Efficient Implementation of the Plan Graph in STAN

STAN is a Graphplan-based planner, so-called because it uses a variety of STate ANalysis techniques to enhance its performance. STAN competed in the AIPS-98 planning competition where it compared well with the other competitors in terms of speed, finding solutions fastest to many of the problems posed. Although the domain analysis techniques STAN exploits are an important factor in its overall performance, we believe that the speed at which STAN solved the competition problems is largely due to the implementation of its plan graph. The implementation is based on two insights: that many of the graph construction operations can be implemented as bit-level logical operations on bit vectors, and that the graph should not be explicitly constructed beyond the fix point. This paper describes the implementation of STAN's plan graph and provides experimental results which demonstrate the circumstances under which advantages can be obtained from using this implementation

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

Plan permutation symmetries as a source of inefficiency in planning

This paper briefly reviews sources of symmetry in planning and highlights one source that has not previously been tackled: plan permutation symmetry. Symmetries can be a significant problem for efficiency of planning systems, as has been previously observed in the treatment of other forms of symmetry in planning problems. We examine how plan permutation symmetries can be eliminated and present evidence to support the claim that these symmetries are an important problem for planning systems

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

Marvin: A Heuristic Search Planner with Online Macro-Action Learning

This paper describes Marvin, a planner that competed in the Fourth International Planning Competition (IPC 4). Marvin uses action-sequence-memoisation techniques to generate macro-actions, which are then used during search for a solution plan. We provide an overview of its architecture and search behaviour, detailing the algorithms used. We also empirically demonstrate the effectiveness of its features in various planning domains; in particular, the effects on performance due to the use of macro-actions, the novel features of its search behaviour, and the native support of ADL and Derived Predicates

Taming Numbers and Durations in the Model Checking Integrated Planning System

The Model Checking Integrated Planning System (MIPS) is a temporal least commitment heuristic search planner based on a flexible object-oriented workbench architecture. Its design clearly separates explicit and symbolic directed exploration algorithms from the set of on-line and off-line computed estimates and associated data structures. MIPS has shown distinguished performance in the last two international planning competitions. In the last event the description language was extended from pure propositional planning to include numerical state variables, action durations, and plan quality objective functions. Plans were no longer sequences of actions but time-stamped schedules. As a participant of the fully automated track of the competition, MIPS has proven to be a general system; in each track and every benchmark domain it efficiently computed plans of remarkable quality. This article introduces and analyzes the most important algorithmic novelties that were necessary to tackle the new layers of expressiveness in the benchmark problems and to achieve a high level of performance. The extensions include critical path analysis of sequentially generated plans to generate corresponding optimal parallel plans. The linear time algorithm to compute the parallel plan bypasses known NP hardness results for partial ordering by scheduling plans with respect to the set of actions and the imposed precedence relations. The efficiency of this algorithm also allows us to improve the exploration guidance: for each encountered planning state the corresponding approximate sequential plan is scheduled. One major strength of MIPS is its static analysis phase that grounds and simplifies parameterized predicates, functions and operators, that infers knowledge to minimize the state description length, and that detects domain object symmetries. The latter aspect is analyzed in detail. MIPS has been developed to serve as a complete and optimal state space planner, with admissible estimates, exploration engines and branching cuts. In the competition version, however, certain performance compromises had to be made, including floating point arithmetic, weighted heuristic search exploration according to an inadmissible estimate and parameterized optimization

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

AltAltp: Online Parallelization of Plans with Heuristic State Search

Despite their near dominance, heuristic state search planners still lag behind disjunctive planners in the generation of parallel plans in classical planning. The reason is that directly searching for parallel solutions in state space planners would require the planners to branch on all possible subsets of parallel actions, thus increasing the branching factor exponentially. We present a variant of our heuristic state search planner AltAlt, called AltAltp which generates parallel plans by using greedy online parallelization of partial plans. The greedy approach is significantly informed by the use of novel distance heuristics that AltAltp derives from a graphplan-style planning graph for the problem. While this approach is not guaranteed to provide optimal parallel plans, empirical results show that AltAltp is capable of generating good quality parallel plans at a fraction of the cost incurred by the disjunctive planners

DSHOP: Distributed simple hierarchical ordered planner.

Planning has been an important subject in the area of Artificial Intelligence (AI) for over three decades. Planning is the problem of seeking a series of actions (that is, a plan) that will accomplish a desired goal. Most planning approaches rely on a single processor or a single-agent paradigm. Unfortunately, in a complex world, a single agent may not be sufficient to optimally solve the problem. Distributed Planning is a sub-field of Distributed AI that involves multi-agents working together to solve large planning problems. Distribution may speed up the traditional planning system through parallelism. Hierarchical Task Network (HTN) planning is an AI planning methodology that creates plans by task decomposition. SHOP (Simple Hierarchical Ordered Planner) is a domain-independent HTN planning system designed by Dana Nau et al. that plans for tasks in the same order that they will later be executed. This thesis aims at designing and implementing a distributed version of SHOP (that is, DSHOP) and running it on a high performance distributed system called SHARCNET. The implementation is based upon Message Passing Interface (MPI), that is, a library of functions used to achieve parallelism via message-passing. We investigate two approaches to share work between processors: state-copying and state-recomputation. We implemented a state-copying based DSHOP system (DSHOPC), and a state-recomputation based DSHOP system (DSHOPR). We compared these two implementations of DSHOP with the Java version of SHOP on a set of randomly generated artificial domains. A set of experimental results has been used to evaluate the performance of the DSHOP algorithm.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .L83. Source: Masters Abstracts International, Volume: 43-01, page: 0240. Advisers: Scott Goodwin; Froduald Kabanza. Thesis (M.Sc.)--University of Windsor (Canada), 2004

A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making

The field of Sequential Decision Making (SDM) provides tools for solving Sequential Decision Processes (SDPs), where an agent must make a series of decisions in order to complete a task or achieve a goal. Historically, two competing SDM paradigms have view for supremacy. Automated Planning (AP) proposes to solve SDPs by performing a reasoning process over a model of the world, often represented symbolically. Conversely, Reinforcement Learning (RL) proposes to learn the solution of the SDP from data, without a world model, and represent the learned knowledge subsymbolically. In the spirit of reconciliation, we provide a review of symbolic, subsymbolic and hybrid methods for SDM. We cover both methods for solving SDPs (e.g., AP, RL and techniques that learn to plan) and for learning aspects of their structure (e.g., world models, state invariants and landmarks). To the best of our knowledge, no other review in the field provides the same scope. As an additional contribution, we discuss what properties an ideal method for SDM should exhibit and argue that neurosymbolic AI is the current approach which most closely resembles this ideal method. Finally, we outline several proposals to advance the field of SDM via the integration of symbolic and subsymbolic AI