bluuebunny/crossref_metadata_2025 huggingface dataset:
Waking Up a Sleeping Rabbit: On Natural-Language Sentence Generation with FF
Authors: Koller Alexander , Hoffmann Joerg
Published: September 2022

We present a planning domain that encodes the problem of generating natural language sentences. This domain has a number of features that provoke fairly unusual behavior in planners. In particular, hitherto no existing automated planner was sufficiently effective to be of practical value in this application. We analyze in detail the reasons for ineffectiveness in FF, resulting in a few minor implementation fixes in FF's preprocessor, and in a basic reconfiguration of its search options. The performance of the modified FF is up to several orders of magnitude better than that of the original FF, and for the first time makes automated planners a practical possibility for this application. Beside thus highlighting the importance of preprocessing and automated configuration techniques, we show that the domain still poses several interesting challenges to the development of search heuristics.
Program Committee
Authors: Bacchus Fahiem , Domshlak Carmel , Edelkamp Stefan , Helmert Malte
Published: September 2022

Program committee for the Twenty-First International Conference on Automated Planning and Scheduling, held in Freiburg, Germany, on June 11–16, 2011.
Cost-Sensitive Concurrent Planning Under Duration Uncertainty for Service-Level Agreements
Authors: Coles Andrew , Coles Amanda , Clark Allan , Gilmore Stephen
Published: September 2022

This paper brings together work in stochastic modelling, using the process algebra PEPA, and work in automated planning. Stochastic modelling has been concerned with verification of system performance metrics for some time: given a model of a system, determining whether it will meet a service-level agreement (SLA). For example, whether a given sequence of transitions on a network will complete within 5 seconds 80% of the time. The problem of deciding how to reconfigure the system most cost-effectively when the SLA cannot be met has not been widely explored: it is currently solved manually. Inspired by this, we consider how planning can be used to automate the configuration of service-oriented systems. Configuring these stochastic systems presents new challenges to planning: building plans that meet SLAs, but also have low cost. To this end, we present a domain-independent planner for planning problems with action costs and stochastic durations, and show how this can be used to solve both traditional planning domains, and within the framework of configuring a larger process algebra model.
Scheduling an Aircraft Repair Shop
Authors: Aramon Bajestani Maliheh , Beck J. Christopher
Published: September 2022

We address a scheduling problem in the context of military aircraft maintenance where the goal is to meet the aircraft requirements for a number of missions in the presence of breakdowns. The assignment of aircraft to a mission must consider the requirements for the mission, the probability of aircraft failure, and capacity of the repair shop that maintains the aircraft. Therefore, a solution both assigns aircraft to missions and schedules the repair shop to meet the assignments. We propose a dispatching heuristic algorithm; three complete approaches based on mixed integer programming, constraint programming, and logic-based Benders decomposition; and a hybrid heuristic-complete approach. Experiments demonstrate that the logic-based Benders variation combining mixed integer programming and constraint programming outperforms the other approaches, that the dispatching heuristic can feasibly schedule the repair shop in a very short time, and that using the dispatching solution as a bound marginally improves the complete approaches.
Ensemble Monte-Carlo Planning: An Empirical Study
Authors: Fern Alan , Lewis Paul
Published: September 2022

Monte-Carlo planning algorithms, such as UCT, select actions at each decision epoch by intelligently expanding a single search tree given the available time and then selecting the best root action. Recent work has provided evidence that it can be advantageous to instead construct an ensemble of search trees and to make a decision according to a weighted vote. However, these prior investigations have only considered the application domains of Go and Solitaire and were limited in the scope of ensemble configurations considered. In this paper, we conduct a more exhaustive empirical study of ensemble Monte-Carlo planning using the UCT algorithm in a set of six additional domains. In particular, we evaluate the advantages of a broad set of ensemble configurations in terms of space and time efficiency in both parallel and singlecore models. Our results demonstrate that ensembles are an effective way to improve performance per unit time given a parallel time model and performance per unit space in a single-core model. However, contrary to prior isolated observations, we did not find significant evidence that ensembles improve performance per unit time in a single-core model.
Theoretical Aspects of Scheduling Coupled-Tasks in the Presence of Compatibility Graph
Authors: Simonin Gilles , Giroudeau Rodolphe , König Jean-Claude , Darties Benoit
Published: September 2022

This paper presents a generalization of the coupled-task scheduling problem introduced by Shapiro, where considered tasks are subject to incompatibility constraint depicted by an undirected graph. The motivation of this problem comes from data acquisition and processing in a mono-processor torpedo used for underwater exploration. As we add the compatibility graph, we focus on complexity of the problem, and more precisely on the border between P and NP-completeness when some other input parameters are restricted (e.g. the ratio between the durations of the two sub-tasks composing a task): we adapt the global visualization of the complexity of scheduling problems with coupled-task given by Orman and Potts to our problem, determine new complexity results, and thus propose a new visualization including incompatibility constraint. In the end, we give a new polynomial-time approximation algorithm result which completes previous works.
Exploiting the Computational Power of the Graphics Card: Optimal State Space Planning on the GPU
Authors: Sulewski Damian , Edelkamp Stefan , Kissmann Peter
Published: September 2022

In this paper optimal state space planning is parallelized by exploiting the processing power of a graphics card. The two exploration steps, namely selecting the actions to be applied and generating the successors, are performed on a graphics processing unit. Duplicate detection, however, is delayed to be executed on the central processing unit. Multiple cores are employed to bypass main memory latency. To increase processing speed for exact duplicate detection, the hash tables are lock-free. Moreover, a bucket-based representation enhances the concurrent distribution of frontier states. The planner supports cost-first exploration and is able to deal with a considerable fraction of current PDDL, including numerical state variables, complex objective functions, and goal preferences. It can maximize the net-benefit. Experimental findings show visible performance gains especially for larger benchmark problems.
The Minimal Seed Set Problem
Authors: Gefen Avitan , Brafman Ronen
Published: September 2022

This paper defines and studies a new, interesting, and challenging benchmark problem that originates in systems biology. The minimal seed-set problem is defined as follows: given a description of the metabolic reactions of an organism, characterize the minimal set of nutrients with which it could synthesize all nutrients it is capable of synthesizing. Current methods used in systems biology yield only approximate solutions. And although it is natural to cast it as a planning problem, current optimal planners are unable to solve it, while non-optimal planners return plans that are very far from optimal. As a planning problem, it is inherently delete-free, has many zero-cost actions, all propositions are landmarks, and many legal permutations of the plan exist. We show how a simple uninformed search algorithm that exploits inherent independence between sub-goals can solve it optimally by reducing the branching factor drastically.
An Effective Approach to Realizing Planning Programs
Authors: Gerevini Alfonso , Patrizi Fabio , Saetti Alessandro
Published: September 2022

Planning programs are loose, high-level, declarative representations of the behavior of agents acting in a domain and following a path of goals to achieve. Such programs are specified through transition systems that can include cycles and decisions to make at certain points. We investigate a new effective approach for solving the problem of realizing a planning program, i.e., informally, for finding and combining a collection of plans that guarantee the planning program executability. We focus on deterministic domains and propose a general algorithm that solves the problem exploiting a planning technique handling goal constraints and preferences. A preliminary experimental analysis indicates that our approach dramatically outperforms the existing method based on formal verification and synthesis techniques.
Tractable Monotone Temporal Planning
Authors: Cooper Martin , Maris Frederic , Regnier Pierre
Published: September 2022

This paper describes a polynomially-solvable sub-problem of temporal planning. Polynomiality follows from two assumptions. Firstly, by supposing that each sub-goal fluent can be established by at most one action, we can quickly determine which actions are necessary in any plan. Secondly, the monotonicity of sub-goal fluents allows us to express planning as an instance of STP≠ (Simple Temporal Problem, difference constraints). Our class includes temporally-expressive problems, which we illustrate with an example of chemical process planning.
Using AI Planning to Enhance E-Learning Processes
Authors: Garrido Antonio , Morales Lluvia , Serina Ivan
Published: September 2022

This work describes an approach that automatically extracts standard metadata information from e-learning contents, combines it with the student preferences/goals and creates PDDL planning domains+problems.These PDDL problems can be solved by current planners, although we motivate the use and benefits of case-based planning techniques, to obtain fully tailored learning routes that significantly enhance the learning process. During the execution of a given route, a monitoring phase is used to detect discrepancies, i.e. flaws that prevent the student from continuing with the original plan. In such a situation, an adaptation mechanism becomes necessary to fix the flaws, while also trying to minimise the differences between the original and the new route. We have integrated this approach on top of Moodle and experimented with 100 benchmark problems to evaluate the quality, scalability and viability of the system.
Temporal Planning with Preferences and Time-Dependent Continuous Costs
Authors: Benton J. , Coles Amanda , Coles Andrew
Published: September 2022

Temporal planning methods usually focus on the objective of minimizing makespan. Unfortunately, this misses a large class of planning problems where it is important to consider a wider variety of temporal and non-temporal preferences, making makespan lower-order concern. In this paper we consider modeling and reasoning with plan quality metrics that are not directly correlated with plan makespan, building on the planner POPF. We begin with the preferences defined in PDDL3, and present a mixed integer programming encoding to manage the the interaction between the hard temporal constraints for plan steps, and soft temporal constraints for preferences. To widen the support of metrics that can be expressed directly in PDDL, we then discuss an extension to soft-deadlines with continuous cost functions, avoiding the need to approximate these with several PDDL3 discrete-cost preferences. We demonstrate the success of our new planner on the benchmark temporal planning problems with preferences, showing that it is the state-of-the-art for such problems. We then analyze the benefits of reasoning with continuous (versus discretized) models of domains with continuous cost functions, showing the improvement in solution quality afforded through making the continuous cost function directly available to the planner.
Schedule-Driven Coordination for Real-Time Traffic Network Control
Authors: Xie Xiao-Feng , Smith Stephen , Barlow Gregory
Published: September 2022

Real-time optimization of the dynamic flow of vehicle traffic through a network of signalized intersections is an important practical problem. In this paper, we take a decentralized, schedule-driven coordination approach to address the challenge of achieving scalable network-wide optimization. To be locally effective, each intersection is controlled independently by an on-line scheduling agent. At each decision point, an agent constructs a schedule that optimizes movement of the observable traffic through the intersection, and uses this schedule to determine the best control action to take over the current look-ahead horizon. Decentralized coordination mechanisms, limited to interaction among direct neighbors to ensure scalability, are then layered on top of these asynchronously operating scheduling agents to promote overall performance. As a basic protocol, each agent queries for newly planned output flows from its upstream neighbors to obtain an optimistic projection of future demand. This projection may incorporate non-local influence from indirect neighbors depending on horizon length. Two additional mechanisms are then introduced to dampen ``nervousness'' and dynamic instability in the network, by adjusting locally determined schedules to better align with those of neighbors. We present simulation results on two traffic networks of tightly-coupled intersections that demonstrate the ability of our approach to establish traffic flows with lower average vehicle wait times than both a simple isolated control strategy and other contemporary coordinated control strategies that use moving average forecast or traditional offset calculation.
Incremental Lower Bounds for Additive Cost Planning Problems
Authors: Haslum Patrik
Published: September 2022

We present a novel method for computing increasing lower bounds on the cost of solving planning problems, based on repeatedly solving and strengthening the delete relaxation of the problem. Strengthening is done by compiling select conjunctions into new atoms, similar to the Pm* construction. Because it does not rely on search in the state space, this method does not suffer some of the weaknesses of admissible search algorithms and therefore is able to prove higher lower bounds for many problems that are too hard for optimal planners to solve, thus narrowing the gap between lower bound and cost of the best known plan, providing better assurances of plan quality.
Integrating Vehicle Routing and Motion Planning
Authors: Kiesel Scott , Burns Ethan , Wilt Christopher , Ruml Wheeler
Published: September 2022

There has been much interest recently in problems that com-bine high-level task planning with low-level motion planning.In this paper, we present a problem of this kind that arises inmulti-vehicle mission planning. It tightly integrates task al-location and scheduling, who will do what when, with pathplanning, how each task will actually be performed. It ex-tends classical vehicle routing in that the cost of executing aset of high-level tasks can vary significantly in time and costaccording to the low-level paths selected. It extends classi-cal motion planning in that each path must minimize costwhile also respecting temporal constraints, including thoseimposed by the agent’s other tasks and the tasks assigned toother agents. Furthermore, the problem is a subtask withinan interactive system and therefore must operate within se-vere time constraints. We present an approach to the problembased on a combination of tabu search, linear programming,and heuristic search. We evaluate our planner on represen-tative problem instances and find that its performance meetsthe demanding requirements of our application. These resultsdemonstrate how integrating multiple diverse techniques cansuccessfully solve challenging real-world planning problemsthat are beyond the reach of any single method.
Long-Run Stability in Dynamic Scheduling
Authors: Terekhov Daria , Tran Tony , Down Douglas , Beck J. Christopher
Published: September 2022

Stability analysis consists of identifying conditions under which the number of jobs in a system is guaranteed to remain bounded over time. To date, such long-run performance guarantees have not been available for periodic approaches to dynamic scheduling problems. However, stability has been extensively studied in queueing theory. In this paper, we introduce stability to the dynamic scheduling literature and demonstrate that stability guarantees can be obtained for methods that build the schedule for a dynamic problem by periodically solving static deterministic sub-problems. Specifically, we analyze the stability of two dynamic environments: a two-machine flow shop, which has received significant attention in scheduling research, and a polling system with a flow-shop server, an extension of systems typically considered in queueing. We demonstrate that, among stable policies, methods based on periodic optimization of static schedules may achieve better mean flow times than traditional queueing approaches.
Enhanced Symmetry Breaking in Cost-Optimal Planning as Forward Search
Authors: Domshlak Carmel , Katz Michael , Shleyfman Alexander
Published: September 2022

The paper illustrates a novel approach to conformant planning using classical planners. The approach relies on two core ideas developed to deal with incomplete information in the initial situation: the use of a classical planner to solve non-classical planning problems, and the reduction of the size of the initial belief state. Differently from previous uses of classical planners to solve non-classical planning problems, the approach proposed in this paper creates a valid plan from a possible plan---by inserting actions into the possible plan and maintaining only one level of non-deterministic choice (i.e., the initial plan being modified). The algorithm can be instantiated with different classical planners---the paper presents the GC[LAMA] implementation, whose classical planner is LAMA. We investigate properties of the approach, including conditions for completeness. GC[LAMA] is empirically evaluated against state-of-the-art conformant planners, using benchmarks from the literature. The experimental results show that GC[LAMA] is superior to other planners, in both performance and scalability. GC[LAMA] is the only planner that can solve the largest instances from several domains. The paper investigates the reasons behind the good performance and the challenges encountered in GC[LAMA].
Minimal Landmarks for Optimal Delete-Free Planning
Authors: Haslum Patrik , Slaney John , Thiebaux Sylvie
Published: September 2022

We present a simple and efficient algorithm to solve delete-free planning problems optimally and calculate the h+ heuristic. The algorithm efficiently computes a minimum-cost hitting set for a complete set of disjunctive action landmarks generated on the fly. Unlike other recent approaches, the landmarks it generates are guaranteed to be set-inclusion minimal. In almost all delete-relaxed IPC domains, this leads to a significant coverage and runtime improvement.
Optimally Relaxing Partial-Order Plans with MaxSAT
Authors: Muise Christian , McIlraith Sheila , Beck Christopher
Published: September 2022

Partial-order plans (POPs) are attractive because of their least commitment nature, providing enhanced plan flexibility at execution time relative to sequential plans. Despite the appeal of POPs, most of the recent research on automated plan generation has focused on sequential plans. In this paper we examine the task of POP generation by relaxing or modifying the action orderings of a plan to optimize for plan criteria that promotes flexibility in the POP. Our approach relies on a novel partial weighted MaxSAT encoding of a plan that supports the minimization of deordering or reordering of actions. We further extend the classical least commitment criterion for a POP to consider the number of actions in a solution, and provide an encoding to achieve least commitment plans with respect to this criterion. We compare the effectiveness of our approach to a previous approach for POP generation via sequential-plan relaxation. Our results show that while the previous approach is proficient at heuristically finding the optimal deordering of a plan, our approach gains greater flexibility with the optimal reordering.
Learning Portfolios of Automatically Tuned Planners
Authors: Seipp Jendrik , Braun Manuel , Garimort Johannes , Helmert Malte
Published: September 2022

Portfolio planners and parameter tuning are two ideas that have recently attracted significant attention in the domain-independent planning community. We combine these two ideas and present a portfolio planner that runs automatically configured planners. We let the automatic parameter tuning framework ParamILS find fast configurations of the Fast Downward planning system for a number of planning domains. Afterwards we learn a portfolio of those planner configurations. Evaluation of our portfolio planner on the IPC 2011 domains shows that it has a significantly higher IPC score than the winner of the sequential satisficing track.
Constricting Insertion Heuristic for Traveling Salesman Problem with Neighborhoods
Authors: Alatartsev Sergey , Augustine Marcus , Ortmeier Frank
Published: September 2022

Sequence optimization is an important problem in many production automation scenarios involving industrial robots. Mostly, this is done by reducing it to Traveling Salesman Problem (TSP). However, in many industrial scenarios optimization potential is not only hidden in optimizing a sequence of operations but also in optimizing the individual operations themselves. From a formal point of view, this leads to the Traveling Salesman Problem with Neighborhoods (TSPN). TSPN is a generalization of TSP where areas should be visited instead of points. In this paper we propose a new method for solving TSPN efficiently. We compare the new method to the related approaches using existing test benchmarks from the literature. According to the evaluation on instances with known optimal values, our method is able to obtain a solution close to the optimum.
Fault Tolerant Planning: Complexity and Compilation
Authors: Domshlak Carmel
Published: September 2022

In the context of modeling and reasoning about agent actions, contingent and classical planning can often be respectively seen as adopting ``extreme pessimism'' and ``extreme optimism'' about the action outcomes. For many  everyday scenarios of human reasoning (and thus for many types of autonomous systems), both these approaches are just too extreme. Following Jensen, Veloso, and Bryant (2004), we examine a planning model that interpolates between classical and contingent planning via tolerance to arbitrary k faults occurring during plan execution.  We show that an important fragment of this fault tolerant planning (FT-planning) exhibits both an appealing solution structure, as well as appealing worst-case time-complexity properties. We also show that  such FT-planning tasks can be efficiently compiled into  classical planning as long as the number of possible faults per operator is bounded by a constant, and we show that this compilation can be attractive in practice.
Who Said We Need to Relax All Variables?
Authors: Katz Michael , Hoffmann Joerg , Domshlak Carmel
Published: September 2022

Despite its success in both satisficing and optimal planning, thedelete relaxation has significant pitfalls in many important classesof planning domains, and it has been a challenge from the outset todevise heuristics that take some deletes into account. Weherein devise an elegant and simple method for doing just that. In thecontext of finite-domain state variables, we define red variables to take the relaxed semantics, in which they accumulate their values rather than switching between them, as opposed to black variables that take the regular semantics. Red-black planning then interpolates between relaxed planning and regular planning simply by allowing a subset of variables to be painted red. Of course, this relaxation is useful as a basis for devising heuristic functions only if the resulting red-black planning task is polynomial-time solvable. We herein investigate the tractability region of red-black planning, extending Chen and Gimenez' characterization theorems for regular planning to the more generalred-black setting. In particular, we identify significant islands of tractable red-black planning, opening the road to the efficient computation of very powerful heuristics.
Using Alternative Suboptimality Bounds in Heuristic Search
Authors: Valenzano Richard , Jabbari Arfaee Shahab , Thayer Jordan , Stern Roni , Sturtevant Nathan
Published: September 2022

Most bounded suboptimal algorithms in the search literature have been developed so as to be epsilon-admissible. This means that the solutions found by these algorithms are guaranteed to be no more than a factor of (1 + ε) greater than optimal. However, this is not the only possible form of suboptimality bounding. For example, another possible suboptimality guarantee is that of additive bounding, which requires that the cost of the solution found is no more than the cost of the optimal solution plus a constant gamma.In this work, we consider the problem of developing algorithms so as to satisfy a given, and arbitrary, suboptimality requirement. To do so, we develop a theoretical framework which can be used to construct algorithms for a large class of possible suboptimality paradigms. We then use the framework to develop additively bounded algorithms, and show that in practice these new algorithms effectively trade-off additive solution suboptimality for runtime.
Task Sequencing for Remote Laser Welding in the Automotive Industry
Authors: Kovács András
Published: September 2022

This paper proposes a new model and algorithm for task sequencing in remote laser welding in the automotive industry. It is shown that task sequencing (in which order to weld the seams) is strongly related to path planning (how the welding robot should move), therefore the two problems must be solved together, in an integrated way. The problem is modeled as a direct product of a traveling salesman and a path planning problem, and a tabu search algorithm is proposed for solving it. Computational experiments show that the proposed method leads to a substantial reduction in the cycle time of the welding operation compared to an earlier approach.
Planning Spacecraft Activities: An Automated Approach
Authors: Policella Nicola , Oliveira Henrique , Benzi Edoardo
Published: September 2022

In 2013 the Alphasat spacecraft will be launched: in addition to its main commercial payload, four Technology Demonstration Payloads (TDPs) will fly on-board. The different payloads are provided by different research institutes, which will be able to define in-orbit demonstration tests of these new technologies. To optimize this opportunity, coordination of the different, possibly conflicting, payload operations is required. A software system to support the management of the TDP operations has been developed. While this system is intended to be completely automated (i.e., without any human intervention in the nominal case), it has been designed to keep all the different users (system and TDP operators) in the loop. This paper presents this system and in particular focuses on its core: the planning engine.
What’s in It for My BDD? On Causal Graphs and Variable Orders in Planning
Authors: Kissmann Peter , Hoffmann Jörg
Published: September 2022

One decisive factor for the success of symbolic search using BDDs is whether or not the variable ordering is good. A general intuition is that smaller BDDs result if inter-dependent variables are close together. The most common means to capture variable dependencies in planning are causal graphs, and consequently previous work defined variable orders based on these. Starting from the observation that the two concepts of “dependency” are actually quite different, we introduce a framework for assessing the strength of variable ordering heuristics in sub-classes of planning. It turns out that causal graph based variable orders may be exponentially worse than optimal even for very simple planning tasks. Experiments with a broad range of such variable ordering variants indicate that they are mediocre at best.
Stronger Abstraction Heuristics Through Perimeter Search
Authors: Eyerich Patrick , Helmert Malte
Published: September 2022

Perimeter search is a bidirectional search algorithm consisting of two phases. In the first phase, a limited regression search computes the perimeter, a region which must necessarily be passed in every solution. In the second phase, a heuristic forward search finds an optimal plan from the initial state to the perimeter. The drawback of perimeter search is the need to compute heuristic estimates towards every state on the perimeter in the forward phase. We show that this limitation can be effectively overcome when using pattern database (PDB) heuristics in the forward phase. The combination of perimeter search and PDB heuristics has been considered previously by Felner and Ofek for solving combinatorial puzzles. They claimed that, based on theoretical considerations and experimental evidence, the use of perimeter search in this context offers "limited or no benefits". Our theoretical and experimental results show that this assessment should be revisited.
An Optimal Any-Angle Pathfinding Algorithm
Authors: Harabor Daniel , Grastien Alban
Published: September 2022

Any-angle pathfinding is a common problem from robotics and computer games: it requires finding a Euclidean shortest path between a pair of points in a grid map. Prior research has focused on approximate online solutions. A number of exact methods exist but they all require supra-linear space and preprocessing time. In this paper we describe Anya: a new optimal any-angle pathfinding algorithm which searches over sets of states represented as intervals. Each interval is identified online. From each we select a representative point to derive a corresponding f-value for the set. Anya always returns an optimal path. Moreover it does so entirely online, without any preprocessing or memory overheads. This result answers an open question from the areas of Artificial Intelligence and Game Development: is there an any-angle pathfinding algorithm which is online and optimal? The answer is yes.
Revisiting Risk-Sensitive MDPs: New Algorithms and Results
Authors: Hou Ping , Yeoh William , Varakantham Pradeep
Published: September 2022

While Markov Decision Processes (MDPs) have been shown to be effective models for planning under uncertainty, the objective to minimize the expected cumulative cost is inappropriate for high-stake planning problems. As such, Yu, Lin, and Yan (1998) introduced the Risk-Sensitive MDP (RS-MDP) model, where the objective is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold. In this paper, we revisit this problem and introduce new algorithms that are based on classical techniques, such as depth-first search and dynamic programming, and a recently introduced technique called Topological Value Iteration (TVI). We demonstrate the applicability of our approach on randomly generated MDPs as well as domains from the ICAPS 2011 International Probabilistic Planning Competition (IPPC).
Thompson Sampling Based Monte-Carlo Planning in POMDPs
Authors: Bai Aijun , Wu Feng , Zhang Zongzhang , Chen Xiaoping
Published: September 2022

Monte-Carlo tree search (MCTS) has been drawing great interest in recent years for planning under uncertainty. One of the key challenges is the trade-off between exploration and exploitation. To address this, we introduce a novel online planning algorithm for large POMDPs using Thompson sampling based MCTS that balances between cumulative and simple regrets. The proposed algorithm Dirichlet-Dirichlet-NormalGamma based Partially Observable Monte-Carlo Planning (D2NG-POMCP) treats the accumulated reward of performing an action from a belief state in the MCTS search tree as a random variable following an unknown distribution with hidden parameters. Bayesian method is used to model and infer the posterior distribution of these parameters by choosing the conjugate prior in the form of a combination of two Dirichlet and one NormalGamma distributions. Thompson sampling is exploited to guide the action selection in the search tree. Experimental results confirmed that our algorithm outperforms the state-of-the-art approaches on several common benchmark problems.
Optimization Model and Heuristic Approach for Blocks Retrieval Processes in Warehouses
Authors: Expósito-Izquierdo Christopher , Melián-Batista Belén , Moreno-Vega José Marcos
Published: September 2022

In this paper we introduce a planning problem termed as Q-Blocks Relocation Problem, which pursues to retrieve a subset of blocks located in a warehouse by minimizing the number of relocation movements. We formalize the problem by means of a Mixed Integer Linear Programming model. However, the high computational burden required by the model encourages us to develop a heuristic algorithm for tackling it. The rationale behind the proposed heuristic is both to retrieve the requested blocks as soon as possible while reducing the number of blocks placed above another one with a higher priority. The computational results indicate that the heuristic reports near-optimal solutions for realistic instances by short computational times, which makes it attractive to be applied by management systems.
Planning Under Uncertainty Using Reduced Models: Revisiting Determinization
Authors: Pineda Luis , Zilberstein Shlomo
Published: September 2022

We introduce a family of MDP reduced models characterized by two parameters: the maximum number of primary outcomes per action that are fully accounted for and the maximum number of occurrences of the remaining exceptional outcomes that are planned for in advance. Reduced models can be solved much faster using heuristic search algorithms such as LAO*, benefiting from the dramatic reduction in the number of reachable states. A commonly used determinization approach is a special case of this family of reductions, with one primary outcome per action and zero exceptional outcomes per plan. We present a framework to compute the benefits of planning with reduced models, relying on online planning when the number of exceptional outcomes exceeds the bound. Using this framework, we compare the performance of various reduced models and consider the challenge of generating good ones automatically. We show that each one of the dimensions---allowing more than one primary outcome or planning for some limited number of exceptions---could improve performance relative to standard determinization. The results place recent work on determinization in a broader context and lay the foundation for efficient and systematic exploration of the space of MDP model reductions.
Symbolic and Explicit Search Hybrid through Perfect Hash Functions — A Case Study in Connect Four
Authors: Edelkamp Stefan , Kissmann Peter , Rohte Martha
Published: September 2022

This work combines recent advances in AI planning under memory limitation, namely bitvector and symbolic search. Bitvector search assumes a bijective mapping between state and memory addresses, while symbolic search compactly represents state sets. The memory requirements vary with the structure of the problem to be solved. The integration of the two algorithms into one hybrid algorithm for strongly solving general games initiates a BDD-based solving algorithm, which consists of a forward computation of the reachable state set, possibly followed by a layered backward retrograde analysis. If the main memory becomes exhausted, it switches to explicit-state two-bit retrograde search. We use the classical game of Connect Four as a case study, and solve some instances of the problem space-efficiently with the proposed hybrid search algorithm.
Diverse and Additive Cartesian Abstraction Heuristics
Authors: Seipp Jendrik , Helmert Malte
Published: September 2022

We have recently shown how counterexample-guided abstraction refinement can be used to derive informative Cartesian abstraction heuristics for optimal classical planning. In this work we introduce two methods for producing diverse sets of heuristics within this framework, one based on goal facts, the other based on landmarks. In order to sum the heuristic estimates admissibly we present a novel way of finding cost partitionings for explicitly represented abstraction heuristics. We show that the resulting heuristics outperform other state-of-the-art abstraction heuristics on many benchmark domains.
The Complexity of Partial-Order Plan Viability Problems
Authors: Tan Xing , Gruninger Michael
Published: September 2022

Estimating the distance from a current partial-order plan to the goal state of the plan task is a challenging problem, with past research achieving only limited success. In an effort to understand the reasons for this situation, we investigate the computational complexity of the partial-order plan viability problem. We define several boundaries between the tractable and intractable subclasses of the problem, from which we identify several constraints that contribute to the computational intractability of the problem. These results bring new insights into the design and the development of future  partial-order planning heuristics.
Bounded Approximations for Linear Multi-Objective Planning Under Uncertainty
Authors: Roijers Diederik , Scharpff Joris , Spaan Matthijs , Oliehoek Frans , De Weerdt Mathijs , Whiteson Shimon
Published: September 2022

Planning under uncertainty poses a complex problem in which multiple objectives often need to be balanced. When dealing with multiple objectives, it is often assumed that the relative importance of the objectives is known a priori. However, in practice human decision makers often find it hard to specify such preferences, and would prefer a decision support system that presents a range of possible alternatives. We propose two algorithms for computing these alternatives for the case of linearly weighted objectives. First, we propose an anytime method, approximate optimistic linear support (AOLS), that incrementally builds up a complete set of ε-optimal plans, exploiting the piecewise linear and convex shape of the value function. Second, we propose an approximate anytime method, scalarised sample incremental improvement (SSII), that employs weight sampling to focus on the most interesting regions in weight space, as suggested by a prior over preferences. We show empirically that our methods are able to produce (near-)optimal alternative sets orders of magnitude faster than existing techniques.
A Novel Priority Rule Heuristic: Learning from Justification
Authors: De Nijs Frits , Klos Tomas
Published: September 2022

The Resource Constrained Project Scheduling Problem consists of finding start times for precedence-constrained activities which compete over renewable resources, with the goal to produce the shortest schedule. The method of Justification is a very popular post-processing schedule optimization technique which, although it is not clear exactly why, has been shown to work very well, even improving randomly generated schedules over those produced by advanced heuristics. In this paper, we set out to investigate why Justification works so well, and, with this understanding, to bypass the need for Justification by computing a priori the priorities Justification implicitly employs. We perform an exploratory study to investigate the effectiveness of Justification on a novel test set which varies the RCPSP phase-transition parameters across a larger range than existing test sets. We propose several hypotheses to explain the behavior of Justification, which we test by deriving from them several predictions, and a new priority rule. We show that this rule matches the priorities used by Justification more closely than existing rules, making it outperform the most successful priority rule heuristic.
Optimal Planning with Global Numerical State Constraints
Authors: Ivankovic Franc , Haslum Patrik , Thiebaux Sylvie , Shivashankar Vikas , Nau Dana
Published: September 2022

Automating the operations of infrastructure networks such as energy grids and oil pipelines requires a range of planning and optimisation technologies. However, current planners face significant challenges in responding to this need. Notably, they are unable to model and reason about the global numerical state constraints necessary to capture flows and similar physical phenomena occurring in these networks. A single discrete control action can affect the flow throughout the network in a way that may depend on the entire network topology. Determining whether preconditions, goals and invariant conditions are satisfied requires solving a system of numerical constraints after each action application. This paper extends domain-independent optimal planning to this kind of reasoning. We present extensions of the formalism, relaxed plans, and heuristics, as well as new search variants and experimental results on two problem domains.
Heuristic Evaluation Based on Lifted Relaxed Planning Graphs
Authors: Ridder Bram , Fox Maria
Published: September 2022

In previous work we have shown that grounding, while used by most (if not all) modern state-of-the-art planners, is not necessary and is sometimes even undesirable. In this paper we extend this work and present a novel forward-chaining planner that does not require grounding and can solve problem instances that are too large for current planners to handle. We achieve this by exploiting equivalence relationships between objects whist constructing a lifted version of the relaxed planning graph (RPG) and extracting a relaxed plan. We compare our planner to FF and show that our approach consumes far less memory whist still being competitive. In addition we show that by not having to ground the domain we can solve much larger problem instances.
Satellite Data Download Management with Uncertainty about the Generated Volumes
Authors: Pralet Cédric , Verfaillie Gérard , Maillard Adrien , Hébrard Emmanuel , Jozefowiez Nicolas , Huguet Marie-Jo , Desmousceaux Thierry , Blanc-Paques Pierre , Jaubert Jean
Published: September 2022

Earth observation satellites are space sensors which acquire data, compress and record it on board, and then download it to the ground. Because of the use of more and more sophisticated compression algorithms, the amount of data resulting from an acquisition is more and more unpredictable. In such conditions, planning satellite data download activities offline on the ground is more and more problematic. In this paper, we report the results of a work aiming at evaluating the positive impact of planning downloads onboard when the amount of data produced by each acquisition is known.The data download problem to be solved on board is an assignment and scheduling problem with unsharable resources, precedence constraints, time-dependent minimum durations, and a complex optimization criterion. The generic InCELL library is used to model constraints and criterion, to check non temporal constraints, to propagate temporal constraints, and to evaluate the criterion. On top of this library, greedy and local search algorithms have been designed to produce download plans with limited time and computing resources available on board.
Automated Planning for Multi-Objective Machine Tool Calibration: Optimising Makespan and Measurement Uncertainty
Authors: Parkinson Simon , Gregory Peter , Longstaff Andrew , Crampton Andrew
Published: September 2022

The evolution in precision manufacturing has resulted in the requirement to produce and maintain more accurate machine tools. This new requirement coupled with desire to reduce machine tool downtime places emphasis on the calibration procedure during which the machine’s capabilities are assessed. Machine tool downtime is significant for manufacturers because the machine will be unavailable for manufacturing use, therefore wasting the manufacturer’s time and potentially increasing lead-times for clients. In addition to machine tool downtime, the uncertainty of measurement, due to the schedule of the calibration plan, has significant implications on tolerance conformance, resulting in an increased possibility of false acceptance and rejection of machined parts. The work presented in this paper is focussed on expanding a developed temporal optimisation model to reduce the uncertainty of measurement. Encoding the knowledge in regular PDDL requires the discretization of non-linear, continuous temperature change and implementing the square root function. The implementation shows that not only can domainindependent automated planning reduce machine downtime by 10.6% and the uncertainty of measurement by 59%, it is also possible to optimise both metrics reaching a compromise that is on average 9% worse that the best-known solution for each individual metric.
A Robotic Execution Framework for Online Probabilistic (Re)Planning
Authors: Chanel Caroline , Lesire Charles , Teichteil-Königsbuch Florent
Published: September 2022

Due to the high complexity of probabilistic planning algorithms, roboticists often opt for deterministic replanning paradigms, which can quickly adapt the current plan to the environment's changes. However, probabilistic planning suffers in practice from the common misconception that it is needed to generate complete or closed policies, which would not require to be adapted on-line. In this work, we propose an intermediate approach, which generates incomplete partial policies taking into account mid-term probabilistic uncertainties, continually improving them on a gliding horizon or regenerating them when they fail. Our algorithm is a configurable anytime meta-planner that drives any sub-(PO)MDP standard planner, dealing with all pending and time-bounded planning requests sent by the execution framework from many reachable possible future execution states, in anticipation of the probabilistic evolution of the system. We assess our approach on generic robotic problems and on combinatorial UAVs (PO)MDP missions, which we tested during real flights: emergency landing with discrete and continuous state variables, and target detection and recognition in unknown environments.
Making A* Run Faster than D*-Lite for Path-Planning in Partially Known Terrain
Authors: Hernandez Carlos , Baier Jorge , Asin Roberto
Published: September 2022

Focused D* and D*-Lite are two popular incremental heuristic search algorithm amenable to goal-directed navigation in partially known terrain. Recently it has been shown that, unlike commonly believed, a version of A* is in many cases faster than D*-Lite, posing the question of whether or not there exist other variants of A* which could outperform algorithms in the D* family on most problems. In this paper we present Multipath Adaptive A* (MPAA*), a simple, easy-to-implement modification of Adaptive A* (AA*) that reuses paths found in previous searches to speed up subsequent searches, and that almost always outperforms D*Lite. We evaluate MPAA* against D*-Lite on random maps and standard game, room, and maze maps, assuming partially known terrain. In environments comparable to indoor and outdoor navigation (room and game maps) MPAA* is 35% faster than D*Lite on average, while on random maps MPAA* is over 3 times faster than D*Lite. D*Lite is faster than MPAA* only in mazes; notwithstanding, we show that if a small percentage of obstacle cells in a maze are made traversable, MPAA* outperforms D*Lite. In addition, we prove MPAA* is optimal and that it finds a solution if one exists. We conclude that for most real-life goal-directed navigation applications MPAA* should be preferred to D*Lite.