The deterministic part of the seventh International Planning Competition

López, Carlos Linares; Celorrio, Sergio Jiménez; Olaya, Angel García

doi:10.1016/j.artint.2015.01.004

Cited by 24 publications

(14 citation statements)

References 34 publications

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“…With this result in mind, one may speculate why planners apparently are better at analysing solvable instances than unsolvable instances in empirical evaluations. One possible explanation is that the development of planners has to a large extent been spurred by the international planning competitions [LCO15]. However, the backside of this development is that these competitions, and thus also most planners, have been heavily focused on instances that are guaranteed to be solvable, that is, the planners and methods used are getting increasingly faster at finding solutions but not on verifying that no solutions exist.…”

Section: Solvable Vs Unsolvable Instancesmentioning

confidence: 99%

Methods for Detecting Unsolvable Planning Instances using Variable Projection

Ståhlberg¹

2017

Linköping Studies in Science and Technology. Dissertations

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In this thesis we study automated planning, a branch of artificial intelligence, which deals with the construction of plans. A plan is typically an action sequence that achieves some specific goal. In particular, we study unsolvable planning instances, i.e. when there is no plan. Historically, this topic has been neglected by the planning community, and up to recently the International Planning Competition only evaluated planners on solvable planning instances. For many applications we can know, for instance by design, that there is a solution, but this cannot be a general assumption. One example is penetration testing in computer security, where a system is considered safe if there is no plan for intrusion. Other examples are resource bounded planning instances that have insufficient resources to achieve the goal.The main theme of this thesis is to use variable projection to prove unsolvability of planning instances. We implement and evaluate two planners: the first checks variable projections with the goal of finding an unsolvable projection, and the second builds a pattern collection to provide dead-end detection. In addition to comparing these planners to existing planners, we also utilise a large computer cluster to statistically assess whether they can be optimised further. On the benchmarks of planning instances that we used, it turns out that further improvement is likely to come from supplementary techniques rather than further optimisation. We pursue this and enhanced variable projections with mutexes, which yields a very competitive planner. We also inspect whether unsolvable variable projections tend to be composed of variables that play different roles, i.e. they are not 'similar'. We devise a variable similarity measure to rate how similar two variables are, and statistically analyse this. The measure can differentiate between unsolvable and solvable planning instances quite well, and is integrated into our planners. We also define a binary version of the measure, namely, that two variables are isomorphic if they behave in exactly the same way in some optimal solution (extremely similar). With the help of isomorphic variables we identify a computationally tractable class of planning instances that meet certain restrictions. There are several special cases of this class that are of practical interest, and this result encompass them. This work has been supported by the Theoretical Computer Science Laboratory, Department of Computer and Information Science, Linköping University, and in part by CUGS (the National Graduate School in Computer Science, Sweden).i Populärvetenskaplig sammanfattningI den här avhandlingen studerar vi automatisk planering, vilket är ett nyckelområde inom artificiell intelligens. Planering är att tänka innan man handlar för att uppnå ett specificerat mål. Motsatsen till detta är att enbart handla reaktivt på vad som sker ögonblickligen. Planering använder sig av kunskap om världen för att konstruera en plan för vad som behöver göras, och i vilken ordning. Normalt tar vi...

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Section: Solvable Vs Unsolvable Instancesmentioning

confidence: 99%

Methods for Detecting Unsolvable Planning Instances using Variable Projection

Ståhlberg¹

2017

Linköping Studies in Science and Technology. Dissertations

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“…This competition provides an empirical environment for assessing planning systems under the same conditions. The IPC 2011 [25] was composed of three parts:…”

Section: International Planning Competition 2011mentioning

confidence: 99%

Automatic construction of optimal static sequential portfolios for AI planning and beyond

Núñez

Borrajo

López

2015

Artificial Intelligence

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In recent years the notion of portfolio has been revived with the aim of improving the performance of modern solvers. For example, Fast Downward Stone Soup and SATzilla have shown an excellent performance at the International Planning and SAT Competitions respectively. However, a deeper understanding of the limits and possibilities of portfolios is still missing. Most approaches to the study of portfolios are purely empirical. Thus, we propose a theoretically-grounded method based on Mixed-Integer Programming named gop. It addresses, among others, three main issues: how to derive an upper bound on the solvers performance for a given set of problem solving tasks; how to analyze the utility of training instances when designing portfolios; and how to configure a high performance portfolio for a given training set which generalizes very well on unseen instances. Experimental results both with data from the International Planning Competitions 2008 and 2011 and the SAT Competition 2013 show that this approach significantly outperforms others under the same conditions. Indeed, MIPSat, the sequential SAT portfolio automatically configured with gop, won the silver medal in the Open track of the SAT Competition 2013. In addition, MIPlan, the planning system which is able to automatically generate a portfolio configuration for a specific planning domain using gop, won the learning track of the International Planning Competition 2014.

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“…With this result in mind, one may speculate why planners apparently are better at analysing solvable instances than unsolvable instances in empirical evaluations. One possible explanation is that the development of planners has to a large extent been spurred by the international planning competitions [21]. However, the backside of this development is that these competitions, and thus also most planners, has been heavily focused on instances that are guaranteed to be solvable, that is, the planners and methods used are getting increasingly faster at finding solutions but not on verifying that no solutions exist.…”

Section: Corollary 16 Let a Be A Planning Algorithm And Let C Be A Clmentioning

confidence: 99%

Refining complexity analyses in planning by exploiting the exponential time hypothesis

Aghighi

Bäckström

Jönsson

et al. 2016

Ann Math Artif Intell

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The use of computational complexity in planning, and in AI in general, has always been a disputed topic. A major problem with ordinary worst-case analyses is that they do not provide any quantitative information: they do not tell us much about the running time of concrete algorithms, nor do they tell us much about the running time of optimal algorithms. We address problems like this by presenting results based on the exponential time hypothesis (ETH), which is a widely accepted hypothesis concerning the time complexity of 3-SAT. By using this approach, we provide, for instance, almost matching upper and lower bounds onthe time complexity of propositional planning.

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The deterministic part of the seventh International Planning Competition

Cited by 24 publications

References 34 publications

Methods for Detecting Unsolvable Planning Instances using Variable Projection

Methods for Detecting Unsolvable Planning Instances using Variable Projection

Automatic construction of optimal static sequential portfolios for AI planning and beyond

Refining complexity analyses in planning by exploiting the exponential time hypothesis

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