also be characterized as exploiting the overlap present in sets of planning graphs. For the purpose of exposition, we first present the technique in deterministic (classical) planning to capture a set of planning graphs used in forward chaining search. A more prominent application of this technique is in conformant and conditional planning (i.e., search in belief state space), where each search node utilizes a set of planning graphs; an optimization to exploit state overlap between belief states collapses the set of sets of planning graphs to a single set. We describe another extension in conformant probabilistic planning that reuses planning graph samples of probabilistic action outcomes across search nodes to otherwise curb the inherent prediction cost associated with handling probabilistic actions. Finally, we show how to extract a state agnostic relaxed plan that implicitly solves the relaxed planning problem in each of the planning graphs represented by the state agnostic planning graph and reduces each heuristic evaluation to counting the relevant actions in the state agnostic relaxed plan. Our experimental evaluation (using many existing International Planning Competition problems from classical and non-deterministic conformant tracks) quantifies each of these performance boosts, and demonstrates that heuristic belief state space progression planning using our technique is competitive with the state of the art.

Safety Analysis of ARBAC Policy Based on Graphplan

Safety analysis is the prerequisite mechanism for distributed access control system.Graphplan theory was imported to perform safety analysis on those access control system which support role hierarchy and Static Mutual Exclusion Roles(SMER).A complete resolution for the reachability problems,a principal safety analysis problem,is planned and designed.Firstly,a description model using planning language is set up,virtual action is put forward to express the inheritance relation between roles,and SMERs is transformed to domain constraints.Secondly,to settle the negative predicate problem and open world assumption problem,domain axiom is employed.Then the Graphplan arithmetic is modified by trimming 'NooP' actions and relative predicates from plan graph using those axioms.Based on the amended arithmetic,the corresponding experiment system is developed.At last,a application case of the analysis process is illustrated.

DIADS

Many enterprise environments have databases running on network-attached storage infrastructure (referred to as Storage Area Networks or SANs ). Both the database and the SAN are complex subsystems that are managed by separate teams of administrators. As often as not, database administrators have limited understanding of SAN configuration and behavior, and limited visibility into the SAN's run-time performance; and vice versa for the SAN administrators. Diagnosing the cause of performance problems is a challenging exercise in these environments. We propose to remedy the situation through a novel tool, called Diads, for database and SAN problem diagnosis. This demonstration proposal summarizes the technical innovations in Diads: (i) a powerful abstraction called Annotated Plan Graphs (APGs) that ties together the execution path of queries in the database and the SAN using low-overhead monitoring data, and (ii) a diagnosis workflow that combines domain-specific knowledge with machine-learning techniques. The scenarios presented in the demonstration are also described.

컴포넌트 서비스 기반의 휴리스틱 탐색 계획기

최근 들어 로봇 작업 계획기에 요구되는 중요한 기능 중의 하나가 이미 존재하는 컴포넌트 서비스들을 결합하여 새로운 서비스로 조합해낼 수 있는 계획 기능이다. 본 논문에서는 이러한 컴포넌트 서비스 조합을 위한 커널모듈로 개발된 휴리스틱 탐색 계획기인 JPLAN의 설계와 구현에 대해 설명한다. JPLAN은 효율적인 상태 공간 탐색을 위해 지역 탐색 알고리즘과 계획 그래프 휴리스틱을 이용한다. 본 논문에서 제안하는 지역 탐색 알고리즘인 EHC+는 FF 등의 상태 공간 계획기에 적용되어 높은 효율성을 보인 Enforced Hill-Climbing (EHC)을 확장한 것이다. EHC+는 EHC에 비해 소량의 추가적인 지역 탐색을 필요로 하지만 목표 상태까지 전체 탐색 양을 줄일 수 있고 더 짧은 계획을 얻을 수있다. 또한 본 본문에서는 대규모 상태 공간 탐색에 필수적인 효과적인 휴리스틱 추출 방법을 제안한다. 본 논문에서 제안하는 휴리스틱 추출방법은 Graphplan에서 계획 생성을 위해 처음 제안된 계획 그래프를 이용한다. 본 논문에서는 이러한 계획 그래프 기반의 다양한 휴리스틱들을 소개하고, 이들이 계획 생성에 미치는 효과를 실험을 통해 분석해본다. Nowadays, one of the important functionalities required from robot task planners is to generate plans to compose existing component services into a new service. In this paper, we introduce the design and implementation of a heuristic search planner, JPLAN, as a kernel module for component service composition. JPLAN uses a local search algorithm and planning graph heuristics. The local search algorithm, EHC+, is an extended version of the Enforced Hill-Climbing(EHC) which have shown high efficiency applied in state-space planners including FF. It requires some amount of additional local search, but it is expected to reduce overall amount of search to arrive at a goal state and get shorter plans. We also present some effective heuristic extraction methods which are necessarily needed for search on a large state-space. The heuristic extraction methods utilize planning graphs that have been first used for plan generation in Graphplan. We introduce some planning graph heuristics and then analyze their effects on plan generation through experiments.

Sequential Monte Carlo in reachability heuristics for probabilistic planning

Some of the current best conformant probabilistic planners focus on finding a fixed length plan with maximal probability. While these approaches can find optimal solutions, they often do not scale for large problems or plan lengths. As has been shown in classical planning, heuristic search outperforms bounded length search (especially when an appropriate plan length is not given a priori). The problem with applying heuristic search in probabilistic planning is that effective heuristics are as yet lacking. In this work, we apply heuristic search to conformant probabilistic planning by adapting planning graph heuristics developed for non-deterministic planning. We evaluate a straight-forward application of these planning graph techniques, which amounts to exactly computing a distribution over many relaxed planning graphs (one planning graph for each joint outcome of uncertain actions at each time step). Computing this distribution is costly, so we apply Sequential Monte Carlo (SMC) to approximate it. One important issue that we explore in this work is how to automatically determine the number of samples required for effective heuristic computation. We empirically demonstrate on several domains how our efficient, but sometimes suboptimal, approach enables our planner to solve much larger problems than an existing optimal bounded length probabilistic planner and still find reasonable quality solutions.

Planning in highly dynamic environments: an anytime approach for planning under time constraints

In this paper, we present a novel and domain-independent planner aimed at working in highly dynamic environments with time constraints. The planner follows the anytime principles: a first solution can be quickly computed and the quality of the final plan is improved as long as time is available. This way, the planner can provide either fast reactions or very good quality plans depending on the demands of the environment. As an on-line planner, it also offers important advantages: our planner allows the plan to start its execution before it is totally generated, unexpected events are efficiently tackled during execution, and sensing actions allow the acquisition of required information in partially observable domains. The planning algorithm is based on problem decomposition and relaxation techniques. The traditional relaxed planning graph has been adapted to this on-line framework by considering information about sensing actions and action costs. Results also show that our planner is competitive with other top-performing classical planners.

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.

Planning Graph Heuristics for Belief Space Search

Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics. We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A* search. The second, POND, is a conditional progression planner that uses AO* search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several state of the art approaches in conditional planning.

Branching and pruning

A key feature of modern optimal planners such as graphplan and blackbox is their ability to prune large parts of the search space. Previous Partial Order Causal Link (POCL) planners provide an alte...

Using Memory to Transform Search on the Planning Graph

The Graphplan algorithm for generating optimal make-span plans containing parallel sets of actions remains one of the most effective ways to generate such plans. However, despite enhancements on a range of fronts, the approach is currently dominated in terms of speed, by state space planners that employ distance-based heuristics to quickly generate serial plans. We report on a family of strategies that employ available memory to construct a search trace so as to learn from various aspects of Graphplan?s iterative search episodes in order to expedite search in subsequent episodes. The planning approaches can be partitioned into two classes according to the type and extent of search experience captured in the trace. The planners using the more aggressive tracing method are able to avoid much of Graphplan?s redundant search effort, while planners in the second class trade off this aspect in favor of a much higher degree of freedom than Graphplan in traversing the space of 'states' generated during regression search on the planning graph. The tactic favored by the second approach, exploiting the search trace to transform the depth-first, IDA* nature of Graphplan?s search into an iterative state space view, is shown to be the more powerful. We demonstrate that distance-based, state space heuristics can be adapted to informed traversal of the search trace used by the second class of planners and develop an augmentation targeted specifically at planning graph search. Guided by such a heuristic, the step-optimal version of the planner in this class clearly dominates even a highly enhanced version of Graphplan. By adopting beam search on the search trace we then show that virtually optimal parallel plans can be generated at speeds quite competitive with a modern heuristic state space planner.