bluuebunny/crossref_metadata_2025 huggingface dataset:
MA3: Model-Accuracy Aware Anytime Planning with Simulation Verification for Navigating Complex Terrains
Authors: Das Manash Pratim , Conover Damon M , Eum Sungmin , Kwon Heesung , Likhachev Maxim
Published: November 2022

Off-road and unstructured environments often contain complex patches of various types of terrain, rough elevation changes, deformable objects, etc. An autonomous ground vehicle traversing such environments experiences physical interactions that are extremely hard to model at scale and thus very hard to predict. Nevertheless, planning a safely traversable path through such an environment requires the ability to predict the outcomes of these interactions instead of avoiding them. One approach to doing this is to learn the interaction model offline based on collected data. Unfortunately, though, this requires large amounts of data and can often be brittle. Alternatively, models using physics-based simulators can generate large data and provide a reliable prediction. However, they are very slow to query online within the planning loop. This work proposes an algorithmic framework that utilizes the combination of a learned model and a physics-based simulation model for fast planning. Specifically, it uses the learned model as much as possible to accelerate planning while sparsely using the physics-based simulator to verify the feasibility of the planned path. We provide a theoretical analysis of the algorithm and its empirical evaluation showing a significant reduction in planning times.
On Bidirectional Heuristic Search in Classical Planning: An Analysis of BAE*
Authors: Hu Kilian , Speck David
Published: November 2022

Heuristic search is a successful approach to cost-optimal planning. Bidirectional heuristic search algorithms have been around for a long time, but only recent advances have led to algorithms like BAE* that have the potential to outperform unidirectional heuristic search algorithms like A* in practice. In this work, we analyze BAE* for classical planning and the challenges associated with the underlying assumption of an explicit state representation. We show that it is crucial to use mutexes and reachability analysis to reduce the potentially exponential number of goal states, which makes it possible to create an explicit representation of a reversed planning task that can be used for the backward search of BAE*. Our empirical evaluation shows that BAE* solves more instances than A* in multiple domains with significantly fewer node expansions, demonstrating the usefulness of BAE* in planning.
Heuristic Search for SSPs with Lexicographic Preferences over Multiple Costs
Authors: Miura Shuwa , Hollins Wray Kyle , Zilberstein Shlomo
Published: November 2022

Real-world decision problems often involve multiple competing objectives. The Stochastic Shortest Path (SSP) with lexicographic preferences over multiple costs offers an expressive formulation for many practical problems. However, the existing solution methods either lack optimality guarantees or require costly computations over the entire state space. We propose the first heuristic algorithm for this problem, based on the heuristic algorithm for Constrained SSPs. Our experiments show that our heuristic search algorithm can compute optimal policies while avoiding a large portion of the state space. We further analyze the theoretical properties of the problem, showing the conditions under which SSPs with lexicographic preferences have a proper optimal policy.
ePA*SE: Edge-Based Parallel A* for Slow Evaluations
Authors: Mukherjee Shohin , Aine Sandip , Likhachev Maxim
Published: November 2022

Parallel search algorithms harness the multithreading capability of modern processors to achieve faster planning. One such algorithm is PA*SE (Parallel A* for Slow Expansions), which parallelizes state expansions to achieve faster planning in domains where state expansions are slow. In this work, we propose ePA*SE (Edge-Based Parallel A* for Slow Evaluations) that improves on PA*SE by parallelizing edge evaluations instead of state expansions. This makes ePA*SE more efficient in domains where edge evaluations are expensive and need varying amounts of computational effort, which is often the case in robotics. On the theoretical front, we show that ePA*SE provides rigorous optimality guarantees. In addition, ePA*SE can be trivially extended to handle an inflation weight on the heuristic resulting in a bounded suboptimal algorithm w-ePA*SE (Weighted ePA*SE) that trades off optimality for faster planning. On the experimental front, we validate the proposed algorithm in two different planning domains: 1) motion planning for 3D humanoid navigation and 2) task and motion planning for a dual-arm robotic assembly task. We show that ePA*SE can be significantly more efficient than PA*SE and other alternatives. The open-source code for ePA*SE along with the baselines is available here: https://github.com/shohinm/parallel_search
Scaling-Up Generalized Planning as Heuristic Search with Landmarks
Authors: Segovia-Aguas Javier , Jiménez Celorrio Sergio , Sebastiá Laura , Jonsson Anders
Published: November 2022

Landmarks are one of the most effective search heuristics for classical planning, but largely ignored in generalized planning. Generalized planning (GP) is usually addressed as a combinatorial search in a given space of algorithmic solutions, where candidate solutions are evaluated w.r.t. the instances they solve. This type of solution evaluation ignores any sub-goal information that is not explicit in the representation of the planning instances, causing plateaus in the space of candidate generalized plans. Furthermore, node expansion in GP is a run-time bottleneck since it requires evaluating every child node over the entire batch of classical planning instances in a GP problem. In this paper we define a landmark counting heuristic for GP (that considers sub-goal information that is not explicitly represented in the planning instances), and a novel heuristic search algorithm for GP (that we call PGP) and that progressively processes subsets of the planning instances of a GP problem. Our two orthogonal contributions are analyzed in an ablation study, showing that both improve the state-of-the-art in GP as heuristic search, and that both benefit from each other when used in combination.
Light Contraction Hierarchies: Hierarchical Search Without Shortcuts
Authors: Proissl Claudius
Published: November 2022

Hierarchical search such as Contraction Hierarchies is a popular and successful branch of optimization techniques for shortest path computation. Existing hierarchical techniques have one component in common: they add edges to the graph, so called shortcuts. This component usually causes a considerable space overhead but is mandatory in order to preserve correctness. In this work we show a hierarchical method that requires to store only one additional number per node and no shortcuts at all. We prove the correctness of our method and experimentally show that it improves query times by one order of magnitude compared to Dijkstra's bidirectional algorithm.
Bounded-Cost Bi-Objective Heuristic Search
Authors: Skyler Shawn , Atzmon Dor , Felner Ariel , Salzman Oren , Zhang Han , Koenig Sven , Yeoh William , Hernández Ulloa Carlos
Published: November 2022

There are many settings that extend the basic shortest path search problem. In Bounded-Cost Search, we are given a constant bound and the task is to find a solution within the bound. In Bi-Objective Search, each edge is associated with two costs (objectives) and the task is to minimize both objectives. In this paper, we combine both these settings into a new setting of Bounded-Cost Bi-Objective Search. We are given two bounds, one for each objective and the task is to find a solution within these bounds. We provide a scheme for normalizing the two objectives. We then introduce several algorithms for this new setting and compare them experimentally.
Informed Steiner Trees: Sampling and Pruning for Multi-Goal Path Finding in High Dimensions (Extended Abstract)
Authors: Chandak Nikhil , Chour Kenny , Rathinam Sivakumar , Ravi R.
Published: November 2022

We interleave sampling based motion planning methods with pruning ideas from minimum spanning tree algorithms to develop a new approach for solving a Multi-Goal Path Finding (MGPF) problem in high dimensional spaces. The approach alternates between sampling points from selected regions in the search space and de-emphasizing regions that may not lead to good solutions for MGPF. Our approach provides an asymptotic, 2-approximation guarantee for MGPF. We also present extensive numerical results to illustrate the advantages of our proposed approach over uniform sampling in terms of the quality of the solutions found and computation speed.
Fast Traffic Assignment by Focusing on Changing Edge Flows (Extended Abstract)
Authors: Davoodi Ali , Wallace Mark , Harabor Daniel
Published: November 2022

This paper presents a novel algorithm for solving the traffic assignment problem (TAP). Contrary to traditional algorithms, which use the one-to-all shortest path algorithm to solve the problem for all origin destinations (OD) pairs, this algorithm tracks the changes of the edges and (at certain iterations) solves the problem only for critical edges whose flows have changed substantially using a state-of-the-art edge p2p shortest path algorithm. When additionally, only OD pairs with larger flows are considered, this enhancement halves the time needed to optimize the solution with a very small error in a large-scale network.
Focal Discrepancy Search for Learned Heuristics (Extended Abstract)
Authors: Greco Matias , Araneda Pablo , Baier Jorge A.
Published: November 2022

Machine learning allows learning accurate but inadmissible heuristics for hard combinatorial puzzles like the 15-puzzle, the 24-puzzle, and Rubik's cube.
In this paper, we investigate how to exploit these learned heuristics in the context of heuristic search with suboptimality guarantees. Specifically, we study how Focal Search (FS), a well-known bounded-suboptimal search algorithm can be modified to better exploit inadmissible learned heuristics. We propose to use Focal Discrepancy Search (FDS) in the context of learned heuristics, which uses a discrepancy function, instead of the learned heuristic, to sort the focal list. In our empirical evaluation, we evaluate FS and FDS using DeepCubeA, an effective learned heuristic for the 15-puzzle. We show that FDS substantially outperforms FS.
This suggests that in some domains, when a highly accurate heuristics is available, one should always consider using discrepancies for better search.
Joint Chance Constrained Probabilistic Simple Temporal Networks via Column Generation (Extended Abstract)
Authors: Murray Andrew , Cashmore Michael , Arulselvan Ashwin , Frank Jeremy
Published: November 2022

Probabilistic Simple Temporal Networks (PSTN) are used to represent scheduling problems under uncertainty. In a temporal network that is Strongly Controllable (SC) there exists a concrete schedule that is robust to any uncertainty. We solve the problem of determining Chance Constrained PSTN SC as a Joint Chance Constrained optimisation problem via column generation, lifting the usual assumptions of independence and Boole's inequality typically leveraged in PSTN literature. Our approach offers on average a 10 times reduction in cost versus previous methods.
Simultaneously Searching with Multiple Settings: An Alternative to Parameter Tuning for Suboptimal Single-Agent Search Algorithms
Authors: Valenzano Richard , Sturtevant Nathan , Schaeffer Jonathan , Buro Karen , Kishimoto Akihiro
Published: November 2022

Many search algorithms have parameters that need to be tuned to get the best performance. Typically, the parameters are tuned offline, resulting in a generic setting that is supposed to be effective on all problem instances. For suboptimal single-agent search, problem-instance-specific parameter settings can result in substantially reduced search effort. We consider the use of dovetailing as a way to take advantage of this fact. Dovetailing is a procedure that performs search with multiple parameter settings simultaneously. Dovetailing is shown to improve the search speed of weighted IDA* by several orders of magnitude and to generally enhance the performance of weighted RBFS. This procedure is trivially parallelizable and is shown to be an effective form of parallelization for WA* and BULB. In particular, using WA* with parallel dovetailing yields good speedups in the sliding-tile puzzle domain, and increases the number of problems solved when used in an automated planning system.
Lazy Theta*: Any-Angle Path Planning and Path Length Analysis in 3D
Authors: Nash Alex , Koenig Sven , Tovey Craig
Published: November 2022

Grids with blocked and unblocked cells are often used to represent continuous 2D and 3D environments in robotics and video games. The shortest paths formed by the edges of 8-neighbor 2D grids can be up to ≈ 8% longer than the shortest paths in the continuous environment. Theta* typically finds much shorter paths than that by propagating information along graph edges (to achieve short runtimes) without constraining paths to be formed by graph edges (to find short “any-angle” paths). We show in this paper that the shortest paths formed by the edges of 26-neighbor 3D grids can be ≈ 13% longer than the shortest paths in the continuous environment, which highlights the need for smart path planning algorithms in 3D. Theta* can be applied to 3D grids in a straight-forward manner, but it performs a line-of-sight check for each unexpanded visible neighbor of each expanded vertex and thus it performs many more line-of-sight checks per expanded vertex on a 26-neighbor 3D grid than on an 8-neighbor 2D grid. We therefore introduce Lazy Theta*, a variant of Theta* which uses lazy evaluation to perform only one line-of-sight check per expanded vertex (but with slightly more expanded vertices). We show experimentally that Lazy Theta* finds paths faster than Theta* on 26-neighbor 3D grids, with one order of magnitude fewer line-of-sight checks and without an increase in path length.
High-Quality Policies for the Canadian Traveler's Problem
Authors: Eyerich Patrick , Keller Thomas , Helmert Malte
Published: November 2022

We consider the stochastic variant of the Canadian Traveler's Problem, a path planning problem where adverse weather can cause some roads to be untraversable. The agent does not initially know which roads can be used. However, it knows a probability distribution for the weather, and it can observe the status of roads incident to its location. The objective is to find a policy with low expected travel cost. We introduce and compare several algorithms for the stochastic CTP. Unlike the optimistic approach most commonly considered in the literature, the new approaches we propose take uncertainty into account explicitly. We show that this property enables them to generate policies of much higher quality than the optimistic one, both theoretically and experimentally.
Preface
Authors: Felner Ariel , Sturtevant Nathan
Published: November 2022

Introduction and preface to the Proceedings of the Third Annual Symposium on Combinatorial Search (SoCS-10).
Bootstrap Learning of Heuristic Functions
Authors: Jabbari Arfaee Shahab , Zilles Sandra , Holte Robert
Published: November 2022

search algorithms such as IDA* or heuristic-search planners. Our method aims to generate a strong heuristic from a given weak heuristic h0 through bootstrapping. The "easy" problem instances that can be solved using h0 provide training examples for a learning algorithm that produces a heuristic h1 that is expected to be stronger than h0. If h0 is too weak to solve any of the given instances we use a random walk technique to create a sequence of successively more difficult instances starting with ones that are solvable by h0. The bootstrap process is then repeated using hi in lieu of hi–1 until a sufficiently strong heuristic is produced. We test our method on the 15- and 24-sliding tile puzzles, the 17- and 24-pancake puzzles, and the 15- and 20-blocks world. In every case our method produces a heuristic that allows IDA* to solve randomly generated problem instances extremely quickly with solutions very close to optimal.
Common Misconceptions Concerning Heuristic Search
Authors: Holte Robert
Published: November 2022

This paper examines the following statements about heuristic search, which are commonly held to be true:More accurate heuristics result in fewer node expansions by A* and IDA*.A* does fewer node expansions than any other equally informed algorithm that finds optimal solutions. Any admissible heuristic can be turned into a consistent heuristic by a simple technique called pathmax.In search spaces whose operators all have the same cost A* with the heuristic function h(s)=0 for all states, s, is the same as breadth-first search.Bidirectional A* stops when the forward and backward search frontiers meet.The paper demonstrates that all these statements are false and provides alternative statements that are true.
On Transposition Tables for Single-Agent Search and Planning: Summary of Results
Authors: Akagi Yuima , Kishimoto Akihiro , Fukunaga Alex
Published: November 2022

Transposition tables are a well-known method for pruning duplicates in heuristic search. This paper presents a detailed analysis of transposition tables for IDA*. We show that some straightforward implementations of IDA* with transposition tables (IDA*+TT) can result in suboptimal solutions being returned. Furthermore, straightforward implementations of IDA*+TT are not complete. We identify several variants of IDA*+TT which are guaranteed to return the optimal solution, as well as a complete variant. An empirical study shows that IDA*+TT can significantly improve upon the performance of A* in domain-independent planning.
Portal-Based True-Distance Heuristics for Path Finding
Authors: Goldenberg Meir , Felner Ariel , Sturtevant Nathan , Schaeffer Jonathan
Published: November 2022

True distance memory-based heuristics (TDHs) were recently introduced as a way to obtain admissible heuristics for explicit state spaces. In this paper, we introduce a new TDH, the portal-based heuristic. The domain is partitioned into regions and portals between regions are identified. True distances between all pairs of portals are stored and used to obtain admissible heuristics throughout the search. We introduce an A*-based algorithm that takes advantage of the special properties of the new heuristic. We study the advantages and limitations of the new heuristic. Our experimental results show large performance improvements over previously-reported TDHs for commonly used classes of maps.
Layer-Abstraction for Symbolically Solving General Two-Player Games
Authors: Kissmann Peter , Edelkamp Stefan
Published: November 2022

In this paper we propose a new algorithm for solving general two-player turn-taking games that performs symbolic search utilizing binary decision diagrams (BDDs). It consists of two stages: First, it determines all breadth-first search (BFS) layers using forward search and omitting duplicate detection, next, the solving process operates in backward direction only within these BFS layers thereby partitioning all BDDs according to the layers the states reside in. We provide experimental results for selected games and compare to a previous approach. This comparison shows that in most cases the new algorithm outperforms the existing one in terms of runtime and used memory so that it can solve games that could not be solved before with a general approach.
Finding Acceptable Solutions Faster Using Inadmissible Information
Authors: Thayer Jordan , Ruml Wheeler
Published: November 2022

Bounded suboptimal search algorithms attempt to find a solution quickly while guaranteeing that the cost does not exceed optimal by more than a desired factor. These algorithms generally use a single admissible heuristic both for guidance and guaranteeing solution quality. We present a new approach to bounded suboptimal search that separates these roles, consulting multiple sources of potentially inadmissible information to determine search order and using admissible information to guarantee quality. An empirical evaluation across six benchmark domains shows the new approach has better overall performance.
Computing Equivalent Transformations for Combinatorial Optimization by Branch-and-Bound Search
Authors: Hsu Eric , McIlraith Sheila
Published: November 2022

Branch-and-Bound search is a basic algorithm for solving combinatorial optimization problems. Here we introduce a new lower-bounding methodology that can be incorporated into any branch-and-bound solver, and demonstraint its use on the MaxSAT constraint optimization problem. The approach is to adapt a “minimum-height equivalent transformation” framework that was first developed in the context of computer vision. We present efficient algorithms to realize this framework within the MaxSAT domain, and demonstrate their feasibility by implementing them within the state-of-the-art maxsatz solver. We evaluate the solver on test sets from the 2009 MaxSAT competition; we observe a basic performance tradeoff whereby the (quadratic) time cost of computing the transformations may or may not be worthwhile in exchange for better bounds and more frequent pruning. For specific test sets, the trade-off does result in significant improvement in both prunings and overall run-time.
Anytime AND/OR Depth-First Search for Combinatorial Optimization
Authors: Otten Lars , Dechter Rina
Published: November 2022

One popular and efficient scheme for solving exactly combinatorial optimization problems over graphical models is depth-first Branch and Bound. However, when the algorithm exploits problem decomposition using AND/OR search spaces, its anytime behavior breaks down. This paper 1) analyzes and demonstrates this inherent conflict between effective exploitation of problem decomposition (through AND/OR search spaces) and the anytime behavior of depth-first search (DFS), 2) presents a first scheme to address this issue while maintaining desirable DFS memory properties, 3) analyzes and demonstrates its effectiveness. Our work is applicable to any problem that can be cast as search over an AND/OR search space.
Search-Based Planning with Provable Suboptimality Bounds for Continuous State Spaces
Authors: Gonzalez Juan , Likhachev Maxim
Published: November 2022

Search-based planning is widely used for mobile robot motion planning because of its guarantees of optimality and completeness. In continuous state-spaces, however, most existing approaches have significant limitations in terms of optimality and completeness because of the underlying grid used. We propose an approach that eliminates the dependency on grids by using more general equivalence classes to quickly find an initial solution and instead of pruning states that fall within an equivalence class and have higher cost, we use an inflated heuristic to lower the priority of these states in the search. In further iterations, we reduce the inflated heuristic in a principled way, thus providing fast solutions with provable suboptimality bounds that can be improved as time allows. The proposed approach produces smooth paths with the resolution dictated by the action set. Finer action sets produce higher resolution paths that are more computationally intensive to calculate and coarser action sets produce lower resolution paths that are faster to compute. To the best of our knowledge, this is the first algorithm that is able to plan in continuous state-spaces with provable guarantees on completeness and bounds on suboptimality for a given action set. Experimental results on 3D (x,y,theta) path planning show that, on average, this approach is able to find paths in less than two seconds that are within 2% of the optimal path cost in worlds of up to 1000x1000 m with a minimum step size of one meter.
Size-Independent Additive Pattern Databases for the Pancake Problem
Authors: Torralba Arias de Reyna Álvaro , Linares López Carlos
Published: November 2022

The Pancake problem has become a classical combinatorial problem. Different attempts have been made to optimally solve it and/or to derive tighter bounds on the diameter of its state space for a different number of discs. Until very recently, the most successful technique for solving different instances optimally was based on Pattern Databases. Although different approaches have been tried, solutions with Pattern Databases on Pancakes with more than 19 discs have never been reported. In this work, a new technique is introduced which allows the definition of Additive Pattern Databases for solving Pancakes of an arbitrary length. As a result, this technique solves Pancake problems with twice as many discs as the largest ones solved nowadays with other techniques based on Pattern Databases saving up to two orders of magnitude of space.
Deadline-Aware Search Using On-Line Measures of Behavior
Authors: Dionne Austin , Thayer Jordan , Ruml Wheeler
Published: November 2022

In many applications of heuristic search, insufficient time isavailable to find provably optimal solutions. We consider thecontract search problem: finding the best solution possible within agiven time limit. The conventional approach to this problem is to usean interruptible anytime algorithm. Such algorithms return a sequenceof improving solutions until interuppted and do not consider theapproaching deadline during the course of the search. We propose anew approach, Deadline Aware Search, that explicitly takes the deadlineinto account and attempts to use all available time to find a singlehigh-quality solution. This algorithm is simple and fully general: itmodifies best-first search with on-line pruning. Empirical results onvariants of gridworld navigation, the sliding tile puzzle, and dynamicrobot navigation show that our method can surpass the leading anytimealgorithms across a wide variety of deadlines.
Degrees of Separation in Social Networks
Authors: Bakhshandeh Reza , Samadi Mehdi , Azimifar Zohreh , Schaeffer Jonathan
Published: November 2022

Social networks play an increasingly important role in today's society. Special characteristics of these networks make them challenging domains for the search community. In particular, social networks of users can be viewed as search graphs of nodes, where the cost of obtaining information about a node can be very high. This paper addresses the search problem of identifying the degree of separation between two users. New search techniques are introduced to provide optimal or near-optimal solutions. The experiments are performed using Twitter, and they show an improvement of several orders of magnitude over greedy approaches. Our optimal algorithm finds an average degree of separation of 3.43 between two random Twitter users, requiring an average of only 67 requests for information over the Internet to Twitter. A near-optimal solution of length 3.88 can be found by making an average of 13.3 requests.
Best-First Search for Bounded-Depth Trees
Authors: Rose Kevin , Burns Ethan , Ruml Wheeler
Published: November 2022

Tree search is a common technique for solving constraint satisfaction and combinatorial optimization problems. The most popular strategies are depth-first search and limited discrepancy search. Aside from pruning or ordering the children of each node, these algorithms do not adapt their search order to take advantage of information that becomes available during search, such as heuristic scores or leaf costs. We present a framework called best-leaf-first search (BLFS) that uses this additional information to estimate the cost of taking discrepancies in the search tree and then attempts to visit leaves in a best-first order. In this way, BLFS brings the idea of best-first search from shortest path problems to the areas of constraint satisfaction and combinatorial optimization. Empirical results demonstrate that this new dynamic approach results in better search performance than previous static search strategies on two very different domains: structured CSPs and the traveling salesman problem.
Evolving Solvers for FreeCell and the Sliding-Tile Puzzle
Authors: Elyasaf Achiya , Zaritsky Yael , Hauptman Ami , Sipper Moshe
Published: November 2022

We use genetic algorithms to evolve highly successful solvers for two puzzles: FreeCell and the Sliding-Tile Puzzle.
A SAT-Based Approach to Cooperative Path-Finding Using All-Different Constraints
Authors: Surynek Pavel
Published: November 2022

The approach to solving cooperative-path finding (CPF) as satisfiability (SAT) is revisited. An alternative encoding that exploits multi-valued state variables representing locations where a given agent resides is suggested. This encoding employs the ALL-DIFFERENT constraint to model the requirement that agents must not collide with each other. We show that our new domain-dependent encoding enables finding of optimal or near optimal solutions to CPFs in certain hard setups where A*-based techniques such as WHCA* fail to do so. Our finding is also that the ALL-DIFFERENT encoding can be solved faster than the existent encoding.
Partial-Expansion A* with Selective Node Generation
Authors: Felner Ariel , Goldenberg Meir , Sharon Guni , Stern Roni , Beja Tal , Sturtevant Nathan , Holte Robert , Schaeffer Jonathan
Published: November 2022

A* is often described as being 'optimal,' in that it expands the minimum number of unique nodes. But, A* may generate many extra nodes which are never expanded. This is a performance loss, especially when the branching factor is large. Partial Expansion A* (PEA*) addresses this problem when expanding a node, n, by generating all the children of n but only storing children with the same f-cost as n. We introduce an enhanced version of PEA* (EPEA*). Given a priori domain knowledge, EPEA* only generates the children with the same f-cost as the parent. State-of-the-art results were obtained for a number of domains. Drawbacks of EPEA* are also discussed. A full version of this paper appears in the proceedings of AAAI-2012
Multi-Agent Pathfinding with Simultaneous Execution of Single-Agent Primitives
Authors: Sajid Qandeel , Luna Ryan , Bekris Kostas
Published: November 2022

Multi-agent pathfinding is a challenging combinatorial problem that involves multiple agents moving on a graph from a set of initial nodes to a set of desired goals without inter-agent collisions. Searching the composite space of all agents has exponential complexity and does not scale well. Decoupled methods are more efficient but are generally incomplete. There are, however, polynomial time algorithms, which utilize single or few-agents primitives with completeness guarantees. One limitation of these alternatives is that the resulting solution is sequential, where only one agent moves at a time. Such solutions are of low quality when compared to methods where multiple agents can move simultaneously. This work proposes an algorithm for multi-agent pathfinding that utilizes similar single-agent primitives but allows all agents to move in parallel. The paper describes the algorithm and its properties. Experimental comparisons suggest that the resulting paths are considerably better than sequential ones, even after a post-processing, parallelization step, as well as solutions returned by decoupled and coupled alternatives. The experiments also suggest good scalability and competitive computational performance.
When Does Weighted A* Fail?
Authors: Wilt Christopher , Ruml Wheeler
Published: November 2022

Weighted A* is the most popular satisficing algorithm for heuristic search. Although there is no formal guarantee that increasing the weight on the heuristic cost-to-go estimate will decrease search time, it is commonly assumed that increas- ing the weight leads to faster searches, and that greedy search will provide the fastest search of all. As we show, however, in some domains, increasing the weight slows down the search. This has an important consequence on the scaling behavior of Weighted A*: increasing the weight ad infinitum will only speed up the search if greedy search is effective. We examine several plausible hypotheses as to why greedy search would sometimes expand more nodes than A* and show that each of the simple explanations has flaws. Our contribution is to show that greedy search is fast if and only if there is a strong correlation between h(n) and d∗(n), the true distance-to-go, or if the heuristic is extremely accurate.
The JPS Pathfinding System
Authors: Harabor Daniel , Grastien Alban
Published: November 2022

We describe a pathfinding system based on Jump Point Search (JPS): a recent and very successful search strategy that performs symmetry breaking to speed up optimal pathfinding on grid maps. We first modify JPS for grid maps where corner-cutting moves are not allowed. We then describe JPS+: a new derivative search strategy that reformulates an input graph into an equivalent symmetry-reduced form that can be searched more efficiently. JPS and JPS+ were both submitted to the 2012 Grid-based Path Planning Competition.
Diverse Depth-First Search in Satisificing Planning
Authors: Kishimoto Akihiro , Zhou Rong , Imai Tatsuya
Published: November 2022

Diverse best-first search (DBFS) is a successful algorithm for satisficing planning that is built on top of greedy best-first search and uses stochastic search techniques to select the next ``best'' state that is not always in agreement with the heuristic function. DBFS thus considers a diversity of search directions and avoids search plateaus caused by inaccurate heuristic estimation. However, the scalability of DBFS is still limited by the available memory resource, as in any planning system that uses best-first search at its core. This paper presents the diverse depth-first search (DDFS) algorithm that overcomes the memory bottleneck of DBFS. DDFS requires much less memory than DBFS yet it successfully incorporates DBFS' randomized node selection scheme for effective search diversification. Additionally, DDFS is enhanced with a transposition table to avoid duplicate search effort commonly found in depth-first search. Experimental results in solving problems used in previous International Planning Competitions show that DDFS can solve instances that remain unsolvable by DBFS due to its excessive memory requirements.
Position Paper: Incremental Search Algorithms Considered Poorly Understood
Authors: Hernandez Carlos , Baier Jorge , Uras Tansel , Koenig Sven
Published: November 2022

Incremental search algorithms, such as D* Lite, reuse information from previous searches to speed up the current search and can thus solve sequences of similar search problems faster than Repeated A*, which performs repeated A* searches. In this position paper, we study goal-directed navigation in initially unknown terrain and point out that it is currently not well understood when D* Lite runs faster than Repeated A*. In general, it appears that Repeated A* runs faster than D* Lite for easy navigation problems (where the agent reaches the goal with only a small number of searches), which means that it runs faster than D* Lite quite often in practice. We draw two conclusions, namely that incremental search algorithms need to be evaluated in more diverse testbeds to improve our understanding of their properties and that they can be improved to be more competitive for easy navigation problems.
Towards Rational Deployment of Multiple Heuristics in A* (Extended Abstract)
Authors: Tolpin David , Beja Tal , Shimony Solomon Eyal , Felner Ariel , Karpas Erez
Published: November 2022

In this paper we discuss and experiment with Lazy A*, a variant of A* where heuristics are evaluated lazily and with Rational Lazy A*, which decides whether to compute the more expensive heuristics at all, based on a myopic value of information estimate. Full version appears in IJCAI-2013.
Candidate Sets for Alternative Routes in Road Networks (Extended Abstract)
Authors: Luxen Dennis , Schieferdecker Dennis
Published: November 2022

We present a fast algorithm with preprocessing for computing multiple good alternative routes in road networks. Our approach is based on single via node routing on top of Contraction Hierarchies and achieves superior quality and efficiency compared to previous methods. The algorithm has neglectable memory overhead.
Red-Black Relaxed Plan Heuristics Reloaded
Authors: Katz Michael , Hoffmann Joerg
Published: November 2022

Despite its success, the delete relaxation has significant pitfalls. In an attempt to overcome these pitfalls, recent work has devised so-called red-black relaxed plan heuristics, where red variables take the relaxed semantics (accumulating their values), while black variables take the regular semantics. These heuristics were shown to significantly improve over standard delete relaxation heuristics. However, the experiments also brought to light a major weakness: Being based on repairing fully delete-relaxed plans, the returned estimates depend on arbitrary choices made in such plans. This can lead to huge over-estimation in arbitrary subsets of states. Here we devise a new red-black planning method not based on repairing relaxed plans, getting rid of much of this variance. Our experiments show a significant improvement over previous red-black relaxed plan heuristics, and other related methods.
Frequency Data Compression for Public Transportation Network Algorithms (Extended Abstract)
Authors: Bast Hannah , Storandt Sabine
Published: November 2022

Timetable information in public transportation networks exhibit a large degree of redundancy; e.g. consider a bus going from station A to station B at 6:00, 6:15, 6:30, 6:45, 7:00, 7:15, 7:30, . . . , 20:00, the very same data can be provided by a frequency-based representation as ’6:00-20:00, every 15 minutes’ in considerably less space. Nevertheless a common graph model for routing in public transportation networks is the time-expanded representation where for each arrival/departure event a single node is created. We will introduce a frequency-based graph model which allows for a significantly more compact representation of the network, resulting also in a speed-up for station-to-station queries. Moreover we will describe a new variant of Dijkstra’s algorithm, where also the labels are frequency-based. This approach allows for accelerating profile queries in public transportation networks.
ReACT: Real-Time Algorithm Configuration through Tournaments
Authors: Fitzgerald Tadhg , Malitsky Yuri , O’Sullivan Barry , Tierney Kevin
Published: November 2022

The success or failure of a solver is oftentimes closely tied to the proper configuration of the solver's parameters. However, tuning such parameters by hand requires expert knowledge, is time consuming, and is error-prone. In recent years, automatic algorithm configuration tools have made significant advances and can nearly always find better parameters than those found through hand tuning. However, current approaches require significant offline computational resources, and follow a train-once methodology that is unable to later adapt to changes in the type of problem solved. To this end, this paper presents Real-time Algorithm Configuration through Tournaments (ReACT), a method that does not require any offline training to perform algorithm configuration. ReACT exploits the multi-core infrastructure available on most modern machines to create a system that continuously searches for improving parameterizations, while guaranteeing a particular level of performance. The experimental results show that, despite the simplicity of the approach, ReACT quickly finds a set of parameters that is better than the default parameters and is competitive with state-of-the-art algorithm configurators.
Max Is More than Min: Solving Maximization Problems with Heuristic Search
Authors: Stern Roni , Kiesel Scott , Puzis Rami , Felner Ariel , Ruml Wheeler
Published: November 2022

Most work in heuristic search considers problems where a low cost solution is preferred (MIN problems). In this paper, we investigate the complementary setting where a solution of high reward is preferred (MAX problems). Example MAX problems include finding the longest simple path in a graph, maximal coverage, and various constraint optimization problems. We examine several popular search algorithms for MIN problems — optimal, suboptimal, and bounded suboptimal - and discover the curious ways in which they misbehave on MAX problems. We propose modifications that preserve the original intentions behind the algorithms but allow them to solve MAX problems, and compare them theoretically and empirically. Interesting results include the failure of bidirectional search and a discovered close relationships between Dijkstra's algorithm, weighted A*, and depth-first search. This work demonstrates that MAX problems demand their own heuristic search algorithms, which are worthy objects of study in their own right.
Hybrid Planning Heuristics Based on Task Decomposition Graphs
Authors: Bercher Pascal , Keen Shawn , Biundo Susanne
Published: November 2022

Hybrid Planning combines Hierarchical Task Network (HTN) planning with concepts known from Partial-Order Causal-Link (POCL) planning. We introduce novel heuristics for Hybrid Planning that estimate the number of necessary modifications to turn a partial plan into a solution. These estimates are based on the task decomposition graph that contains all decompositions of the abstract tasks in the planning domain. Our empirical evaluation shows that the proposed heuristics can significantly improve planning performance.
Evaluating Weighted DFS Branch and Bound over Graphical Models
Authors: Flerova Natalia , Marinescu Radu , Dechter Rina
Published: November 2022

Weighted search was explored significantly in recent years for path-finding problems, but until now was barely considered for optimization tasks such as MPE/MAP and Weighted CSPs. An important virtue of weighted search schemes, especially in the context of anytime search, is that they are w-optimal, i.e. when terminated, they return a weight w, and a solution cost C, such that C ≤ w · C*, where C* is the optimal cost. In this paper we introduce Weighted Branch and Bound (WBB) for graphical models and provide a broad empirical evaluation of its performance compared with one of the best unweighted anytime search scheme, BRAOBB (won Pascal 2011 competition). We also compare against weighted best-first (WBF). Our results show that W BB can be superior to both unweighted BB and to weighted BF on a significant number of instances. We also illustrate the benefit of weighted search in providing suboptimality relative error bounds.
Toward a Search Strategy for Anytime Search in Linear Space Using Depth-First Branch and Bound
Authors: Hernandez Carlos , Baier Jorge
Published: November 2022

Depth-First Branch and Bound (DFBnB) is an anytime algorithm for solving combinatorial optimization problems. In this paper we present a weighted version of DFBnB, wDFBnB, which incorporates standard techniques for using weights in heuristic search and offers suboptimality guarantees. Our main contribution drawn from a preliminary evaluation is the observation that wDFBnB, used along with automated or hand-crafted weight schedules, can significantly outperform DFBnB both in terms of anytime behavior and convergence to the optimal. We think this small study calls for more research on the design of automated weight schedules that could provide superior anytime performance across a wider range of domains.
ICBS: The Improved Conflict-Based Search Algorithm for Multi-Agent Pathfinding
Authors: Boyarski Eli , Felner Ariel , Stern Roni , Sharon Guni , Betzalel Oded , Tolpin David , Shimony Eyal
Published: November 2022

Conflict-Based Search (CBS) and its generalization, Meta-Agent CBS are amongst the strongest newly introduced algorithms for Multi-Agent Path Finding. This paper introduces ICBS, an improved version of CBS. ICBS incorporates three orthogonal improvements to CBS which are systematically described and studied. Experimental results show that each of these improvements reduces the runtime over basic CBS by up to 20x in many cases. When all three improvements are combined, an even larger improvement is achieved, producing state-ofthe art results for a number of domains.
Building a Heuristic for Greedy Search
Authors: Wilt Christopher , Ruml Wheeler
Published: November 2022

Suboptimal heuristic search algorithms such as greedy best-first search allow us to find solutions when constraints of either time, memory, or both prevent the application of optimal algorithms such as A*. Guidelines for building an effective heuristic for A* are well established in the literature, but we show that if those rules are applied for greedy best-first search, performance can actually degrade. Observing what went wrong for greedy best-first search leads us to a quantitative metric appropriate for greedy heuristics, called Goal Distance Rank Correlation (GDRC). We demonstrate that GDRC can be used to build effective heuristics for greedy best-first search automatically.
Sibling Conspiracy Number Search
Authors: Pawlewicz Jakub , Hayward Ryan
Published: November 2022

For some two-player games (e.g. Go), no accurate and inexpensive heuristic is known for evaluating leaves of a search tree. For other games (e.g. chess), a heuristic is known (sum of piece values). For other games (e.g. Hex), only a local heuristic — one that compares children reliably, but non-siblings poorly — is known (cell voltage drop in the Shannon/Anshelevich electric circuit model). In this paper we introduce a search algorithm for a two-player perfect information game with a reasonable local heuristic. Sibling Conspiracy Number Search (SCNS) is an anytime best-first version of Conspiracy Number Search based not on evaluation of leaf states of the search tree, but — for each node — on relative evaluation scores of all children of that node. SCNS refines CNS search value intervals, converging to Proof Number Search. SCNS is a good framework for a game player. We tested SCNS in the domain of Hex, with promising results. We implemented an 11-by-11 SCNS Hex bot, DeepHex. We competed DeepHex against current Hex bot champion MoHex, a Monte-Carlo Tree Search player, and previous Hex bot champion Wolve, an Alpha-Beta Search player. DeepHex widely outperforms Wolve at all time levels, and narrowly outperforms MoHex once time reaches 4min/move.
Metareasoning in Real-Time Heuristic Search
Authors: O'Ceallaigh Dylan , Ruml Wheeler
Published: November 2022

Real-time heuristic search addresses the setting in which planning andacting can proceed concurrently. We explore the use of metareasoning at two decision points within a real-time heuristic search. First, if the domain has an `identity action' that allows the agent to remain in the same state and deliberate further, when should this action be taken? Second, given a partial plan that extends to the lookahead frontier, to how many actions should the agent commit? We show that considering these decisions carefully can reduce the agent's total time taken to arrive at a goal in several benchmark domains, relative to the current state-of-the-art. The resulting algorithm can dynamically adjust the way it interleaves planning and acting, between greedy hill-climbing and A*, depending on the problem instance.
Maximum a Posteriori Estimation by Search in Probabilistic Programs
Authors: Tolpin David , Wood Frank
Published: November 2022

We introduce an approximate search algorithm for fast maximum a posteriori probability estimation in probabilistic programs, which we call Bayesian ascent Monte Carlo (BaMC). Probabilistic programs represent probabilistic models with varying number of mutually dependent finite, countable, and continuous random variables. BaMC is an anytime MAP search algorithm applicable to any combination of random variables and dependencies. We compare BaMC to other MAP estimation algorithms and show that BaMC is faster and more robust on a range of probabilistic models.