Numeric Planning via Abstraction and Policy Guided Search

Illanes, León; McIlraith, Sheila A.

doi:10.24963/ijcai.2017/606

Cited by 11 publications

(13 citation statements)

References 19 publications

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“…One such example is a line of work concerned with the synthesis of finite-state controllers for conformant and generalized planning (Bonet and Geffner 2015;Bonet et al 2017), where the unfairness is handled by enforcing relevant trajectory constraints. A different example is the use of abstract policies to guide search in domains with numeric state variables (Illanes and McIlraith 2017), where the issues with unfairness are dealt by falling back to traditional search methods whenever the guidance is ineffective. In our work, we directly exploit the fact that we do have a model for how the unfair nondeterminism resolves and implement a termination verification process into the search for a solution.…”

Section: Related Workmentioning

confidence: 99%

“…Our work realizes this vision by leveraging ideas from a number of different areas of research including generalized planning (e.g., (Levesque 2005;Hu and Levesque 2009;Srivastava, Immerman, and Zilberstein 2008;Hu and Levesque 2010; Hu and De Giacomo 2011)), numeric planning (e.g., (Srivastava et al 2011;Illanes and McIlraith 2017)), and techniques for recognizing indistinguishable objects in planning domains (Riddle et al 2015;2016; Fuentetaja and de la Rosa 2016).…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Generalized Planning via Abstraction: Arbitrary Numbers of Objects

Illanes

McIlraith

2019

AAAI

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We consider a class of generalized planning problems based on the idea of quantifying over sets of similar objects. We show how we can adapt fully observable nondeterministic planning techniques to produce generalized solutions that are easy to instantiate over particular problem instances. We also describe how we can reformulate a classical planning problem into a quantified one. The reformulation allows us to solve the original planning task without grounding every action with respect to all objects in the problem, and a single solution can be applied to a possibly infinite set of related classical planning tasks. We report experimental results that show our approach is a practical and promising technique for solving an interesting class of problems.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Generalized Planning via Abstraction: Arbitrary Numbers of Objects

Illanes

McIlraith

2019

AAAI

Self Cite

View full text Add to dashboard Cite

show abstract

“…Temporal fast downward, which is the heuristic search planner employing this heuristic, was not able to solve any of the instances of our Sailing problem. Illanes and McIlraith (2017) also use a qualitative abstraction of the numeric aspects of the problem. They construct a classical planning problem by replacing numeric conditions with propositions and use a classical planner to devise a partial policy, which is used to guide the search for a plan in the numeric planning problem.…”

Section: Numeric Planning Via Heuristic Searchmentioning

confidence: 99%

Subgoaling Techniques for Satisficing and Optimal Numeric Planning

Scala¹,

Haslum²,

Thiébaux³

et al. 2020

jair

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This paper studies novel subgoaling relaxations for automated planning with propositional and numeric state variables. Subgoaling relaxations address one source of complexity of the planning problem: the requirement to satisfy conditions simultaneously. The core idea is to relax this requirement by recursively decomposing conditions into atomic subgoals that are considered in isolation. Such relaxations are typically used for pruning, or as the basis for computing admissible or inadmissible heuristic estimates to guide optimal or satis_cing heuristic search planners. In the last decade or so, the subgoaling principle has underpinned the design of an abundance of relaxation-based heuristics whose formulations have greatly extended the reach of classical planning. This paper extends subgoaling relaxations to support numeric state variables and numeric conditions. We provide both theoretical and practical results, with the aim of reaching a good trade-o_ between accuracy and computation costs within a heuristic state-space search planner. Our experimental results validate the theoretical assumptions, and indicate that subgoaling substantially improves on the state of the art in optimal and satisficing numeric planning via forward state-space search.

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“…Later, a random-walk approach was proposed for consumer-only numeric planning problems (Nakhost, Hoffmann, and Müller 2012). A recent work transforms a numeric task into a classical one via abstractions and a plan in the abstract space is used to guide the search in the numeric state space (Illanes and McIlraith 2017). Most of these works are valid for problems beyond simple numeric conditions, as they consider linear or polynomial effects of actions.…”

Section: Related Workmentioning

confidence: 99%

Linear and Integer Programming-Based Heuristics for Cost-Optimal Numeric Planning

Piacentini

Castro

Ciré

et al. 2018

AAAI

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Linear programming has been successfully used to compute admissible heuristics for cost-optimal classical planning. Although one of the strengths of linear programming is the ability to express and reason about numeric variables and constraints, their use in numeric planning is limited. In this work, we extend linear programming-based heuristics for classical planning to support numeric state variables. In particular, we propose a model for the interval relaxation, coupled with landmarks and state equation constraints. We consider both linear programming models and their harder-to-solve, yet more informative, integer programming versions. Our experimental analysis shows that considering an NP-Hard heuristic often pays off and that A* search using our integer programming heuristics establishes a new state of the art in cost-optimal numeric planning.

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Numeric Planning via Abstraction and Policy Guided Search

Cited by 11 publications

References 19 publications

Generalized Planning via Abstraction: Arbitrary Numbers of Objects

Generalized Planning via Abstraction: Arbitrary Numbers of Objects

Subgoaling Techniques for Satisficing and Optimal Numeric Planning

Linear and Integer Programming-Based Heuristics for Cost-Optimal Numeric Planning

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