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108
Coming up With Good Excuses: What to do When no Plan Can be Found
"... When using a plannerbased agent architecture, many things can go wrong. First and foremost, an agent might fail to execute one of the planned actions for some reasons. Even more annoying, however, is a situation where the agent is incompetent, i.e., unable to come up with a plan. This might be due ..."
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Cited by 22 (0 self)
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When using a plannerbased agent architecture, many things can go wrong. First and foremost, an agent might fail to execute one of the planned actions for some reasons. Even more annoying, however, is a situation where the agent is incompetent, i.e., unable to come up with a plan. This might be due to the fact that there are principal reasons that prohibit a successful plan or simply because the task’s description is incomplete or incorrect. In either case, an explanation for such a failure would be very helpful. We will address this problem and provide a formalization of coming up with excuses for not being able to find a plan. Based on that, we will present an algorithm that is able to find excuses and demonstrate that such excuses can be found in practical settings in reasonable time.
Strengthening Landmark Heuristics via Hitting Sets
"... The landmark cut heuristic is perhaps the strongest known polytime admissible approximation of the optimal delete relaxation heuristic h +. Equipped with this heuristic, a bestfirst search was able to optimally solve 40 % more benchmark problems than the winners of the sequential optimization track ..."
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Cited by 21 (5 self)
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The landmark cut heuristic is perhaps the strongest known polytime admissible approximation of the optimal delete relaxation heuristic h +. Equipped with this heuristic, a bestfirst search was able to optimally solve 40 % more benchmark problems than the winners of the sequential optimization track of IPC 2008. We show that this heuristic can be understood as a simple relaxation of a hitting set problem, and that stronger heuristics can be obtained by considering stronger relaxations. Based on these findings, we propose a simple polytime method for obtaining heuristics stronger than landmark cut, and evaluate them over benchmark problems. We also show that hitting sets can be used to characterize h + and thus provide a fresh and novel insight for better comprehension of the delete relaxation. 1
Sound and Complete Landmarks for And/Or Graphs
"... Abstract. Landmarks for a planning problem are subgoals that are necessarily made true at some point in the execution of any plan. Since verifying that a fact is a landmark is PSPACEcomplete, earlier approaches have focused on finding landmarks for the delete relaxation Π +. Furthermore, some of th ..."
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Cited by 20 (8 self)
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Abstract. Landmarks for a planning problem are subgoals that are necessarily made true at some point in the execution of any plan. Since verifying that a fact is a landmark is PSPACEcomplete, earlier approaches have focused on finding landmarks for the delete relaxation Π +. Furthermore, some of these approaches have approximated this set of landmarks, although it has been shown that the complete set of causal deleterelaxation landmarks can be identified in polynomial time by a simple procedure over the relaxed planning graph. Here, we give a declarative characterisation of this set of landmarks and show that the procedure computes the landmarks described by our characterisation. Building on this, we observe that the procedure can be applied to any deleterelaxation problem and take advantage of a recent compilation of the mrelaxation of a problem into a problem with no delete effects to extract landmarks that take into account delete effects in the original problem. We demonstrate that this approach finds strictly more causal landmarks than previous approaches and discuss the relationship between increased computational effort and experimental performance, using these landmarks in a recently proposed admissible landmarkcounting heuristic. 1
Resourceconstrained planning: A Monte Carlo random walk approach
, 2012
"... The need to economize limited resources, such as fuel or money, is a ubiquitous feature of planning problems. If the resources cannot be replenished, the planner must make do with the initial supply. It is then of paramount importance how constrained the problem is, i.e., whether and to which extent ..."
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Cited by 19 (11 self)
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The need to economize limited resources, such as fuel or money, is a ubiquitous feature of planning problems. If the resources cannot be replenished, the planner must make do with the initial supply. It is then of paramount importance how constrained the problem is, i.e., whether and to which extent the initial resource supply exceeds the minimum need. While there is a large body of literature on numeric planning and planning with resources, such resource constrainedness has only been scantily investigated. We herein start to address this in more detail. We generalize the previous notion of resource constrainedness, characterized through a numeric problem feature C ≥ 1, to the case of multiple resources. We implement an extended benchmark suite controlling C. We conduct a largescale study of the current state of the art as a function of C, highlighting which techniques contribute to success. We introduce two new techniques on top of a recent Monte Carlo Random Walk method, resulting in a planner that, in these benchmarks, outperforms previous planners when resources are scarce (C close to 1). We investigate the parameters influencing the performance of that planner, and we show that one of the two new techniques works well also on the regular IPC benchmarks.
Analyzing search topology without running any search: On the connection between causal graphs and h+
 JAIR
, 2011
"... The ignoring delete lists relaxation is of paramount importance for both satisficing and optimal planning. In earlier work, it was observed that the optimal relaxation heuristic h + has amazing qualities in many classical planning benchmarks, in particular pertaining to the complete absence of local ..."
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Cited by 19 (3 self)
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The ignoring delete lists relaxation is of paramount importance for both satisficing and optimal planning. In earlier work, it was observed that the optimal relaxation heuristic h + has amazing qualities in many classical planning benchmarks, in particular pertaining to the complete absence of local minima. The proofs of this are handmade, raising the question whether such proofs can be lead automatically by domain analysis techniques. In contrast to earlier disappointing results – the analysis method has exponential runtime and succeeds only in two extremely simple benchmark domains – we herein answer this question in the affirmative. We establish connections between causal graph structure and h + topology. This results in loworder polynomial time analysis methods, implemented in a tool we call TorchLight. Of the 12 domains where the absence of local minima has been proved, TorchLight gives strong success guarantees in 8 domains. Empirically, its analysis exhibits strong performance in a further 2 of these domains, plus in 4 more domains where local minima may exist but are rare. In this way, TorchLight can distinguish “easy” domains from “hard” ones. By summarizing structural reasons for analysis failure, TorchLight also provides diagnostic output indicating domain aspects that may cause local minima.
To Max or not to Max: Online Learning for Speeding Up Optimal Planning
, 2010
"... It is well known that there cannot be a single “best ” heuristic for optimal planning in general. One way of overcoming this is by combining admissible heuristics (e.g. by using their maximum), which requires computing numerous heuristic estimates at each state. However, there is a tradeoff between ..."
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Cited by 17 (7 self)
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It is well known that there cannot be a single “best ” heuristic for optimal planning in general. One way of overcoming this is by combining admissible heuristics (e.g. by using their maximum), which requires computing numerous heuristic estimates at each state. However, there is a tradeoff between the time spent on computing these heuristic estimates for each state, and the time saved by reducing the number of expanded states. We present a novel method that reduces the cost of combining admissible heuristics for optimal search, while maintaining its benefits. Based on an idealized search space model, we formulate a decision rule for choosing the best heuristic to compute at each state. We then present an active online learning approach for that decision rule, and employ the learned model to decide which heuristic to compute at each state. We evaluate this technique empirically, and show that it substantially outperforms each of the individual heuristics that were used, as well as their regular maximum.
Implicit abstraction heuristics
"... Statespace search with explicit abstraction heuristics is at the state of the art of costoptimal planning. These heuristics are inherently limited, nonetheless, because the size of the abstract space must be bounded by some, even if a very large, constant. Targeting this shortcoming, we introduce t ..."
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Cited by 17 (8 self)
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Statespace search with explicit abstraction heuristics is at the state of the art of costoptimal planning. These heuristics are inherently limited, nonetheless, because the size of the abstract space must be bounded by some, even if a very large, constant. Targeting this shortcoming, we introduce the notion of (additive) implicit abstractions, in which the planning task is abstracted by instances of tractable fragments of optimal planning. We then introduce a concrete setting of this framework, called forkdecomposition, that is based on two novel fragments of tractable costoptimal planning. The induced admissible heuristics are then studied formally and empirically. This study testifies for the accuracy of the fork decomposition heuristics, yet our empirical evaluation also stresses the tradeoff between their accuracy and the runtime complexity of computing them. Indeed, some of the power of the explicit abstraction heuristics comes from precomputing the heuristic function offline and then determining h(s) for each evaluated state s by a very fast lookup in a “database. ” By contrast, while forkdecomposition heuristics can be calculated in polynomial time, computing them is far from being fast. To address this problem, we show that the timepernode complexity bottleneck of the forkdecomposition heuristics can be successfully overcome. We demonstrate that an equivalent of the explicit abstraction notion of a “database ” exists for the forkdecomposition abstractions as well, despite their exponentialsize abstract spaces. We then verify empirically that heuristic search with the “databased ” forkdecomposition heuristics favorably competes with the state of the art of costoptimal planning. 1.
Simultaneously searching with multiple settings: An alternative to parameter tuning for suboptimal singleagent search algorithms
 In Proc. ICAPS
, 2010
"... 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 singleagent search, probleminstancespecific parameter set ..."
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Cited by 16 (3 self)
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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 singleagent search, probleminstancespecific 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 slidingtile puzzle domain, and increases the number of problems solved when used in an automated planning system. 1.
Incremental Lower Bounds for Additive Cost Planning Problems
"... 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 theP m ⋆ construction. Becau ..."
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Cited by 15 (7 self)
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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 theP m ⋆ 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.
SemiRelaxed Plan Heuristics
"... Heuristics based on the delete relaxation are at the forefront of modern domainindependent planning techniques. Here we introduce a principled and flexible technique for augmenting deleterelaxed tasks with a limited amount of delete information, by introducing special fluents that explicitly repre ..."
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Cited by 13 (4 self)
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Heuristics based on the delete relaxation are at the forefront of modern domainindependent planning techniques. Here we introduce a principled and flexible technique for augmenting deleterelaxed tasks with a limited amount of delete information, by introducing special fluents that explicitly represent conjunctions of fluents in the original planning task. Differently from previous work in this direction, conditional effects are used to limit the growth of the task to be linear, rather than exponential, in the number of conjunctions that are introduced, making its use for obtaining heuristic functions feasible. We discuss how to obtain an informative set of conjunctions to be represented explicitly, and analyze and extend existing methods for relaxed planning in the presence of conditional effects. The resulting heuristics are empirically evaluated, and shown to be sometimes much more informative than standard deleterelaxation heuristics.