Modeling Organic Chemistry and Planning Organic Synthesis

Masoumi, Arman; Antoniazzi, Megan; Soutchanski, Mikhail

doi:10.29007/493z

Cited by 8 publications

(12 citation statements)

References 25 publications

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“…Briefly, this benchmark consists of problems relating to organic synthesis in chemistry and was originally developed by Heifets and Jurisica [22,21]. The benchmark set was then amended by our colleagues, namely Arman Masoumi, Megan Antoniazzi, and Mikhail Soutchanski [45,44] to be used in domain-independent planning. Whereby, we have corrected the previous encoding (summer 2016) by consulting with professors in chemistry at Ryerson University, Dr. Anne Johnson, Dr. Russell Viirre, Dr. Sharonna Greenberg, and Dr. Andrew McWilliams.…”

Section: Contribution To State-of-the-art Planningmentioning

confidence: 99%

“…With regards to organic synthesis, we maintain an identical representation of molecules as in [44], and differ in how the chemical reactions are encoded. Atoms are expressed in SC as objects using situation-independent predicates based on the atom chemical name in Mendeleev's periodic table.…”

Section: Representation Of the Chemistry Domain In Situation Calculusmentioning

confidence: 99%

“…For our purposes, chemical reactions are expressed in SC similarly to the representation as stated in [44]. This macro approach expresses each chemical reaction as an action term in SC.…”

Section: Representation Of the Chemistry Domain In Situation Calculusmentioning

confidence: 99%

“…Although we follow the same formalism as the work produced in [44], our encoding differs in the representation and identification of the preconditions for actions. In [44], the PA for each action is encoded as a long conjunction of literals. In turn, the instantiation of literals is solved as a constraint satisfaction problem (CSP).…”

Section: Representation Of the Chemistry Domain In Situation Calculusmentioning

confidence: 99%

“…The complexities of solving CSPs are well known and have been a difficult research issue for some time [69]. Specifically for the organic synthesis domain, in the previous implementation [44], the task of computing instantiations that satisfy the conjunction of literals is solved through exhaustive search and backtracking. Ultimately, this limitation creates a major bottleneck for solving planning problems in the organic synthesis domain.…”

Section: Representation Of the Chemistry Domain In Situation Calculusmentioning

confidence: 99%

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Efficient Lifted Planning with Regression-Based Heuristics

Qovaizi¹

2021

Preprint

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Modern state-of-the-art planners operate by generating a grounded transition system prior to performing search for a solution to a given planning task. Some tasks involve a significant number of objects or entail managing predicates and action schemas with a significant number of arguments. Hence, this instantiation procedure can exhaust all available memory and therefore prevent a planner from performing search to find a solution. This thesis explores this limitation by presenting a benchmark set of problems based on Organic Chemistry Synthesis that was submitted to the latest International Planning Competition (IPC-2018). This benchmark was constructed to gauge the performance of the competing planners given that instantiation is an issue. Furthermore, a novel algorithm, the Regression-Based Heuristic Planner (RBHP), is developed with the aim of averting this issue. RBHP was inspired by the retro-synthetic approach commonly used to solve organic synthesis problems efficiently. RBHP solves planning tasks by applying domain independent heuristics, computed by regression, and performing best-first search. In contrast to most modern planners, RBHP computes heuristics backwards by applying the goal-directed regression operator. However, the best-first search proceeds forward similar to other planners. The proposed planner is evaluated on a set of planning tasks included in previous International Planning Competitions (IPC) against a subset of the top scoring state-of-the-art planners submitted to the IPC-2018.

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Section: Contribution To State-of-the-art Planningmentioning

confidence: 99%

Section: Representation Of the Chemistry Domain In Situation Calculusmentioning

confidence: 99%

“…For our purposes, chemical reactions are expressed in SC similarly to the representation as stated in [44]. This macro approach expresses each chemical reaction as an action term in SC.…”

Section: Representation Of the Chemistry Domain In Situation Calculusmentioning

confidence: 99%

Section: Representation Of the Chemistry Domain In Situation Calculusmentioning

confidence: 99%

Section: Representation Of the Chemistry Domain In Situation Calculusmentioning

confidence: 99%

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Efficient Lifted Planning with Regression-Based Heuristics

Qovaizi¹

2021

Preprint

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Plotting: a case study in lifted planning with constraints

Espasa,

Miguel,

Nightingale

et al. 2024

Constraints

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We study a planning problem based on Plotting, a tile-matching puzzle video game published by Taito in 1989. The objective of this turn-based game is to remove a target number of coloured blocks from a grid by sequentially shooting blocks into the same grid. Plotting features complex transitions after every shot: various blocks are affected directly, while others can be indirectly affected by gravity. We consider modelling and solving Plotting from two perspectives. The puzzle is naturally cast as an AI Planning problem and we first discuss modelling the problem using the Planning Domain Definition Language (PDDL). We find that a model in which planning actions correspond to player actions is inefficient with a grounding-based state-of-the-art planner. However, with a more fine-grained action model, where each change of a block is a planning action, solving performance is dramatically improved. We also describe two lifted constraint models, able to capture the inherent complexities of Plotting and enabling the application of efficient solving approaches from SAT and CP. Our empirical results with these models demonstrates that they can compete with, and often exceed, the performance of the dedicated planning solvers, suggesting that the richer languages available to constraint modelling can be of benefit when considering planning problems with complex changes of state. CP and SAT solvers solved almost all of the largest and most challenging instances within 1 hour, whereas the best planning approach solved approximately 30%. Finally, the flexibility provided by the constraint models allows us to easily curate interesting levels for human players.

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Classical Planning as QBF without Grounding

Shaik

Pol

2022

ICAPS

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Most classical planners use grounding as a preprocessing step, essentially reducing planning to propositional logic. However, grounding involves instantiating all rules and actions with concrete object combinations, and results in large encodings for SAT/QBF-based planners. This severe cost in memory becomes a main bottleneck when actions have many parameters, such as in the Organic Synthesis problems from the IPC 2018 competition. We provide a compact QBF encoding that is logarithmic in the number of objects and avoids grounding completely by using universal quantification for object combinations. We show that we can solve some of the Organic Synthesis problems, which could not be handled before by any SAT/QBF based planners due to grounding.

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Modeling Organic Chemistry and Planning Organic Synthesis

Cited by 8 publications

References 25 publications

Efficient Lifted Planning with Regression-Based Heuristics

Efficient Lifted Planning with Regression-Based Heuristics

Plotting: a case study in lifted planning with constraints

Classical Planning as QBF without Grounding

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