Bounding Quality in Diverse Planning

Katz, Michael B.; Sohrabi, Shirin; Udrea, Octavian

doi:10.1609/aaai.v36i9.21216

Cited by 5 publications

(10 citation statements)

References 9 publications

(24 reference statements)

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“…The second stage of the two-stage approach, which we call the diverse plan selection problem, involves selecting a subset of plans subject to a cardinality constraint k that maximizes diversity (Vadlamudi and Kambhampati 2016). Katz and Sohrabi (2020) tested and later proposed using mixed-integer programming (MIP) to obtain the set with optimal minimum pairwise diversity (Katz, Sohrabi, and Udrea 2022). However, solving the MIPs can be quite expensive as their MIP formulation features pairwise linear constraints which grow quadratically with the number of plans n in the candidate set.…”

Section: Diverse Planningmentioning

confidence: 99%

“…Greedy search has shown surprisingly strong performance in terms of solution quality. In particular, experiments show that greedy search is able to achieve optimality around 60% of the time (Katz, Sohrabi, and Udrea 2022). The greedy search for diverse planning introduced by Katz and Sohrabi (2020) G ← G ∪ π 6: end for 7: return G To evaluate the diversity of a set of plans, we first need a measure of similarity (or dissimilarity) between plans.…”

Section: Diverse Planningmentioning

confidence: 99%

“…is the similarity between two states. We note that the original formulation for the stability similarity has been under an action set (Fox et al 2006;Coman and Munoz-Avila 2011;Katz and Sohrabi 2020), but Katz, Sohrabi, and Udrea (2022) proposed it should be defined for action multi-sets. It follows the same definition except that A(π) now denotes the multi-set of actions, allowing for repetitions.…”

Section: Diverse Planningmentioning

confidence: 99%

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Bi-Criteria Diverse Plan Selection via Beam Search Approximation

Zhong,

Shati,

Cohen

2024

SOCS

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Recent work on diverse planning has focused on a two-step setting where the first step consists of generating a large number of plans, and the second step consists of selecting a subset of plans that maximizes diversity. For the second step, previous work has focused on solving a combinatorial optimization problem for diverse subset selection that can be approximated using greedy search. In this work, we propose a flexible, bi-criteria framework for diverse plan selection. Our framework consists of optimizing both quality and diversity, generalizing previous work and providing flexibility to prioritize one objective over the other. We consider two quality and two diversity measures and show that greedy search guarantees an approximation with a constant ratio for certain configurations based on established results in the literature. To allow users to trade off additional computation for better solutions, we introduce a beam search approximation that generalizes the greedy search, and we provide approximation guarantees on the obtained solutions. Finally, we conduct extensive experiments that show that: (1) our flexible bi-criteria framework allows us to obtain solutions of better quality while still maintaining a high degree of diversity; (2) our beam search approximation obtains significant improvement in performance over greedy search and, for a large number of instances, is able to generate solutions that are equal to or better than those obtained by an exact MIP solver with a significantly higher runtime limit.

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Section: Diverse Planningmentioning

confidence: 99%

Section: Diverse Planningmentioning

confidence: 99%

Section: Diverse Planningmentioning

confidence: 99%

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Bi-Criteria Diverse Plan Selection via Beam Search Approximation

Zhong,

Shati,

Cohen

2024

SOCS

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“…To complete the flow composition process, we apply AI planning techniques to generate one or more flows depending on the candidates provided by the IKG in the previous step. The AI Planner that we use -Masterplan -is based off of prior work in the automated planning community (Katz 2018;Katz et al 2018a,b;Katz, Sohrabi, and Udrea 2020;. The planner receives the candidates identified via the AMR and IKG steps; additionally, it also receives the action schemas for each of the idenfitied candidates.…”

Section: Ai Plannermentioning

confidence: 99%

A Goal-Driven Natural Language Interface for Creating Application Integration Workflows

Brachman¹,

Bygrave²,

Chakraborti³

et al. 2022

AAAI

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Web applications and services are increasingly important in a distributed internet filled with diverse cloud services and applications, each of which enable the completion of narrowly defined tasks. Given the explosion in the scale and diversity of such services, their composition and integration for achieving complex user goals remains a challenging task for end-users and requires a lot of development effort when specified by hand. We present a demonstration of the Goal Oriented Flow Assistant (GOFA) system, which provides a natural language solution to generate workflows for application integration. Our tool is built on a three-step pipeline: it first uses Abstract Meaning Representation (AMR) to parse utterances; it then uses a knowledge graph to validate candidates; and finally uses an AI planner to compose the candidate flow. We provide a video demonstration of the deployed system as part of our submission.

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“…The need for producing such a collection of plans is well established for many applications, including plan recognition (Sohrabi, Riabov, and Udrea 2016), malware detection (Boddy et al 2005), business process automation (Chakraborti et al 2020), and automated machine learning . Top-quality planning serves as a basis for solving other computational problems, such as qualityaware diverse planning (Nguyen et al 2012;Vadlamudi and Kambhampati 2016;Katz, Sohrabi, and Udrea 2022). In these and other applications, the choices of planning models, whether avoidable or not, may have unintended effects on plans.…”

Section: Introductionmentioning

confidence: 99%

Who Needs These Operators Anyway: Top Quality Planning with Operator Subset Criteria

Katz¹,

Sohrabi²

2022

ICAPS

Self Cite

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Top-quality planning in general and quotient top-quality planning in particular deal with producing multiple high-quality plans while allowing for their efficient generation, skipping equivalent ones. Prior work has explored one equivalence relation, considering two plans to be equivalent if their operator multi-sets are equal. This allowed omitting plans that are reorderings of previously found ones. However, the resulting sets of plans were still large, in some domains even infinite. In this paper, we consider a different relation: two plans are related if one's operator multiset is a subset of the other's. We propose novel reformulations that forbid plans that are related to the given ones. While the new relation is not transitive and thus not an equivalence relation, we can define a new subset top-quality planning problem, with finite size solution sets. We formally prove that these solutions can be obtained by exploiting the proposed reformulations. Our empirical evaluation shows that solutions to the new problem can be found for more tasks than unordered top-quality planning solutions. Further, the results shows that the solution sizes significantly decrease, making the new approach more practical, particularly in domains with redundant operators.

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Bounding Quality in Diverse Planning

Cited by 5 publications

References 9 publications

Bi-Criteria Diverse Plan Selection via Beam Search Approximation

Bi-Criteria Diverse Plan Selection via Beam Search Approximation

A Goal-Driven Natural Language Interface for Creating Application Integration Workflows

Who Needs These Operators Anyway: Top Quality Planning with Operator Subset Criteria

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