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The LAMA planner: Guiding costbased anytime planning with landmarks
, 2010
"... LAMA is a classical planning system based on heuristic forward search. Its core feature is the use of a pseudoheuristic derived from landmarks, propositional formulas that must be true in every solution of a planning task. LAMA builds on the Fast Downward planning system, using finitedomain rather ..."
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Cited by 140 (5 self)
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LAMA is a classical planning system based on heuristic forward search. Its core feature is the use of a pseudoheuristic derived from landmarks, propositional formulas that must be true in every solution of a planning task. LAMA builds on the Fast Downward planning system, using finitedomain rather than binary state variables and multiheuristic search. The latter is employed to combine the landmark heuristic with a variant of the wellknown FF heuristic. Both heuristics are costsensitive, focusing on highquality solutions in the case where actions have nonuniform cost. A weighted A ∗ search is used with iteratively decreasing weights, so that the planner continues to search for plans of better quality until the search is terminated. LAMA showed best performance among all planners in the sequential satisficing track of the International Planning Competition 2008. In this paper we present the system in detail and investigate which features of LAMA are crucial for its performance. We present individual results for some of the domains used at the competition, demonstrating good and bad cases for the techniques implemented in LAMA. Overall, we find that using landmarks improves performance, whereas the incorporation of action costs into the heuristic estimators proves not to be beneficial. We show that in some domains a search that ignores cost solves far more problems, raising the question of how to deal with action costs more effectively in the future. The iterated weighted A ∗ search greatly improves results, and shows synergy effects with the use of landmarks. 1.
The Joy of Forgetting: Faster Anytime Search via Restarting
"... {jtd7, ruml} at cs.unh.edu Anytime search algorithms solve optimisation problems by quickly finding a usually suboptimal solution and then finding improved solutions when given additional time. To deliver a solution quickly, they are typically greedy with respect to the heuristic costtogo estimate ..."
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Cited by 46 (15 self)
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{jtd7, ruml} at cs.unh.edu Anytime search algorithms solve optimisation problems by quickly finding a usually suboptimal solution and then finding improved solutions when given additional time. To deliver a solution quickly, they are typically greedy with respect to the heuristic costtogo estimate h. In this paper, we first show that this lowh bias can cause poor performance if the heuristic is inaccurate. Building on this observation, we then present a new anytime approach that restarts the search from the initial state every time a new solution is found. We demonstrate the utility of our method via experiments in PDDL planning as well as other domains. We show that it is particularly useful for hard optimisation problems like planning where heuristics may be quite inaccurate and inadmissible, and where the greedy solution makes early mistakes.
State abstraction in realtime heuristic search
 Journal of Artificial Intelligence Research
, 2006
"... Realtime heuristic search methods are used by situated agents in applications that require the amount of planning per move to be constantbounded regardless of the problem size. Such agents plan only a few actions in a local search space and avoid getting trapped in heuristic local minima by improv ..."
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Cited by 33 (10 self)
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Realtime heuristic search methods are used by situated agents in applications that require the amount of planning per move to be constantbounded regardless of the problem size. Such agents plan only a few actions in a local search space and avoid getting trapped in heuristic local minima by improving their heuristic function over time. We extend a wide class of realtime search algorithms with automatically built graph abstraction. Extensive empirical evaluation in the domain of goaldirected navigation demonstrates that the use of abstraction accelerates learning of the heuristic function while maintaining realtime performance. The resulting algorithm outperforms virtually all tested algorithms simultaneously along negatively correlated performance measures.
Faster Than Weighted A*: An Optimistic Approach to Bounded Suboptimal Search
"... Planning, scheduling, and other applications of heuristic search often demand we tackle problems that are too large to solve optimally. In this paper, we address the problem of solving shortestpath problems as quickly as possible while guaranteeing that solution costs are bounded within a specified ..."
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Cited by 31 (12 self)
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Planning, scheduling, and other applications of heuristic search often demand we tackle problems that are too large to solve optimally. In this paper, we address the problem of solving shortestpath problems as quickly as possible while guaranteeing that solution costs are bounded within a specified factor of optimal. 38 years after its publication, weighted A * remains the bestperforming algorithm for generalpurpose bounded suboptimal search. However, it typically returns solutions that are better than a given bound requires. We show how to take advantage of this behavior to speed up search while retaining bounded suboptimality. We present an optimistic algorithm that uses a weight higher than the user’s bound and then attempts to prove that the resulting solution adheres to the bound. While simple, we demonstrate that this algorithm consistently surpasses weighted A * in four different benchmark domains including temporal planning and gridworld pathfinding.
BestFirst Heuristic Search for MultiCore Machines
"... eaburns, seth.lemons, ruml at cs.unh.edu rzhou at parc.com To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we present a general approach to bestfirst heuristic search in a sharedmemory setting. Each thread attempts to ..."
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Cited by 26 (7 self)
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eaburns, seth.lemons, ruml at cs.unh.edu rzhou at parc.com To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we present a general approach to bestfirst heuristic search in a sharedmemory setting. Each thread attempts to expand the most promising open nodes. By using abstraction to partition the state space, we detect duplicate states without requiring frequent locking. We allow speculative expansions when necessary to keep threads busy. We identify and fix potential livelock conditions in our approach, verifying its correctness using temporal logic. In an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using an 8core machine, we show that A * implemented in our framework yields faster search than improved versions of previous parallel search proposals. Our approach extends easily to other bestfirst searches, such as Anytime weighted A*. 1
Anytime Search in Dynamic Graphs
"... Agents operating in the real world often have limited time available for planning their next actions. Producing optimal plans is infeasible in these scenarios. Instead, agents must be satisfied with the best plans they can generate within the time available. One class of planners wellsuited to this ..."
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Cited by 25 (5 self)
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Agents operating in the real world often have limited time available for planning their next actions. Producing optimal plans is infeasible in these scenarios. Instead, agents must be satisfied with the best plans they can generate within the time available. One class of planners wellsuited to this task are anytime planners, which quickly find an initial, highly suboptimal plan, and then improve this plan until time runs out. A second challenge associated with planning in the real world is that models are usually imperfect and environments are often dynamic. Thus, agents need to update their models and consequently plans over time. Incremental planners, which make use of the results of previous planning efforts to generate a new plan, can substantially speed up each planning episode in such cases. In this paper, we present an A*based anytime search algorithm that produces significantly better solutions than current approaches, while also providing suboptimality bounds on the quality of the solution at any point in time. We also present an extension of this algorithm that is both anytime and incremental. This extension improves its current solution while deliberation time allows and is able to incrementally repair its solution when changes to the world model occur. We provide a number of theoretical and experimental results and demonstrate the effectiveness of the approaches in a robot navigation domain involving two physical systems. We believe that the simplicity, theoretical properties, and generality of the presented methods make them well suited to a range of search problems involving large, dynamic graphs.
Anytime Heuristic Search for Partial Satisfaction Planning
, 2008
"... We present a heuristic search approach to solve partial satisfaction planning (PSP) problems. In these problems, goals are modeled as soft constraints with utility values, and actions have costs. Goal utility represents the value of each goal to the user and action cost represents the total resource ..."
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Cited by 22 (9 self)
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We present a heuristic search approach to solve partial satisfaction planning (PSP) problems. In these problems, goals are modeled as soft constraints with utility values, and actions have costs. Goal utility represents the value of each goal to the user and action cost represents the total resource cost (e.g., time, fuel cost) needed to execute each action. The objective is to find the plan that maximizes the tradeoff between the total achieved utility and the total incurred cost; we call this problem PSP NET BENEFIT. Previous approaches to solving this problem heuristically convert PSP NET BENEFIT into STRIPS planning with action cost by preselecting a subset of goals. In contrast, we provide a novel anytime search algorithm that handles soft goals directly. Our new search algorithm has an anytime property that keeps returning better quality solutions until the termination criteria are met. We have implemented this search algorithm, along with relaxed plan heuristics adapted to PSP NET BENEFIT problems, in a forward statespace planner called Sapa PS. An adaptation of Sapa PS, called Yochan PS, received a “distinguished performance” award in the “simple preferences” track of the 5 th International Planning Competition.
Searchbased path planning with homotopy class constraints
 In Proc. National Conference on Artificial Intelligence
"... Goaldirected path planning is one of the basic and widely studied problems in the field of mobile robotics. Homotopy classes of trajectories, arising due to the presence of obstacles, are defined as sets of trajectories that can be transformed into each other by gradual bending and stretching witho ..."
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Cited by 19 (4 self)
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Goaldirected path planning is one of the basic and widely studied problems in the field of mobile robotics. Homotopy classes of trajectories, arising due to the presence of obstacles, are defined as sets of trajectories that can be transformed into each other by gradual bending and stretching without colliding with obstacles. The problem of finding leastcost paths restricted to a specific homotopy class or finding leastcost paths that do not belong to certain homotopy classes arises frequently in such applications as predicting paths for dynamic entities and computing heuristics for path planning with dynamic constraints. In the present work, we develop a compact way of representing homotopy classes and propose an efficient method of graph searchbased optimal path planning with constraints on homotopy classes. The method is based on representing the environment of the robot as a complex plane and making use of the Cauchy Integral Theorem. We prove optimality of the method and show its efficiency experimentally.
Action Elimination and Plan Neighborhood Graph Search: Two Algorithms for Plan Improvement
 PROCEEDINGS OF THE TWENTIETH INTERNATIONAL CONFERENCE ON AUTOMATED PLANNING AND SCHEDULING (ICAPS 2010)
, 2010
"... Compared to optimal planners, satisficing planners can solve much harder problems but may produce overly costly and long plans. Plan quality for satisficing planners has become increasingly important. The most recent planning competition IPC2008 used the cost of the best known plan divided by the c ..."
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Cited by 15 (9 self)
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Compared to optimal planners, satisficing planners can solve much harder problems but may produce overly costly and long plans. Plan quality for satisficing planners has become increasingly important. The most recent planning competition IPC2008 used the cost of the best known plan divided by the cost of the generated plan as an evaluation metric. This paper proposes and evaluates two simple but effective methods for plan improvement: Action Elimination improves an existing plan by repeatedly removing sets of irrelevant actions. Plan Neighborhood Graph Search finds a new, shorter plan by creating a plan neighborhood graph PNG(π) of a given plan π, and then extracts a shortest path from PNG(π). Both methods are implemented in the ARAS postprocessor and are empirically shown to improve the result of several planners, including the top four planners from IPC2008, under competition conditions.
Potential search: a boundedcost search algorithm
 In Proceedings of the TwentyFirst International Conference on Automated Planning and Scheduling
, 2011
"... In this paper we address the following search task: find a goal with cost smaller than or equal to a given fixed constant. This task is relevant in scenarios where a fixed budget is available to execute a plan and we would like to find such a plan while minimizing the search effort. We introduce an ..."
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Cited by 12 (8 self)
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In this paper we address the following search task: find a goal with cost smaller than or equal to a given fixed constant. This task is relevant in scenarios where a fixed budget is available to execute a plan and we would like to find such a plan while minimizing the search effort. We introduce an algorithm called Potential search (PTS) which is specifically designed to solve this problem. PTS is a bestfirst search that expands nodes according to the probability that they will be part of a plan whose cost is less than or equal to the given budget. We show that it is possible to implement PTS even without explicitly calculating these probabilities, when a heuristic function and knowledge about the error of this heuristic function are given. In addition, we also show that PTS can be modified to an anytime search algorithm. Experimental re